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Phase I
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2INNOVATE LLC
STTR Phase I: A novel wall-mounted gait assist system to reduce the risk of injuries on stairs and level surfaces
Contact
3622 WOODLAND DR
Metamora, MI 48455--9626
NSF Award
2304063 – STTR Phase I
Award amount to date
$275,000
Start / end date
09/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is a novel mechanical system which improves the safety of the elderly and disabled while walking on level surfaces and stairs. The novel, wall-mounted system will reduce the risks of injury by mechanically supporting individuals during ambulation, rehabilitation, and eventual in-home gait assistance. The system aims to reduce one of the top reasons for emergency room (ER) visits (nearly 3,000 ER visits in the United States each year) by preventing falls in the home. The solution may also be used in medical facilities to safely ambulate convalescent or acute care patients, especially on stairs. In addition to reducing direct patient injury risks, the technology improves economic and productivity measures by reducing the number of therapists/nurses, relatives, and support workers caring for people with disabilities or those at risk of falls.
This STTR Phase I project will demonstrate a novel, mechanical, wall-mounted gait assist that can safely reduce injuries and the risks of falling while walking across flat surfaces and stairs. A harness with an elastic-like tether, mobile trolley, and mechanical braking mechanism for maintaining patient safety will be completed. The individual is connected to a trolley travelling along a wall-side rail providing the variable forces needed to support a patient’s weight in order to minimize risks of injury. In the event of a fall, the trolley’s brake automatically activates, absorbing the shock with a vest/tether to prevent the user from falling to the floor. Following design engineering and prototype completion, a pilot proof of concept study will be conducted during Phase I to demonstrate the potential for patient use in safely walking without falling and causing injury.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
2PI INC.
SBIR Phase I: High-performance, ultra-compact 3D sensor enabled by metasurface flat optics
Contact
292 NEVADA ST
Newton, MA 02460-
NSF Award
2204825 – SBIR Phase I
Award amount to date
$256,000
Start / end date
12/01/2022 – 11/30/2023 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project lies in the development of a lightweight, ultra-compact 3D sensor offering enhanced performance compared to the current state-of-the-art. Existing sensors on consumer electronic devices, such as those on augmented/virtual reality headsets, are limited in their perception accuracy and detection range resulting in a poor user experience, which has limited their implementation. The technology developed in this project seeks to address these issues to create a truly seamless and immersive interaction experience for users. The technology may also improve the adoption of augmented/virtual reality technologies in fields such as education, telecommuting, healthcare, industrial design, virtual meetings, entertainment, and many others. The technology has the potential to create new jobs in these fields, enhance human-to-machine interactions, and improve connections within and between communities. This effort also supports domestic photonics manufacturing and assembly industries.
This SBIR Phase I project will seeks to develop a novel 3D sensor design that promises performance enhancement over state-of-the-art devices. Building on a proprietary flat optics technology, the project may lead to a 3D sensor module featuring panoramic vision, significant spatial resolution improvements, enhanced signal-to-noise ratio, and a compact, lightweight architecture amenable to low-cost manufacturing and assembly. The project has two main goals: 1) demonstration of a 3D sensor prototype and experimental validation of its performance and 2) development of scalable manufacturing routes for fabrication of flat optics components leveraging standard microfabrication technologies.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
3FATES-XRAY, INC.
SBIR Phase I: Single-shot X-ray Phase-contrast Imaging Using Deep Learning Approaches
Contact
616 BRISTOL TER
Sunnyvale, CA 94087--1488
NSF Award
2321552 – SBIR Phase I
Award amount to date
$274,996
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project relates to the benefits of next-generation X-ray imaging systems. The proposed single-shot platform overcomes the current barriers to widespread commercialization of Differential Phase Contrast (DPC) X-ray imaging. If successful, the single-shot technology will enable the development and application of next generation X-ray imagers for detecting liquids, small explosives, and other security threats for aviation and business applications. Reducing false alarm rates at airports will increase customer satisfaction, improve security, and reduce cost. DPC imaging could also substantially increase the detection of food pests, thereby reducing food waste and saving billions of dollars. In another market, non-destructive testing could significantly improve the inspection of additive manufacturing products, reducing manufacturing time through fewer iterations and creating high-quality products. Medical DPC imagers would provide MRI (Magnetic Resonance Imaging)-like resolution and diagnostics at an order of magnitude lower cost than current MRI.
This Small Business Innovation Research Phase I project aims to develop a deep-learning approach to realize “single-shot” X-ray phase-contrast imaging. To commercialize the technology, the deep-learning algorithm needs to identify more complicated real-world objects effectively and accurately. Deep-learning methods require thousands to millions of training samples to make a reliable model. However, no imaging library for this unique technology currently exists. The research and development plan initially incorporates the standard slow scanning method of X-ray phase-contrast imaging to obtain DPC tri-signature computed tomography (CT) images. The tri-signature CT images provide the basis for precise material characterization (e.g., absorption coefficients, indices of refraction, and scatter characteristics). Once the materials have been characterized, they form the basis for creating millions of numerical representations of real-world objects. These objects subsequently form the core for effectively and efficiently training deep-learning models without further experimentation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
4thought Technologies, Inc.
SBIR Phase I: Super High Performance No-Code Platform
Contact
630 5TH AVE FL 20
New York, NY 10111--3193
NSF Award
2212675 – SBIR Phase I
Award amount to date
$256,000
Start / end date
04/15/2023 – 09/30/2023
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to democratize participation in the expansive growth of blockchain-enabled financial markets that increasingly require the ability to read, analyze, or write smart contracts at scale. Enterprises that lack the programming capabilities to perform these tasks well will become increasingly disadvantaged as smart contracts continue to proliferate. Ranging from simple Non-Fungible Tokens (NFTs) that convey ownership to complex, self-executing agreements that automate future payments, smart contracts are at the heart of the commercial utility and perceived value of a blockchain. Growing numbers of financial institutions are seeking to advance their Web3 capabilities, even at premium prices. As algorithmic trading has demonstrated, automating analysis and execution can provide enormous commercial upside, yet it also can result in large losses if designed improperly or left unchecked. Pioneering a solution that enables enterprises without advanced programmers to reap the benefits of analyzing and writing smart contracts while managing the associated risks requires deep technology— specifically a novel visual programming language designed to manage big-data sets involving millions of smart contracts on public blockchains using simple no-code commands.
This SBIR Phase 1 project proposes to build a unique blockchain application interface that will enable users to scan and read smart contracts and immediately leverage the extracted data to design, build, and test trading algorithms that incorporate digital assets. This interface will ultimately enable users to analyze and write smart contracts at scale without the need to code. The project will set the foundation for the inexpensive development of Web3 capabilities by thousands of enterprises that do not have access to advanced programmers. The project will also produce a computerized tool that does the following two key tasks: 1. continuously scan publicly-accessible smart contracts in real time, and 2. derive indicators from these scanned contracts that likely are meaningful, initially for the purposes of analyzing and trading financial assets. The blockchain application interface to be created will enable users without programming capabilities to automate trading using live smart contract data for the first time.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
AALMV INC.
SBIR Phase I: Mission Planning Methods and Simulated Crisis Management Framework to teach STEM to Underserved Youth
Contact
925 BRIGHTWATERS BLVD NE
Saint Petersburg, FL 33704--3721
NSF Award
2302195 – SBIR Phase I
Award amount to date
$270,755
Start / end date
08/01/2023 – 01/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/ commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in addressing the unemployment of youth, primarily in underserved communities, across the United States by providing an online, collaborative gaming environment to educate, train, and provide Science, Technology, Engineering and Mathematics (STEM)-based career opportunities. It is estimated that more than 5.5 million youth in the U.S. ages 16 to 24 years, are out-of-school and out-of- work. The unemployment rate in this age group is close to 20 percent. This project aims to improve young people’s skills and overall knowledge, and help them secure certifications in drone design, function, and operations related to industry and first responder communities. Implicit to the core learning are knowledge in areas such as critical thinking and planning, rapid prototyping, mission planning, data post-processing and analytics, and co-authoring of effective robotics designs to assist in industry and first responder areas of operation. The project empowers students to develop strong STEM skills that target career opportunities in advanced manufacturing, inspire budding entrepreneurs, and directly benefit industry and first responder communities with a relevantly trained workforce.
This Small Business Innovation Research Phase I project will develop a scalable template of a realistic environment for a multi-player game for education and training of youth, integrating autonomous robots using scenarios driven by industry and first responders. This technology will be accomplished through a collaborative mission readiness workflow application with a multi-player gaming engine, Computer Aided Design (CAD)-generated prototype drones, and geo-accurate areas of terrain for realistic and relevant environments. Intelligent improvements of the system will be accelerated as Machine Learning (ML) and Artificial Intelligence (AI) algorithms are added to the workflow application and gaming environment from data that is collected and analyzed from training, exercise, and lessons learned from live response events. The greatest technical obstacle is modeling the data in a way that preserves integrity and that can be adapted and “trained” for machine learning. Teams of youth, first responders, and engineers in this e-sports gaming league will compete for the best time, procedures, and systems to win each mission. Drone design, physics reality, and machine learning algorithms for each vehicle will be outputs for the drone blueprints that will be actualized via 3D printing.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ABBERIT LLC
SBIR Phase I: Harvesting strawberry using delta robots
Contact
8201 164TH AVE NE
Redmond, WA 98052--7615
NSF Award
2207897 – SBIR Phase I
Award amount to date
$256,000
Start / end date
10/01/2022 – 09/30/2023
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Phase I project is to enhance scientific and technological understanding in selective harvesting of high-value crops such as strawberry. The innovation proposed here will be a key component in creating a compact economically viable autonomous strawberry harvesting robot. The technology developed in this project will help address the farming labor shortage which is experienced by farmers more and more each year. Agricultural robotics market is estimated to be $11.9 billion by 2026. Providing autonomous harvesting robots will enable farmers to grow more crops with less business risks related to manual labor shortages and will ensure that US farmers remain competitive, while creating more skilled, highly paid jobs.
This Small Business Innovation Research (SBIR) Phase I project will develop critical technologies for delicate selective harvesting of fragile crops such as strawberries at a competitive pace. Recent advancements in computer vision object detection and three-dimensional scene reconstruction will be used to create near real-time operational scenes. Those scenes will be used to detect ripe berries, create a movement path and navigate the fast and precise robot arm for harvesting the berries without damaging fruits while avoiding obstacles.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ABSTRACTIVE HEALTH, INC.
SBIR Phase I: A tool to automate a narrative patient summary of the medical chart for outpatient physicians
Contact
333 E 56 ST
New York, NY 10022--3760
NSF Award
2324507 – SBIR Phase I
Award amount to date
$274,991
Start / end date
08/15/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a machine learning-enabled medical record summarization tool designed to provide a narrative summary that can aid physicians in patient care. On average, physicians spend just 3 minutes reviewing a patient’s medical record, and during this time they must interpret unstructured Electronic Health Records (EHR) that can make it difficult for physicians to identify information essential to patient care and diagnosis. By targeting the rich clinical data embedded in unstructured clinical notes, the proposed tool could provide clinically relevant information and a contextual understanding of a patient’s medical history. If successful, the proposed solution will reduce the data burden placed on doctors, mitigate the risk of missing valuable information that could affect patient diagnosis or lead to costly medical errors, and maximize downstream effects on patient outcomes.
This Small Business Innovation Research (SBIR) Phase I project aims to leverage advances in natural language processing (NLP) to assist doctors by automating the process of electronic health record review. The underlying innovation is an extractive-abstractive pipeline that determines what content in the medical record is the most salient and should be summarized through a transformer (a machine learning model). This project aims to advance this summarization tool to more challenging use cases, primarily summarizing the outpatient record, a task made challenging by the large scope of the data, clinical redundancies, different data structures, and sources inherent to outpatient data, all of which need to be accounted for in model training and validation. Objectives include to 1) developing an outpatient summarization model and demonstrating the ability to produce summaries that semantically match reference text with a high level of fluency, 2) validating the utility of artificial intelligence (AI)-generated outpatient summaries to provide significant value to physicians, 3) evaluating the ability of AI-generated summaries that provide information relevant to future patient visit through ablation study, and 4) incorporating checks for bias in the existing model.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ACTIVECHARGE LLC
STTR Phase I: Enhancing wind-energy industry competitiveness using self-powered blade monitoring sensors
Contact
1450 S ROLLING RD
Halethorpe, MD 21227--3863
NSF Award
2131373 – STTR Phase I
Award amount to date
$256,000
Start / end date
10/01/2022 – 09/30/2023
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) project seeks to provide an integrated monitoring solution for wind turbine blades. Continuous and reliable monitoring within the blade has been a challenge, primarily due to the lack of reliable energy for the wireless sensor (e.g., batteries need to be replaced and can be expensive and logistically difficult to replace inside the blade and power lines inside the blade are hard to install and require frequent maintenance). The proposed solution seeks to overcome current technical challenges by providing a long-lasting, maintenance-free, self-powered, integrated solution for wind turbine blade monitoring and analytics. If successfully commercialized, the solution can be deployed for autonomous sensing and smart maintenance scheduling based on big data analysis. This project may contribute to significantly and permanently reducing existing blade monitoring costs, decreasing downtime for manual monitoring and battery changes and reducing catastrophic failures with better monitoring information. By reducing the operational costs, the solution may make large-scale wind energy more competitive, reducing the world’s dependence on environmentally-harmful sources of energy. In addition, the technology may reduce the risk of injury to humans as compared to current operational processes, making wind energy safer to operate.
This STTR Phase I project proposes to develop an integrated, self-powering sensor node for wind turbine blade monitoring by overcoming the following technical hurdles: lack of reliable energy for the sensor/transmitter system deep inside the blade, logistical challenges to replacing batteries inside the blade for a large number of sensors at different intervals, and difficulites with long wire-runs inside the blade as those are hard to install and require frequent maintenance. To handle these technical hurdles, this project aims to prototype an integrated, self-powered, wireless sensor node and perform field tests. This project plans to: (1) develop a mechanism for the harvester module that reliably produces electrical voltage and power regardless of the blade rotational speed, (2) develop a power management circuit with autonomous sleep/wakeup and without impedance tracking to increase charging efficiency, and (3) perform indoor and in-field test for verification of power harvesting and data transmission performance. This technolology seeks to address the fundamental weaknesses of vibration energy harvesters while integrating various components (e.g., energy harvester, sensor, transmitter, receiver, and analytics software) for an optimized solution.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ADVANCED GROWING RESOURCES INC.
SBIR Phase I: Novel handheld spectroscopy for the early detection of crop afflictions
Contact
447 VOSBURG RD.
Webster, NY 14580--1040
NSF Award
2213137 – SBIR Phase I
Award amount to date
$255,786
Start / end date
02/01/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
This Small Business Innovation Research (SBIR) Phase I project will focus on development of a handheld optical scanner that integrates precise imaging with optical spectroscopy. Although this technology has broad applications ranging from industrial quality control to education, the beachhead sector to be addressed is precision agriculture. To feed a growing population of 10 billion in 2050, agricultural production will need to increase by 70% over the next 30 years. Critical to this mission is the development of innovative tools and strategies for crop protection and health management to preserve the world’s food supply. The potential customers in this segment are crop growers, consultants, and field scouts aiming to act early while reducing the use of harmful and costly chemicals. The global crop monitoring market was estimated at $2 billion in 2019 and is anticipated to reach $6 billion by 2027, growing at 15.3% annually. The sensing and imaging segment accounted for 49.3% of the market in 2019. This technology has the potential to support the adoption of more sustainable agricultural practices as well as the economic viability of small- to medium-scale farm operations in the U.S. by providing an accessible and affordable tool for disease detection and crop health management.
The intellectual merit of this project is the commercial development of novel and affordable imaging spectroscopy technology in addition to application-specific analytics for the early detection of crop afflictions. Optical components of the scanning device that will be designed-for-manufacturing in this project bridge the current gap created by systems that trade the detection capabilities of spectral resolution for spatial resolution. The scanner will be deployed to collect data on grapes, a key, high-value crop that is susceptible to a number of afflictions that destroy yields before the human eye can detect them. Machine learning algorithms will be developed from this data to detect stressed and diseased states in the plant before they become apparent to visual inspection. These models will then be validated in the field with user feedback from horticultural experts and customers. Successful implementation of this technology will facilitate the detection of crop diseases before they spread. This detection capability can increase yields while reducing the use of costly and environmentally-harmful pesticides and fertilizers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
AGTEC INNOVATIONS INC
SBIR Phase I: Caged Urea as an Eco-friendly Nitrogen Fertilizer
Contact
1290 ALTAMEAD DR
Los Altos, CA 94024--5568
NSF Award
2233044 – SBIR Phase I
Award amount to date
$274,457
Start / end date
09/15/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is the reduction in greenhouse gas emissions and water contamination due to nitrogen fertilizers. The project will also increase agricultural productivity at lower nitrogen application rates using a novel concept of urea trapped in molecular cages. Nitrogen fertilizers can be serious environmental pollutants. Available alternatives are either too expensive for general agricultural use or cause unwanted residue buildup and are, therefore, minimally used. Pollution from nitrogen fertilizers remains a serious concern globally. This project's impact is directed at three targets: (a) Farmer income: the proposed nitrogen fertilizer may increase crop yields by 5-10%, improve farm profits, and improve the income of 10% of the US population; (b) US economy: the technology may boost the US economy by global export of this fertilizer; and (c) Environment and health: the solution may reduce greenhouse gas emissions and water pollution from nitrogen fertilizer.
The project is aimed at the development of a novel fertilizer compound, where urea is trapped within a biodegradable molecular cage. The molecular cage is designed to bind urea within its structure to reduce its solubility and reduce pollutant production. Nitrogen is released from this cage only when the cage is dissolved by root secretions (such as organic acids). This technology is an intelligent release mechanism where an insoluble nutrient is released only on demand by the plant. The cage itself is constructed of plant nutrients therefore, when the plant dissolves the cage, it not only gets its nitrogen from urea but also consumes the cage because the cage is also food to the plant. To have a commercially successful product, the team will optimize the performance of the caged urea to meet agronomic and environmental targets and farmer acceptability. These goals will be accomplished by modulating the cage-link bridges to improve the trapping of urea, reducing volatilization by controlling the microenvironment modifiers, controlling emissions during production, and improving the physical properties for ease-of-farmer use. This project will help to develop caged- urea into an environmentally impactful, agronomically beneficial, marketable, consumer friendly, and manufacturable commodity.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
AI ACUITY
SBIR Phase I: Improved image compression targeting machine learning based detection algorithms
Contact
90 HULME CT APT 107
Stanford, CA 94305--7432
NSF Award
2233091 – SBIR Phase I
Award amount to date
$274,623
Start / end date
08/15/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in reducing the sizes of earth observation images, thereby saving millions of dollars on transmission, storage and processing costs. Storage and processing costs account for a large portion of costs for earth observation companies, reducing these costs will make earth observation imagery more accessible for a broader audience including companies performing climate and environment studies. The need for reduced image sizes also extends to video compression. Video calls, telepresence, telemedicine, remote work and metaverse all depend on the ability to stream video over limited or variable bandwidth connections. This technology will find ready applications in the area of high-efficiency video compression.
This SBIR Phase I project uses object detection algorithms to detect areas of importance in an image and utilizes that information to improve the efficiency of image compression. More specifically, the research will develop a compression network that maximizes the detection accuracy of a down-stream, machine learning-based object detector. In contrast, current compression algorithms do not interpret the images they are compressing and simply minimize a visual loss function that treats the entire image equally. The technology will produce images that can be stored in the standard image compression file formats including .png and .jpeg. This technology will enable fast compression and will explore modified compression architectures, quantization, pruning and parallelization using graphics processing units to reduce latency of compression.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
AI-RIS, LLC
SBIR Phase I: Novel machine learning framework for the classification of non-mydriatic retinal images
Contact
11403 SHADOW WAY ST
Houston, TX 77024--5234
NSF Award
2151393 – SBIR Phase I
Award amount to date
$255,945
Start / end date
03/15/2022 – 09/30/2023
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is an artificial intelligence (AI)-based method to screen diabetic retinopathy (DR), the leading cause of vision impairment and blindness in the US. DR affects almost 4 million people in the US and is associated with direct annual costs of almost $500 M. If diagnosed early, clinical treatment and lifestyle changes can halt the progression of the disease, preventing blindness. However, retinal exams currently require expensive equipment and invasive eye dilation that restrict screenings to ophthalmology or optometry practices, leading to the under-diagnosis of the condition, particularly in underserved populations. This project advances a system with a new camera and a machine learning approach to enable recognition of DR and other retinal disorders by clinicians.
This Small Business Innovation Research (SBIR) Phase I project seeks to explore the feasibility of developing a novel software-enabled non-mydriatic fundus camera that can identifiy DR. The proposed innovation is based on: 1) a portable camera that uses near-infrared (NIR) light, invisible to the human eye, to illuminate the retina and acquire fundus images, enabling the use of the device by non-specialists; 2) a novel framework based on transfer learning, which trains Neural Networks with a limited amount of training data (100 images). In this project, a prototype system will collect NIR retinal images, with the goal of developing an AI classification algorithm capable of processing these images. In parallel, a new image processing algorithm will be developed to improve the resolution of the NIR images, based on contrast normalization methods and noise reduction techniques.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ALLIUM ENGINEERING, INC
SBIR Phase I: High-performance, chloride-proof, ferritic steel for cold spray coating of steel rebar
Contact
6 BIRCH ST
Peabody, MA 01960--3324
NSF Award
2231660 – SBIR Phase I
Award amount to date
$274,993
Start / end date
04/01/2023 – 03/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to develop and commercialize a new type of steel rebar which resists corrosion but outperforms existing rebar and is cost competitive. The advanced steel rebar from this project will enable safer and longer lasting concrete infrastructure, requiring fewer repairs and replacements. This technology will provide increases in the lifetimes of concrete structures thereby reducing greenhouse gas emissions while also supporting a number of clean energy technologies, such as offshore wind, hydroelectric, and nuclear power, all of which require long-lasting reinforced concrete.
This SBIR Phase I project aims to develop a novel custom composition of stainless steel to serve as a protective outer cladding for low-cost carbon steel infrastructure. Several technical challenges will be addressed including tailoring of the composition to be highly corrosion resistant in high chloride environments, maintaining a ferritic structure and mechanical compatibility with a carbon steel substrate, and developing a cold-spray compatible processing technique. This project promises to shift the paradigm in corrosion resistance of steel and concrete infrastructure, enabling a novel coating composition and method to integrate into existing steel mills. If successful, this technology will enable low-cost steel components to reach the lifetimes of stainless steel components at less than half the price, extending lifetimes of key infrastructure as much as 3-fold and avoiding massive public costs and carbon emissions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
AMERICAN MAGNET CO LLC
SBIR Phase I: Process for Producing Steel using Super-Pure Iron Ore Powder (SPIOP) and Hydrogen
Contact
15 ARBOR CLUB DR
Ponte Vedra Beach, FL 32082--2616
NSF Award
2231649 – SBIR Phase I
Award amount to date
$273,920
Start / end date
04/01/2023 – 03/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to enable an environmentally sustainable, reduced-carbon emissions steelmaking process for the iron and steel industry. This industry is responsible for 7%~9% of global carbon emissions. The proposed innovation will produce no solid waste discharge and no greenhouse gas emissions. Furthermore, the proposed innovation will contribute to the United States and society by reducing the use of precious mineral resources and promoting the use of renewable energy sources. Once this innovation is proved to be successful and scalable, it can be commercialized and massively applied to the American iron and steelmaking industry, with the potential to reduce dependency on steel imports, provide greater resilience to the US manufacturing sector, and reduce the carbon footprint.
This SBIR Phase I project will demonstrate the feasibility of producing reduced-carbon emissions steel using Super-Pure Iron Ore Powder (SPIOP) and hydrogen. Steel is ubiquitous in our lives, from bridges and skyscrapers to cars and consumer goods. Yet, producing steel often comes with a long flow sheet, high cost, waste, and pollution. The proposed innovative process uses hydrogen (H2) to directly reduce SPIOP in a high-temperature hydrogen reduction electric furnace to produce steel. The raw iron ore is first ground to a fine target size range (~0-0.076mm) to dissociate iron ore particles from gangue/impure particles such as quartz and rocks. The dissociated iron ore particles are then purified using the company’s high-intensity iron separation technologies. The purified iron ore concentrate is >99.0% purity. The hydrogen reduction process and direct reduced iron melting process occur in the same furnace, producing a one-step crude steel manufacturing process.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
AMIE HEALTH INC.
SBIR Phase I: Artificial Intelligence (AI) chatbot providing flare-up support for patients with endometriosis
Contact
8 THE GRN
Dover, DE 19901--3618
NSF Award
2304436 – SBIR Phase I
Award amount to date
$275,000
Start / end date
08/15/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a novel chatbot that provides support to endometriosis patients. Endometriosis is a debilitating chronic pain disorder that impacts approximately one in ten women in the United States - resulting in significant loss of productivity, low quality of life, and greater than $80 billion annual economic burden. This artificial intelligence (AI) chatbot aims to conversationally educate users regarding clinically accepted paradigms of care and options during scenarios including painful endometriosis flare-ups. This project ultimately aims to improve patients' self-knowledge and quality of life, expand access to patients in rural communities to improve endometriosis patient recovery, and reduce the burden of the disease due to lack of knowledge or hesitancy for proper self-care.
This Small Business Innovation Research (SBIR) Phase I project will develop an on-demand endometriosis flare-up management chatbot using advanced Natural Language Processing (NLP) and Understanding (NLU). Many endometriosis patients experience pain or symptoms on a daily basis but symptoms can become unbearable during flare-ups, causing patients with little support to seek emergency room care. The conversational chatbot will be trained to answer user’s questions, guide them through pain-reducing exercises such as pelvic floor therapy, and provide direction to professional resources. This framework uses an internal Natural Language Understanding unit to create user intents, variable entities, conversation context, and conversation resuming slot filling. The technology incorporates emotional understanding and empathetic mirroring allowing the chatbot to have a natural freeform text conversation with the user to encourage them to freely express themselves and feel understood during moments of crisis. This chatbot will serve as a digital companion for patients, acting as both a supportive friend (listening and validating) and as a virtual coach (guiding the patient through exercises to alleviate symptoms). This technology project will design, develop and validate the chatbot in a limited patient pilot.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
APPLIED IMPULSE INC.
SBIR Phase I: Repair, Weld, and Build Metallic Parts with Fill Impact Welding
Contact
2076 FAIRFAX RD
Columbus, OH 43221--4319
NSF Award
2322343 – SBIR Phase I
Award amount to date
$275,000
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project seeks to develop a welding technology that will improve the repair, joining, and additive manufacturing of metallic parts and features. First applications include material additions to repair gouges and mis-drilled holes during aircraft production and service, both of which represent significant financial opportunities. Repairs to the structural material are not currently permissible in a production environment due to adverse effects of currently available repair methods on base material properties, usually due to extreme temperatures. Aerostructure manufacturers have a strong incentive to minimize the weight of the aircraft structure, often at the significant financial and environmental expense of scrapping a whole panel. Maintenance, repair and overhaul often entails total replacement of damaged components with new ones as improper repairs of critical components can cause catastrophic harm. Replacement of parts is expensive and has long lead times due to high-value, low-volume nature of the aerospace industry. This project will develop an effective restoration method to repair of metallic components, while being agnostic to the material and part geometry. Reclamation of previously unrepairable parts made from materials such as titanium, nickel, and aluminum has a large positive environmental impact. Additionally, by broadly enabling solid-state joining, this technology will disrupt the welding industry, globally valued at $20 billion. The foundational technology platform, led in the US, will produce new jobs in science, technology and engineering fields while bolstering domestic manufacturing supply chains.
The innovation underpinning this project involves the sequential, tactical, and controlled deposition of metals using explosive welding. Explosive welding uses coin-sized metallic elements launched to speeds in the range of 300-1000m/s without explosives. While it is known that explosive welding can weld large plates together, the method is not suited to automation or conventional industrial settings. Impact welding will be developed as a fill-welding technique, much like a filler metal in conventional welding, and will use wrought sheet metal as feedstock. Here, electrically vaporized metallic foils will be used as the driver for the fill elements and the research will focus on whether those elements can be launched reproducibly to develop large bond areas and reproducible positioning. The ability to control element shape and orientation during flight and produce an interface that is fully welded are the most high-risk aspects of the technology. Mechanical testing, scanning electron microscopy, and inline process monitoring such as photonic Doppler velocimetry will be performed. This effort will develop a new process-structure-property loop, with the goal of producing parts that are better than those made with a competing technology such as cold spray as measured by total energy consumption, cost, and mechanical properties.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
APPLIED RESEARCH TEAM, INC.
SBIR Phase I: Radar Snow Retrieval
Contact
1750 WEWATTA ST
Denver, CO 80202--6696
NSF Award
2232761 – SBIR Phase I
Award amount to date
$275,000
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will develop transformative, machine-learning algorithms that will improve water management. Water management is critically important to the social well-being, food supply, and climate resiliency of the local population in the Western United States, yet water managers lack adequate snowpack depth and water content information necessary for the management, storage, and transfer of water for irrigation and consumption. Water deficits are made worse when snowpack depths are in error, potentially resulting in devastating damage to agricultural economies and vulnerable populations. The proposed technology will be able to scale from storm events to seasonal snowpack estimations and provide accurate mappings of snowpack depths and water equivalents for watershed areas needing water management.
This Small Business Innovation Research (SBIR) Phase I project develops algorithms for determining snowpack depth and water content. Snow retrieval algorithm development has not kept pace with the deployment of short wavelengths. C- and X-band radars are used as ‘gap-filling’ radars in mountainous valleys. Developing effective algorithms for detection of snow water equivalent is needed for these short wavelength radars. Artificial Intelligence/Machine Learning (AI/ML) and optimization algorithms are expected to improve estimation accuracy compared with point-scale (sensor) observations and across watershed areas relevant to water management. Physics-guided neural networks (PGNNs) can produce physically consistent results and generalize to out of sample scenarios. Application of a PGNN to snow retrievals is expected to perform better than purely data-driven or deterministic algorithms. Anticipated technical results will provide water managers with a cloud-based subscription service updated in real-time, using historical and current radar data to improve operational decision-making.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
APPLIED SENTIENCE INC.
SBIR Phase I: A Human-Aware Platform for Socially Collaborative Personal Artificial Intelligence (AI) Assistants
Contact
353 KEARNY ST
San Francisco, CA 94108--3226
NSF Award
2223224 – SBIR Phase I
Award amount to date
$275,000
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is enabling Artificial Intelligence (AI) assistants to become proactive, empowering them to provide better service to users. Currently, commercial AI assistants respond to user requests reactively. The technologies developed in this project would provide AI assistants with the situational awareness to understand users’ lives and predict their needs. The technology will also enable social intelligence to take the initiative to support users in appropriate ways. This SBIR Phase I project will apply these technologies to a consumer product for assisting users with time management and meeting goals while establishing and strengthening healthy, desirable habits in their daily lives. Proactive personal AI assistants have the potential to improve productivity, convenience, and quality of life for every person, as well as to promote aging in place with greater independence and wellness. Fundamental scientific advancements will also enable a new generation of potential applications for AI assistants across sectors, fueling economic growth and creating jobs.
This project addresses two central technical challenges for enabling proactive AI assistants: contextual awareness of users and agent-initiated interaction. Contextual awareness includes the AI agent’s real-time understanding of current user state and activity, as well as a long-term understanding of past user habits. The project proposes to develop hybrid computational models combining machine learning of multimodal user observations from visual, acoustic, and geolocation data with probabilistic graphical models that perform long-term inference and prediction over historical user observations. A virtually-embodied AI agent will leverage these contextual awareness representations to conduct real-time, face-to-face collaborations with users. The project proposes to research and develop a dynamic scheduling approach to proactively enable the agent to communicate with users. These models will be integrated within a broader system that assists users with time management. This system will implement an end-to-end architecture for protecting user privacy while handling their data. The technical solution will be validated based on quantitative metrics related to utility and user acceptance by deploying the prototype in end users’ homes over a multi-week period and conducting surveys about their subjective experience of the proactive AI assistants.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ASIMICA LLC
SBIR Phase I: Boosting Industrial Bio-Fermentation with Microbial Stem Cells
Contact
1938 HARNEY ST STE 305
Laramie, WY 82072--3037
NSF Award
2222602 – SBIR Phase I
Award amount to date
$274,100
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to reimagine bio-manufacturing with a novel platform technology that could boost the yields of many products, including food additives, biomaterials precursors, biofuels, and pharmaceuticals. The technological advancement addresses a fundamental issue that limits conventional bio-fermentation, which is that producing cells suffer limited health and viability in exchange for higher yields. In this proposal, genetic tools will be used to divide the labor of cell reproduction and product synthesis into two different cell types, called stem cells and factory cells. As older factory cells become exhausted, productivity is maintained by new factory cells, which are born from the stem cell population. The approach may be particularly well suited to biofuels and other molecules that are difficult to produce in large quantities by conventional bio-fermentation because the product is toxic to the cells that make it. It could be applied toward increasing the profitability of existing bio-processes and also for bringing new products to market, which are currently too difficult to produce. In this project, the team seeks to demonstrate the benefits of producing a fuel (limonene) and a dairy enzyme (chymosin), as proof of its application in biofuel and agricultural sectors. Broad industrial implementation will advance bio-manufacturing toward the ‘green’ revolution, contributing to the development of cleaner industries and decreasing US and global reliance on fossil fuels.
This project aims to solve two major limitations of microbial fermentation processes: metabolic exhaustion and genetic drift. These are nearly universal problems in the industry. Highly producing cells can become inactive due to the lack of metabolic resources, cytotoxic effects of products, and mutations that break the biosynthetic pathway. In this project, Microbial Stem Cell Technology (MiST) uncouples growth and production by establishing a multicellular system. One cell type is dedicated to product synthesis (factory cells), while another (stem cells) is responsible for cell division and the generation of new factory cells. As older factory cells lose productivity, the bioreactor is continuously replenished with new factory cells, derived from the stem cell population. By maintaining an active factory cell population, MiST-supported cultures are expected to exhibit increased production longevity and higher overall yield than conventional bio-fermentations. This project aims to validate the technology in E. coli engineered to produce limonene, a precursor for biodiesel and other useful chemicals. In the factory cells, T7RNAP will drive high-level expression of a suite of biosynthetic enzymes. Since limonene has a cytotoxic effect on producing cells, MiST-supported factory cell replenishment is expected to increase productivity by more than 2-fold compared to the conventional limonene-producing strains.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ASTEK DIAGNOSTICS LLC
SBIR Phase I: Automated One-Hour Testing for Bacteremia and Antibiotic Sensitivity
Contact
875 HOLLINS STREET
Baltimore, MD 21201--1252
NSF Award
2304069 – SBIR Phase I
Award amount to date
$274,587
Start / end date
05/01/2023 – 12/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve early detection and clinical management of bacteremia in patients suspected of bacterial sepsis. Over 250,000 patients die of sepsis each year in the U.S., which results in hospitalization costs exceeding $20 billion. A sepsis patient’s risk of death has been demonstrated to increase by 4-7% per hour, where timely administration of appropriate antibiotics is expected to improve patient survival. Unfortunately, the standard of care approach for determining antibiotic susceptibility requires overnight culturing that can take a day or more. Currently, broad spectrum antibiotics are administered within the first hour of treatment with the hope that the pathogen will be susceptible, while waiting for antibiotic susceptibility results. The growing landscape of antibiotic resistance severely undermines the efficacy of the standard approach, as many pathogens are increasingly resistant to broad-spectrum antibiotics. This project seeks to advance development of a novel optical approach for determining antibiotic susceptibility results within 1 hour to support the use of appropriate antibiotics, which has significant potential to improve patient mortality outcomes and reduce hospitalization costs.
This Small Business Innovation Research (SBIR) Phase I project involves an optical technology for the rapid determination of antibiotic susceptibility results from a blood sample for patients suspected of bacteremia. The product uses an assay for assessment of bacterial metabolism, where a blood specimen may be cultured with and without antibiotics to determine antibiotic susceptibility. Advancements in the assay and microfluidics enable delivery of antibiotic susceptibility results within one hour. This timeframe would enable clinicians to use antibiotics that are targeted to the patient’s specific bacterial pathogen significantly earlier, which is expected to improve patient outcomes. The goal of this project will be to rigorously demonstrate the proof-of-concept and reproducibility of the technology.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ATLANTA ANALYTICS LLC
SBIR Phase I: Simulating Demand and Competition for Emerging Transportation Modes
Contact
1334 EDMUND PARK DR
Atlanta, GA 30306-
NSF Award
2233320 – SBIR Phase I
Award amount to date
$268,580
Start / end date
04/01/2023 – 12/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to advance the state-of-the-art for demand modeling for electric aircraft by developing a decision support tool that assesses the broad economic, environmental, and equity impacts that these new modes of transportation will have on society. The project is motivated by recent advancements in battery technologies, autonomy, and distributed propulsion that are transforming air transportation through the development of a new class of electric vertical takeoff and landing (eVTOL) aircraft. The development of electric aircraft for longer distances is also in progress and could revitalize and support new service out of regional airports. The decision support tool can be used by government agencies, aircraft manufacturers, and other stakeholders to guide policy and multi-million dollar public and private infrastructure investments in a way that achieves a balance between economic, environmental, and equity impacts.
This SBIR Phase I project will push the frontiers in modeling demand for new transportation modes, simultaneously addressing competition with ground modes, competitive pricing, and stimulated demand. The project focuses on examining how to model competition between air and ground transportation for distances between 50 and 350 miles where air travel has not been economically feasible in the past. This project focuses on three unproven, high-impact innovations. The first is the design of a scalable, demand simulation platform for air travel that includes distances in which the new aircraft are expected to operate. The second is the development of algorithms that use location-based data to identify the movement of current passengers between airport pairs and to predict demand stimulation. The third is the use of machine learning to calibrate air transportation networks for competitive marketplaces that have hundreds of inputs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
AVALANCHE ENERGY DESIGNS, INC.
SBIR Phase I: CHARACTERIZATOIN OF FUSION GAIN FACTOR Q FOR ORBITRON MICRO FUSION REACTOR
Contact
9100 E MARGINAL WAY S
Tukwila, WA 98108--4028
NSF Award
2303759 – SBIR Phase I
Award amount to date
$274,890
Start / end date
09/01/2023 – 05/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Phase I Small Business Innovation Research (SBIR) project is to develop a small plasma confinement device called an orbitron, which could have applications to allow low cost, highly mobile fusion sources. Markets with the largest opportunity to benefit from small, carbon-free, micro-fusion reactors are the “hard-to-decarbonize” industries like long haul trucking, maritime shipping, aviation, distributed energy, and also space power and propulsion. The development of a small clean energy fusion reactor would be a transformative technology for society. The proposed micro-fusion device may enable continuous clean energy production from readily available elements, without the use of long-term radioactive elements. This microfusion device is also expected to be orders of magnitude cheaper than larger scale fusion reactors, and will allow for iterative design and testing for optimization.
This SBIR Phase I project will result in the ability to achieve predictions of the fusion gain factor (Q) for orbitron-based micro-fusion reactors. Orbitron science combines aspects of electrostatic ion traps, like an Orbitrap, with high voltage microwave-type electron confinement in “crossed-fields” like a Magnetron. The resulting plasma regime is novel and exhibits very high ion and electron energies, moderate densities, and long particle confinement times. Optimized fusion gain factor modelling will be achieved via systematic anchoring and validation of Particle-in-Cell (PIC) code via experimental measurements. Discrete experiments with small orbitron fusion reactors will be used to assess the various plasma loss mechanisms. These mechanisms include ionization between fuel ions and neutral background atoms, particle scattering collisions to the device walls and Bremsstrahlung X-ray radiation losses. Once these mechanisms are correlated with the PIC code, detailed assessments of the simulated fusion plasma will be made to determine the potential Q of a future small-scale fusion reactor for energy production. This gain in understanding will enable development of solutions to mitigate loss mechanisms in future prototypes to maximize Q for small net energy fusion devices.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
Alchemie Solutions, Inc.
SBIR Phase I: Re-envisioning alt text for education through concurrent authoring and diagram design
Contact
4735 WALNUT LAKE RD
Bloomfield Hills, MI 48301--1328
NSF Award
2221722 – SBIR Phase I
Award amount to date
$275,000
Start / end date
03/01/2023 – 02/29/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to create a pathway to success in Science, Technology, Engineering and Mathematics (STEM) for all students, including those that are blind or have low vision (BLV), with a usable and effective description system for STEM diagrams. Only 3% of BLV individuals have been employed in a STEM field. The lack of accessible accommodations creates roadblocks for these students in STEM education throughout their academic journey, leading many students to give up, even though they have as much potential for academic success as the general population. All web-based, non-decorative diagrams are required to include alternative (alt) text descriptions for use with screen readers. Current methods for making STEM visualizations accessible through alt text are not scalable or amenable for use with dynamic digital media, nor is alt text standardized or proven usable by learners, resulting in lost learning opportunities for BLV students. The absence of reliable auto alt text generation is a roadblock for educational institutions and academic publishers to adopt new instructional technologies, as digital content and learning systems must include accommodations for accessibility.
This Small Business Innovation Research Phase I project will create a first-of-its kind system that generates descriptions of STEM diagrams as the educational resources are constructed. This project will go beyond current standards for accommodation, by creating an innovative layered approach to the alt text representation of STEM diagrams so that screen reader users can interact with the content in a manner that is best supported by their skills and prior knowledge. The technical objectives are to: 1) create a system that produces machine-readable configurations for the components of a diagram and spatial relationships between the components, and then generates a text-based description of the diagram and 2) devise a non-linear method for representing STEM content through a standardized system of representational layers within the description. Both the general description (objective 1) and layered description (objective 2) will be constructed by matching the diagram configuration to a context-driven lexicon.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ArchDia LLC
SBIR Phase I: Value-Driven Design Debt Management for Contemporary Software Systems
Contact
173 VILLAGE DR
Cranberry Township, PA 16066--3349
NSF Award
2236824 – SBIR Phase I
Award amount to date
$274,894
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will design a value-driven design debt management system applicable to modern, distributed, dynamic software systems. This system will pinpoint and quantify design debt and recommend the most effective debt-reduction strategies. This system will also support risk analyses for managers and designers, helping them choose a strategy that prevents severe decay in product performance, and maximize product value, productivity, and quality.
This Small Business Innovation Research (SBIR) Phase I project provide a value-driven, design-debt management system that (1) discovers and empirically validates design debt in modern distributed and dynamically-typed software systems based on options theory, Conway's law, and design principles, leveraging a knowledge graph to capture and manage implicit, heterogeneous, and distributed entities and relations; (2) discovers the intrinsic and implicit relations among multiple design anti-patterns and creates an algorithm to prioritize and recommend the most effective debt-reduction strategies; and (3) bridges the gap between development and management, enabling a user to simulate refactoring outcomes and evaluate their economic implications by combining Monte Carlo simulation and Datar-Mathews option valuations, for each proposed refactoring strategy. The outcome of these research and development activities will lead to the first framework specialized for managing technical debt at the design and architecture level, rooted in financial and design theory, and applicable to modern distributed and dynamically typed software systems. This project has the potential to fundamentally change the management of software, supporting better informed, data driven decisions by focusing on their economic values.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
Articulate.AI Inc.
SBIR Phase I: Adaptive Dialog Systems As Second Language Learning Partners
Contact
350 W 42ND ST 55D
New York, NY 10036--6963
NSF Award
2213202 – SBIR Phase I
Award amount to date
$256,000
Start / end date
03/15/2023 – 11/30/2023 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project centers on offering a lower-cost, more accessible alternative to help people everywhere learn to speak a new language. By using state-of-the-art conversational artificial intelligence (AI), the project offers students individualized, curriculum-based learning that is affordable and accessible 24 hours a day, with no limits on the amount of time they can practice. Additionally, the AI system will be able to analyze learners’ speech to offer suggestions on ways to improve their grammar and conversational fluency, in much the same way a trained tutor would assist a student. The project brings conversational AI to the language learning market, enabling a more effective, entertaining, and low-cost product to transform the current language learning paradigm.
This Small Business Innovation Research (SBIR) Phase I project is an innovative integration effort to combine state-of-the-art AI technologies for education applications. The project will contribute new user-adaptive algorithms and datasets for dialog policy training on language learning tasks. The project will contribute to the company’s open-source, multimodal, dialog system framework to enable the dialog community to reduce the entry barrier for multimodal dialog system research. The project will also provide the community with an annotated conversational dataset for grammatical error correction along with a trained grammar correction model and grammar error type detector. The developed educational dialog systems for second language communication learning can also serve as a general framework for education experts to test different learning theories. This project will also explore hypotheses of whether adaptive conversation experience and different feedback structures will impact learner confidence and learning gain.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
Atrevida Science LLC
STTR Phase I: Active Blade Morphing Control to Improve Efficiency and Reduce Loading for Wind Turbines
Contact
8941 GALWAY TER
Clarence Center, NY 14032--9407
NSF Award
2151668 – STTR Phase I
Award amount to date
$256,000
Start / end date
03/15/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is expanding global deployment of wind turbines with increased production not obtainable with today’s fixed blades. An adaptable blade with advanced control capabilities helps solve technical and scientific challenges as wind projects accelerate their move offshore, extending the physics of the larger turbines needed for future wind farms. Developing a high-fidelity modeling tool to design morphing blades capable of boosting energy production, reducing wear and tear, dampening vibration, improving stability, and reducing load effectively achieves two crucial goals. These goals include accelerating the deployment of renewable energy with affordable electricity that is efficiently and economically extracted from wind and improving the loading and stability necessary for the development of floating wind farms capable of installation in challenging water depths and extreme weather. The technology proposed makes large turbines better at capturing wind in more locations to protect against volatile energy prices, generate jobs, and promote greater participation in the global energy transition for developed and developing countries alike. Furthermore, the blade technology resulting from this research provides opportunities for new manufacturing techniques and commercial applications in other industries such as aviation, automotive, and marine renewable energy.
This STTR Phase I project proposes to examine a high-fidelity model to support the design and control of an advanced wind turbine blade configuration with an adaptive twist angle distribution (TAD). Conventional control is generally applied to rotor torque to maximize wind capture, and thus production, below the rated wind speed. Above this speed, control shifts to the blade pitch angle to maintain full power. However, limitations in existing designs lead to trade-offs where wind capture or power production is relinquished to reduce loads, mitigate vibration, and improve stability. The actively adaptive TAD provides greater control capabilities and satisfies these objectives without trade-offs. A crucial goal of this research is the means to understand the complex aeroelastic and aerodynamic relationships with respect to the TAD. The technology is a high-fidelity model that simulates these dynamics and the aeroelastic performance in a reasonable amount of time. This model involves the development of a framework combining these dynamics and uses computational tools that leverage data analytics and machine learning. The technical result of the proposed work is the creation of a digital twin that enables effective design and robust control of highly sophisticated blades with adaptive TAD.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
BACTANA CORP.
SBIR Phase I: Faecalibacterium prausnitzii supernatant oral formulations to improve insulin sensitivity and treat prediabetes
Contact
400 FARMINGTON AVE.
Farmington, CT 06032--1913
NSF Award
2151168 – SBIR Phase I
Award amount to date
$256,000
Start / end date
03/01/2022 – 10/31/2023 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to prevent and treat prediabetes and type 2 diabetes. More than 1 in 3 American adults have prediabetes or type 2 diabetes with associated healthcare costs exceeding $327 billion. Current therapies often present adverse effects or are ineffective in some patients. The top five human diabetes drugs alone are expected to cost $23 billion annually by 2024. This project advances a novel diabetic treatment composed of a postbiotic mixture from beneficial gut bacteria. This will improve clinical outcomes for prediabetic patients.
This Small Business Innovation Research (SBIR) Phase I project will evaluate the efficacy and elucidate the mechanism of a potential microbiome-based treatment toward an oral treatment that effectively reduces diabetes-associated markers. The three technical objectives are to: 1) evaluate the technology and demonstrate equivalent or superior performance compared to existing antidiabetic drugs, 2) better understand the mechanism that leads to efficacy in the treatment of prediabetes and type 2 diabetes, and 3) identify the active molecule(s) from the postbiotic mixture. These objectives will be carried out using rodent trials, cell-based assays, and advanced separation techniques.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
BLOOM SURGICAL, INC.
SBIR Phase I: Optimization of a Novel Compliant Mechanisms-Based Laparoscope Cleaning Device
Contact
10472 EDINBURG DR.
Highland, UT 84003--9584
NSF Award
2213695 – SBIR Phase I
Award amount to date
$256,000
Start / end date
06/01/2023 – 05/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a novel product for ensuring proper visualization through intraoperative scopes during laparoscopic procedures. Worldwide, 13 million laparoscopic surgeries are performed each year. Surgeons require proper visualization of the operating site which entails continually wiping the lens, reinserting the scope, relocating the surgical site, and then resuming the surgery. This difficulty in visualization results in the need to temporarily halt the operation and potentially lose critical focus of the surgical area in order to restore the surgical field of view. Surgeons repeat this process an average of six times per hour, accounting for nearly 1/3 of the operating time. This time delay results in an estimated loss of 336,000 hours of operating room procedure time and $1.25 billion in productivity losses in the United States alone each year.
This SBIR Phase 1 project will develop operating prototypes of a novel, flexible, micro-mechanical mechanism with multiple degrees of freedom. The device integrates flexible and conforming mechanisms with a wiping blade to enable real time wiping of surgical scopes and ports. This technology enables surgeons to quickly and intraoperatively re-enable laparoscope vision within the patient. The technical challenges of the project include the development of a computational engineering model that optimizes user control of off-axis stiffness, force response, and stress. The team will also need to ensure the solution has sufficient fatigue life and predictable mechanical response throughout the duration of the procedure. Computational engineering models will be used to design and develop several manufacturable prototypes, which will be tested and validated with several currently available laparoscopes.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
BLUEFUSION INC.
SBIR Phase I: Software-Defined Sub-Terahertz Imaging Radar for Algorithmic Agility and All-Weather Transportation Safety
Contact
24 SCHOOL ST
Boston, MA 02108--5140
NSF Award
2230398 – SBIR Phase I
Award amount to date
$275,000
Start / end date
07/01/2023 – 03/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is the development of a universal, affordable, and sustainable sensing solution to enable perimeter security and transportation safety under all weather conditions. Current sensing solutions available today are based on a single modality, expensive to deploy, and not robust to adverse weather conditions. Current solutions also employ proprietary sensor processing interfaces, do not provide the quality of data needed for decision-making by continuous learning, are hard to upgrade, and have poor size, weight, and power specifications. In contrast, the proposed technology leverages the strengths of multiple sensing modalities on a single, converged, open compute platform to enable robust perception in adverse weather conditions while offering significant advantages to the total cost of ownership. The technology has a wide range of applications in sectors as diverse as automotive, robotics, enterprise, aerospace, and defense. The solution developed under this project has the potential to save lives by reducing the number of road accidents, improving the driver reaction time, protecting vulnerable road users such as pedestrians and bicyclists, reducing the downtime for a shipping company, minimizing the costs associated with collision claims and repairs, and detecting, classifying, alerting, and tracking threats on the ground and in the air.
This Small Business Innovation Research Phase I project develops a novel, scalable, centralized sensing platform and a multi-spectral sensor prototype to realize ultra-fine resolution in range, Doppler, azimuth, and elevation domains using commercial, off-the-shelf processing elements. Advanced compression algorithms are employed to transport sensor measurements over secure, open, low-cost, and low-latency interfaces to the centralized processing unit to enable multi-modal sensor processing, early sensor fusion, and high-dimensional perception for higher-level decision-making. The de-coupled sensing and processing architecture produces unprecedented access to measurement-level data to enable artificial intelligence and machine learning-based algorithmic discovery. False-alarm-constrained global object detection algorithms are employed to enable localization, navigation, and mapping for operation under adverse weather conditions. Proof-of-concept sensor hardware is developed with laboratory and field experiments to validate the architecture, technology, algorithms, and software. Some of the key technology risks addressed in this proposal are antenna design and fabrication at millimeter frequencies and above, cascading of multiple radio frequency transceivers to realize a large number of spatial channels, and hardware-level synchronization across the sensors.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
BOTELMOT RESEARCH LLC
SBIR Phase I: A wave attenuation technology for oyster reef restoration and small dock protection
Contact
48 HERNANDEZ AVE
Ormond Beach, FL 32174--5506
NSF Award
2223944 – SBIR Phase I
Award amount to date
$254,493
Start / end date
03/01/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercialization potential of this Small Business Innovation Research (SBIR) Phase I project is in advancing oyster reef restoration efforts by improving the understanding of oyster reef formation at the individual level and improving the resilience of waterfront infrastructure including small docks. This project will develop a technology that could improve ecosystem services and private dock protection. The socioeconomic importance of intact infrastructure and coastal protection includes maintaining property values, economic job opportunities, and supporting coastal infrastructure by minimizing erosion.
This project is focused on a shallow water wave attenuation system that does not restrict water flow behind it and does not create sediment accretion. This system is needed for both oyster reef restoration and the protection of small docks. Oyster reefs do not establish in areas that have been channelized and are then subjected to both boat waking and increased fetch due to the increased wave energy. The proposed system will be indexed to the water surface and will adapt to changes in water height but will necessarily be anchored to the bottom. What is unknown in this project is how rigidly the system has to encounter oncoming waves in order to both survive extreme events and not absorb too much wave energy as to limit water flow. The development of a nontoxic and biocompatible prototype would provide the data necessary to project both economic and hedonic valuation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
BRAILLEWEAR
SBIR Phase I: A Wearable, Independent, Braille-Assistive Learning Device
Contact
611 SOUTH DUPONT HIGHWAY, SUITE 102
Dover, DE 19901-
NSF Award
2236574 – SBIR Phase I
Award amount to date
$274,999
Start / end date
04/01/2023 – 03/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in creating an independent. assistive Braille learning device for blind people. The ability to read Braille is highly correlated with improved independence and quality of life. An estimated 70% of the blind are unemployed yet, of that subpopulation that is Braille literate, only 10% are unemployed. There is a Braille literacy crisis - only 8.5% of the blind population in the US can read Braille today, compared to 50% in the 1960s. There are several factors theorized to contribute to increasing Braille illiteracy including: 1) a shortage of teachers qualified to teach Braille, 2) negative outlooks on the difficulty and cost of Braille learning, and 3) and difficulties integrating blind students into mainstream schools that don’t have the specialized resources for this population. The results of this project will assist students of all ages in learning how to read Braille, including secondary Braille learners who become blind later in life. Aiming at inhibiting the Braille literacy crisis, the technology enables the blind to be given the same opportunities as their sighted peers, including better chances at graduating from high school and college, obtaining employment, and having high independence levels.
The intellectual merit of this project is in development of a wearable, computer vision-based, real-time Braille-to-speech learning device. While the primary mission of the project is to unlock the full potential of blind individuals through Braille literacy, the overall goal for the technology is to unlock the full potential of human touch with computer-assisted augmentation cues in response to intricate textural patterns. The proposed technology will detect such patterns in a contactless approach, preserving the integrity of the material, and provide auditory feedback in real-time to allow for mechanosensory-augmented feedback. This project focuses on establishing the technical feasibility of such an approach by: 1) determining if the device and interpreting algorithms can be made robust to environmental and user postural variations, 2) developing capabilities to perform well on textured and/or patterned surfaces, and 3) conducting usability testing to identify areas of the user experience that must be enhanced in the future to be viable in the market with two vital stakeholders - Braille tutors and Braille students. These goals, if completed successfully, will not only impact Braille learners but also open up other market applications for this technology such as manufacturing and medicine.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
BRAVE VIRTUAL WORLDS, INC.
SBIR Phase I: Brave Virtual Worlds Human Movement Artificial Intelligence (AI) Engine and Biofeedback Loop
Contact
800 BRAZOS ST
Austin, TX 78701--2538
NSF Award
2326586 – SBIR Phase I
Award amount to date
$273,915
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project aims to develop a biofeedback system that combines wearable sensors, real-time data capture, and immersive virtual reality visualization. This system has the potential to enhance the understanding of joint angles and movement patterns enabling in-depth analysis of human movement. The proposed technology targets a significant market opportunity in the athletic training and physical therapy industry, which is valued at $200 million.
This SBIR Phase I project addresses intellectual merit through a systematic research approach. The project involves designing and refining the biofeedback system, conducting extensive data collection, and implementing advanced algorithms for real-time analysis. The project's goals include developing a user-friendly interface, optimizing sensor accuracy, and creating a seamless biofeedback system for improved training and rehabilitation. Anticipated technical results include the design and development of a machine learning layer to classify and identify key components of movements in order to create a contextual database for further analysis to determine movement efficiency and highlight movement patterns as well as the development of a corrective exercise/feedback layer using the context from the machine learning layer to generate further insights into what is causing highlighted movement patterns This second later will incorporate corrective exercises that are suggested for improvement. Additionally, a real-time layer incorporating the first two layers into the real-time portion will be used for immediate feedback to the end-user, thus providing a closed bio-feedback loop.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CALM WATERS GROUP LLC
SBIR Phase I: A Stakeholder Management Platform for Environmental Justice
Contact
2778 GEORGIA ST
Vallejo, CA 94591--6502
NSF Award
2208725 – SBIR Phase I
Award amount to date
$255,937
Start / end date
01/15/2023 – 12/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial impact of this Small Business Innovation Research Phase I project is to break the negative feedback loop of multi-generation, multi-sector cascading impacts of environmental and social injustices by providing effective tools for engagement of underserved communities. The tide is turning as Environmental Justice policies are increasingly being adopted by governments at the federal, state, county, and regional levels - to explicitly and meaningfully engage underserved communities early and often in all regulatory and planning phases. This project addresses the initial challenges around automating the process of identifying, building trust with, and elevating community-based organizations and underserved communities with the goal of accelerating the implementation of equitable climate-smart infrastructure projects. The proposed innovation will help agencies scale up their reach, accuracy, and efficiency of community engagement, establish and build trust with underserved communities, and accelerate community participation in planning and infrastructure projects throughout the United States. The project helps community leaders raise their voice and visibility with agencies, gain access to timely information across different agencies, and gain access to funding opportunities. Successfully implementing this project has the potential to reduce cost burdens on communities, while also supporting economic empowerment in communities where the project is deployed.
This Small Business Innovation Research Phase I project will demonstrate feasibility of using TextAI (Natural Language Processing using Artificial Intelligence (AI)) and GeoAI (Geographic Information Systems using AI) to perform location-based stakeholder discovery of Community Based Organizations (CBOs). This goal poses technical challenges: high variation in unstructured data; quality of manual annotations; complexity and diversity of attributes; and disambiguation of location identification and social challenges. The communities of interest have low trust in the government and technology and need transparent data sharing and ethics. The key innovation is a workflow that combines deep technology development with participatory and inclusive co-design with community-based organizations and government. If the project succeeds, it will have substantial payback for underserved communities. The first use case is with the San Francisco Bay Conservation and Development Commission, which has the right size and scope of jurisdiction to capture variations in data type, stakeholders, and users - and includes a highly diverse set of demographics across urban and rural communities - while also being small enough to manage its data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CAMBRIDGE TERAHERTZ INC
SBIR Phase I: Terahertz Imaging Radar for Law Enforcement
Contact
162 BROOKLINE ST
Cambridge, MA 02139--4540
NSF Award
2301538 – SBIR Phase I
Award amount to date
$274,927
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
This broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will improve public safety by furthering terahertz radar imaging technology for concealed threat detection. With increasing cases of weapons violence and mass casualty events across the nation, and an increase in the difficulty of detecting non-metallic weapons such as 3D-printed firearms and ceramic knives, a significant gap exists in threat detection technologies. This solution addresses a recognized need in an approximately $2 billion market which includes law enforcement and event venue security. Existing approaches (such as airport scanners and walkthrough metal detectors) are expensive, intrusive, and inconvenient or leave large gaps in detection capability. Terahertz radar imaging promises the performance of gold-standard airport scanners in a consumer grade, portable, and discreet device. By demonstrating a terahertz radar transceiver, this Phase I effort will de-risk a key technical element of this technology, which is critical for security applications and beyond. If successful, this project represents a significant step forward in addressing society’s concealed threat detection issues.
The intellectual merit of this project revolves around the design, implementation, fabrication and testing of a terahertz radar transceiver, a key component in the approach used in a personnel screening device. No such transceiver is currently available to purchase on the open market, let alone at the costs and volumes required for the proposed commercial applications. When paired with other elements of the imaging system, the result will be a three-dimensional radar imager which is capable of “seeing through” dielectric materials such as fabrics and detecting concealed weapons and contraband, both metallic and non-metallic. The transceiver design effort will feature development of components such as frequency multipliers, amplifiers and mixers, and their electrical, mechanical, and thermal integration into a larger imaging system. This design phase will prioritize achieving cost, yield, and scalability metrics compatible with mass manufacture and widespread deployment. Key considerations involved in this effort are the signal-to-noise ratio (SNR) and Dynamic Range (DR) of the system, both important metrics in imaging performance and therefore weapons detection capability. The project leverages recent advances in terahertz integrated circuit technology. The anticipated result is the experimental demonstration of such a component for integration into the fully functional imaging systems.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CARBON SOLUTIONS LLC
SBIR Phase I: A Decision-Support Tool for Identifying Carbon Dioxide (CO2) Capture Opportunities for the Nation’s Energy Transition
Contact
398 E BELLEFONTAINE RD
Pleasant Lake, IN 46779--9577
NSF Award
2216541 – SBIR Phase I
Award amount to date
$275,000
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in providing much-needed decision-support tool to progress the United States towards a net-zero carbon dioxide (CO2) emissions economy. The Princeton Net Zero America study suggests that a of minimum 0.9 gigatons /year of CO2 sequestration is required to transition the country to a net-zero economy, which is 1.3 times larger than the country’s oil production on a volume-equivalent basis. One of the many substantial challenges to achieving this feat is identifying and profiling capturable CO2 streams from emitters across the United States. Software that could robustly address this challenge would provide benefits to society and the country. For example, climate change is recognized as the largest threat facing humanity and the security of the United States; Transitioning the US economy to net-zero CO2 emissions is one of the largest wealth creation opportunities of our generation.
The proposed CO2 National Capture Opportunities and Readiness Data software will enable users to identify sources of CO2 (e.g., cement manufacturers and ethanol refineries) that could be profitably turned into carbon capture and sequestration (CCS) projects. The database would provide the break-even CO2 capture cost, technology readiness level (TRL), and lifecycle CO2 emissions of any prospective CCS project across the country by integrating the latest public data and scientific research into a single end-user platform. A novel integration of advances in multiple disciplines is required for successful project completion: 1) big data fusion, 2) CO2 capture stream characterization, 3) lifecycle assessment, 4) advanced techno-economic assessments, and 5) software engineering. Additionally, this project will also advance the status-quo by creating dynamic results that can be easily re-generated as needed (e.g., with new technologies deployed or policies enacted).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CARE WEATHER TECHNOLOGIES, LLC
STTR Phase I:Constellation of Nanosatellite Radars for Near-Hourly, Global Ocean Surface Vector Winds
Contact
144 W 400 N
Provo, UT 84601--2855
NSF Award
2304609 – STTR Phase I
Award amount to date
$274,937
Start / end date
03/01/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is a significant improvement in the accuracy of weather forecasts by increasing the refresh rate of sea wind measurements ten-fold. This forecast improvement will increase the economic competitiveness of the United States by improving efficiency in maritime, agriculture, and logistics industries. Improved weather forecasts will advance the health and welfare of the American public by enabling earlier storm warnings that save thousands of lives. Improved weather forecasts will support national defense, while also saving hundreds of millions of dollars in false-alarm hurricane evacuations. Sea wind data will also directly benefit maritime operators, including recreational sailors, ocean carriers and fishers. Wind map and forecast subscriptions from maritime customers represent a $3 billion commercial opportunity.
This STTR Phase I project proposes to study the feasibility of increasing the refresh rate of sea wind measurements ten-fold using a constellation of nanosatellite radars (scatterometers). Current satellites for measuring sea winds are prohibitively expensive and performance has not substantially improved since they were introduced decades ago. The objective of this research is to evaluate the measurement accuracy, cost, and refresh rate of the proposed nanosatellite scatterometers. Additional objectives study the regulatory feasibility, post-processing feasibility, and commercial feasibility. The research includes calculations of the radar signal, heat, data geolocation, cost, mass, data rate, latency, radio interference, and license feasibility. The research also includes simulations of the radar measurement geometry, the post-processing, the wind maps that will be generated by the scatterometer, as well as the constellation, its operations, and its replenishment requirements. The studies in this project answer key feasibility and performance questions posed by potential users. The results, such as data quality, availability, and simulated sample data, will be used in customer discovery to ensure the needs of potential users are met.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CARIDIAN MEDICAL, INC.
SBIR Phase I: An extravascular bipolar catheter for targeted nerve ablation with minimal collateral damage to surrounding tissues
Contact
2450 HOLCOMBE BLVD STE X
Houston, TX 77021--2041
NSF Award
2213155 – SBIR Phase I
Award amount to date
$255,841
Start / end date
03/01/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a novel and emerging method for treating a subset of heart failure patients. Despite modern medical regimen and device therapies, heart failure affects 6 million Americans and remains a leading cause of death and hospitalization in the United States. The proposed system and approach aim to provide a novel procedure and treatment paradigm by leveraging an established clinical access procedure for performing Greater Splanchnic Ablation (GSN). The system would enable rapid and wider clinical adoption of the emerging therapy.
This Small Business Innovation Research Phase I project will develop a novel, minimally invasive, catheter-delivered, venous procedure for performing extravascular Greater Splanchnic Ablation (GSN). The proposed catheter system utilizes a clinically accepted venous approach in order to perform extravascular denervation, while minimizing collateral (off-target) damage to the other surrounding tissues. The objectives of this project include designing and prototyping new devices that will enable localized energy delivery and incorporate a detection system indicative of procedural success. Both benchtop testing and pre-clinical animal model studies will be used to access and ablate, verifying the function and performance of the device.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CARRTECH LLC
SBIR Phase I: Filter Removal of Glass - A better way of filtering injectables
Contact
4539 METROPOLITAN CT
Frederick, MD 21704--9452
NSF Award
2232923 – SBIR Phase I
Award amount to date
$275,000
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business innovation Research (SBIR) Phase I project is an integrated, single needle filter which removes glass shards from injectable fluids. Liquid pharmaceuticals and medications are often stored in glass ampoules. To gain access to these medications, each ampoule is manually broken at the neck by a health care provider. This can result in glass shards entering the medication that can cause patient hematomas and/or internal bleeding. United States regulations currently mandate all ampoules must be filtered by health care providers prior to injection which requires a multi-step filter process using multiple needles and needle exchanges. This project aims to develop a single, effective, inline filter which that requires half the time of this current process, while improving safety for the patient and the healthcare worker by reducing the risks of needle stick injuries. The commercial potential is to become the standard of care for the $3 billion global filter market and consumes over six billion disposable glass ampoules every year.
This Small Business Innovation research (SBIR) Phase I project is a novel, disposable, inline, mechanical filter with optimal porosity and density to remove glass ampoule shards. The current practice of breaking ampules at their neck to access medication results in shards that are currently manually filtered using a filter needle prior to administration into the patient. The filter needle must be removed and discarded, and a second sterile needle placed on the syringe for injection. The company aims to develop a novel, all-in-one, integrated filter and needle system utilizing a single inner blunt needle with a novel filter located at the distal end versus the current proximal end where the Luer loc is located. This solution will enable a single step process for glass filtering and medication injection. The scope of activities includes engineering and validation tests of leakage, pull force, dead volume, labeling and packaging, handling safety, repeatability/consistency, and accelerated aging of the company’s proprietary design concepts. This project aims to provide an assembly suitable for human use that is manufacturable at scale in a cost-effective manner.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CARTESIAN SYSTEMS, INC.
SBIR Phase I: A Handheld Fine-Grained Radio Frequency IDentification (RFID) Localization System for Retail Automation
Contact
169 MONSIGNOR OBRIEN HWY APT 301
Cambridge, MA 02141--1277
NSF Award
2232748 – SBIR Phase I
Award amount to date
$274,960
Start / end date
04/01/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impacts of this Small Business Innovation Research (SBIR) Phase I project will protect retail stores from loss of merchandise. Brick-and-mortar retail is undergoing an unprecedented transformation, having lost billions of dollars over the past decade due to labor shortages, competition from e-commerce giants, and changing expectations from the modern consumer. To address these issues, retailers have been adopting new digital technologies to gain visibility into their inventory, optimize store operations, and gain customer insights. A key technology that has been adopted by over 90% of US retailers, is Radio Frequency IDentification (RFID). RFID tags are cheap, wireless, and battery-less stickers (similar to barcodes) that have allowed retailers to achieve accurate store-wide inventory, resulting in a significant revenue increase for retailers. In contrast to existing (portable) RFID technology which can only determine whether RFID-tagged items are
in the store (i.e., inventory), the proposed technology aims to precisely locate these items throughout the store. The technology leverages billions of off-the-shelf ultra-high frequency (UHF) RFID tags that are already attached to clothing, footwear, and apparel items. In contrast to existing mobile solutions which can only detect RFID-tagged items, the team's handheld device leverages sophisticated signal excitation and processing techniques to pin down each RFID’s exact position with decimeter-scale accuracy.
This SBIR Phase 1 project will build a system capable of identifying and precisely locating RFID-tagged items and includes three main innovative components: (1) a portable, handheld wireless device for locating RFIDs, (2) a scalable cloud and edge computing platform to process and store the data, and (3) a mobile and web user interface for accessing the data and optimizing picking tasks for retail store associates. Realizing the end-to-end platform requires developing efficient sensor fusion algorithms and low-power, low-cost hardware for accurate, robust, and low-latency localization. This technology necessitates addressing challenges that arise from the computational, memory, bandwidth, and power constraints on the edge device. The platform also requires developing the split and cloud computing architecture to efficiently process data from multiple handheld devices in real-time as well as provide the generalizable application programming interfaces (APIs) to integrate this data pipeline with the retail customers. By the end of the Phase I period, the project will have piloted the fully-integrated system in a retail store to evaluate its real-world performance.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CELLCHORUS INC.
SBIR Phase I: Development of arrays to record dynamic interactions between single cells
Contact
5000 GULF FWY RM 118
Houston, TX 77204--0001
NSF Award
2229323 – SBIR Phase I
Award amount to date
$274,988
Start / end date
06/01/2023 – 05/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to enable experts in the biomedical field to improve understanding of how new therapeutic approaches are developed and perform in the context of a complex immune system. This technology could enable new therapies to translate to patients faster, at less expense, and with higher rates of success. These therapeutics, such as antibodies, cell and gene therapies, and vaccines, can deliver excellent results when they work, but available therapies do not work for all patients. To develop and deliver the next generation of therapies to improve the lives of patients, investigators need to be able to understand how immune cells and other cells move, interact, kill, and survive over time. This project allows researchers, developers, and manufacturing experts to understand the functional performance of new therapies earlier, more completely, and at lower expense. Such single-cell analysis is a multi-billion market among commercial and non-profit markets.
The proposed project will develop and rigorously validate a novel array consumable that enables scaling dynamic, single-cell analysis from an early access laboratory to any facility worldwide. Initial design and testing activities for the next generation arrays using non-scalable proof of concept production methodologies have demonstrated the value of the dynamic single-cell functional analysis platform. This project will develop and evaluate two options for producing the arrays, one with an embossing technique and one with a three-dimensional printing technique. Successful completion of this project will support scaling the only platform that can evaluate migration, contact dynamics, killing, survival, subcellular activity, and biomolecule secretion for the same individual cell over time and in high throughput to improve development and delivery of novel therapies faster, with higher rates of success, and at lower expense.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CENTRAL CASTING AI INC
SBIR Phase I: Machine Learning Actors to Improve Connectedness across Remote Teams
Contact
1 QUAIL RUN DR
Methuen, MA 01844--1579
NSF Award
2303389 – SBIR Phase I
Award amount to date
$275,000
Start / end date
08/01/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop machine learning-powered actors (ML actors) that facilitate social encounters between friends, strangers, classmates, and coworkers in user-generated spaces across the Metaverse. The shift towards virtual work, learning, and socialization has been accompanied by significant societal disruption. Over the past few years, people across the United States reported increasing levels of loneliness and isolation. Building off research that shows games are a powerful tool for team building, and non-player characters have a significant impact on building empathy, this project uses ML actors as the building blocks of free-to-play, multiplayer, cooperative games designed to bring remote workers together socially.
This Small Business Innovation Research (SBIR) Phase I project aims to address the challenge of making ML actors viable for user-generated worlds. In order to be effective in the Metaverse, ML actors will need to navigate unfamiliar settings, player dialogue, and behaviors that are hard to predict. Characters will need to be trained on vast quantities of data with some human supervision. This project seeks to prove that ML actors can be trained from large amounts of data by users of no technical background and those actors can then be deployed in a virtual environment in which they are responsive to their environment and player choices. This project has three main steps: 1) learning a large multimodal hierarchical task network from thousands of movie scripts and game logs, 2) connecting that model to a character in a 3D environment, and 3) testing a game with remote teams to gauge efficacy and enjoyability.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CERFLUX, INC.
SBIR Phase I: Predictive ex vivo solid tumor biopsy-chip multiplexer for screening anticancer agents
Contact
215 RICHARD ARRINGTON JR BLVD N
Birmingham, AL 35203--3770
NSF Award
2321805 – SBIR Phase I
Award amount to date
$275,000
Start / end date
08/01/2023 – 03/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project lies in enhancing health outcomes and quality of life for cancer patients. Each year, over 1.7 million Americans are diagnosed with cancer, and treatment turns out to be ineffective for approximately 75% of those receiving systemic therapy. This failure is because every tumor is distinct in makeup and response to treatment. Unfortunately, a generalized treatment approach is forced upon a disease that is uniquely personal due to lack of personalized predictive tools. Consequently, patients are exposed to several rounds of potentially harmful overtreatment until the right regimen is found. Adding insult to injury, over 40% of patients deplete their entire life savings in just 2 years. In discussions with leadership from the Centers for Medicare & Medicaid Services (CMS) and private insurance providers – who currently spend over $35 billion annually on treating just five types of cancer – the inability to match treatments to tumors was unanimously the critical, unmet, and urgent market need. This proposal can make a profound impact on the lives of cancer patients, offer substantial cost savings to insurance providers, and strengthen US leadership in advanced research.
This Small Business Innovation Research (SBIR) Phase I project represents a major departure from the traditional, one-size-fits-all approach to cancer treatment. The proposed technology will rapidly match biopsy tissue from a patient’s tumor with various treatment regimens – before treatment, outside the patient – to identify the right treatment for that patient. In order to achieve clinical translation for this technology, during this SBIR Phase I project, the company will address several key objectives: 1) establish standard guidelines for tissue collection, handling, and cold chain logistics to minimize the impact of these factors on the viability of live solid-tumor biospecimens; 2) generate 3D bio-printed mimics of patient biopsies for extensive validation and refinement of the technology; and 3) demonstrate the predictive capacity of this platform for personalized cancer treatment. If successful, this personalized approach will not only reduce the cost of cancer treatment incurred by health insurance providers but, more importantly, also lighten the emotional, physical, and financial burden suffered by patients.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CLASS BUCKS LLC
SBIR Phase I: Class Bucks Engagement and Gamified Learning System
Contact
416 PASEO DEL BOSQUE NW
Albuquerque, NM 87114--2264
NSF Award
2233659 – SBIR Phase I
Award amount to date
$275,000
Start / end date
04/01/2023 – 03/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business innovation Research (SBIR) Phase I project is in the positive effect it will have in the classroom, and ultimately, on school dropouts. Dropouts face bleak economic futures, are more likely to commit a crime and be incarcerated, suffer poor health, and exact a financial toll on society. Since dropping out of school is a gradual process of a student becoming disengaged in the classroom, the proposed project will address school dropout at the most elemental level. At the end of this project, the development team will have an innovative, web-based game that will transform the classroom dynamic, in any school, and give teachers meaningful analytics so they can better meet their student’s needs, keep them engaged in the learning process, and reduce the number of kids dropping out of school. The innovative platform has the potential raise the learning opportunities for marginalized learners and stimulate positive societal change.
The intellectual merit of this project is in the quantitative measurement and tracking of student engagement and production of a longitudinal analysis for each student, so teachers can track engagement throughout an entire quarter, semester, or year. The solution will empower instructors to make more well-informed decisions for their classrooms. The research will focus on converting the original composite, laborious, pen, paper, and spreadsheet version of the tool into a web-based version that integrates with teacher lessons while accommodating diverse content areas and a multitude of teaching styles. As students interact with the web-based, gamified lesson delivery platform, they can earn or lose virtual dollars while learning valuable soft skills such as such as responsibility, critical thinking, and work ethics, including financial literacy skills such as online banking, investing, using credit, and budgeting. This project will harness the game platform quality to capture students' attention and generate motivation to invest time and energy into learning. In the process, the game will collect valuable learning metrics and provide reports and analytics for teachers so they can learn more about their students. The goal of the proposed project is to develop the minimum viable product and test pilot its efficacy.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CLAUDIUS LEGAL INTELLIGENCE INC
SBIR Phase I: Artificial Intelligence Tool for Analysis of Legal Documents
Contact
309 TRINITY CT APT 11
Princeton, NJ 08540--7029
NSF Award
2112315 – SBIR Phase I
Award amount to date
$256,000
Start / end date
08/01/2021 – 12/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to establish an artificial intelligence (AI) system capable of providing data-driven insights for attorneys. The legal community currently lacks data analysis tools to help with civil case preparation, which can lead to suboptimal trial outcomes. The proposed technology can help lower costs through document analysis. The technology is designed to both automate and improve the decision-making process and enable attorneys to expand their case load, as well as enabling cost-effective representation.
This Small Business Innovation Research (SBIR) Phase I project will use federated learning techniques to train the technology’s algorithm across multiple decentralized databases without exchanging data samples, thus keeping information private and confidential. This approach overcomes the lack of access problem in applying AI to legal cases, without compromising data confidentiality. The proposed research will include two major objectives: 1) improve and verify the accuracy of the platform, and 2) create internal checks to ensure that the model does not propagate bias. Computational outputs will be assessed using data and records from randomly selected cases with known outcomes to demonstrate system accuracy; moreover, the model will explicitly account for potential sources of bias.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
COLOWRAP, LLC
SBIR Phase I: Novel, Non-Manual Solution for Mitigating Endoscope Looping in Riskier Colonoscopies
Contact
3333 DURHAM CHAPEL HILL BLVD STE A200
Durham, NC 27707--6238
NSF Award
2129569 – SBIR Phase I
Award amount to date
$255,801
Start / end date
08/15/2021 – 07/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a functional prototype for a novel abdominal compression device that prevents endoscope looping during colonoscopy, a common complication that causes pain, procedure failure, and increased risk of bowel perforation. Looping is typically countered with manual application of external pressure to the abdomen by nurses, which is inconsistently effective and can lead to staff injury. This project proposes a novel compression device that provides a safe and effective alternative to manual abdominal pressure to improve colonoscopy outcomes.
This Small Business Innovation Research (SBIR) Phase I project will address the technical challenges associated with engineering a colonoscopy compression device addressing the deficiencies of existing devices in patients with low body mass index (BMI) and low abdominal tissue volume. This project will accomplish this by first characterizing the differences in pressure applied by existing colonoscopy compression devices in high versus low BMI patients using pressure mapping. The results of this study will be utilized to design an adjustable and re-usable insert system that can be used during colonoscopy to apply different amounts of focused pressure. A prototype device will be produced and tested for localized pressure within a pressure range optimize to prevent looping in a low-BMI patient.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
COMMUNITY ENERGY LABS, LLC
SBIR Phase I: Smart Control Automation and Learning for Energy
Contact
401 NE 19TH AVE
Portland, OR 97232--4800
NSF Award
2221872 – SBIR Phase I
Award amount to date
$275,000
Start / end date
02/01/2023 – 12/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a commercial building management system that uses model predictive control for small to mid-sized commercial building owners to help them flexibly manage increasingly complex energy codes and prices. This technology uses machine learning to automate costly aspects of advanced building control, and eliminating complexity, frustration, and expense for leanly staffed building owners who are attempting to save money, meet code, reduce carbon footprint and adapt to rapidly changing energy prices. The proposed approach significantly reduces the setup time, the amount of training data, and the compute time needed for the technology to converge on accurate models and predictions using building thermal dynamics. These improvements reduce the controller costs without sacrificing accuracy. This technology will simplify the setup and implementation process for under-represented segments in the building automation, efficiency and model-based controls market starting with K-12 schools. This simplification has several distinct societal and environmental benefits including: increased energy and demand charge savings, increased energy efficiency, improved environmental footprint, increased job creation for building controls technicians, improved resiliency, and additional educational opportunities for K-12 families and communities.
This SBIR Phase I project develops a technology capable of building efficiency control. The innovation employs a hybrid approach based on constrained deep learning tools that build on physical knowledge of building systems and architecture, thereby making use of sampling data while producing physics-consistent accuracy in modeling and control predictions. Specifically, the project team hopes to converge on an architecture that can more reliably and accurately manage energy use and occupant comfort compared to state of the art control approaches. The project team also aims to demonstrate a significant reduction in heating, ventilation and air-conditioning (HVAC)-driven peak system demand in target buildings while keeping instrumentation, labor, and data costs per building to an affordable cost for the target market.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CONNECTED WISE LLC
SBIR Phase I: Autonomous Warning Triangle System (aWTS) for Emergency Stopping
Contact
3251 PROGRESS DR RM 138A
Orlando, FL 32826--2931
NSF Award
2222996 – SBIR Phase I
Award amount to date
$274,553
Start / end date
06/01/2023 – 11/30/2023 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to ensure the secure transfer of goods and/or passengers in commercial motor vehicles (CMVs) and prevent the likelihood of secondary incidents when a commercial motor vehicles (CMV) has made an emergency stop on a highway. All CMVs need to comply with traffic safety regulations and deploy emergency warning devices (e.g., safety triangles) during an emergency stop. Without the presence of a human driver, an automated CMV should also be able to automate emergency warning device placement. Proper placement of these warning devices can be life-saving for drivers of non-automated vehicles on a highway. The proposed project will increase the safety of external drivers by preventing any secondary incidents associated with the lane/shoulder blockage due to a CMV malfunction and protect drivers of non-automated and/or semi-automated vehicles by assisting in the deployment of emergency warning triangles on highways. This project aims to remove barriers to higher-order automated technology adoption due to lack of standardization.
The project will result in the design, research, and development of an affordable, after-market, reliable, and safe autonomous Warning Triangle System (aWTS) to ensure the safety of the automated and semi-automated commercial motor vehicles (CMVs) during an emergency stop without requiring human assistance. aWTS consists of three low-cost autonomous triangle reflector devices which are planned to optimally fit in a charging dock/enclosure where they will be safely stored during stand-by mode. When activated by the emergency signal transmitted from the CMV, the autonomous triangles are designed to move successively to their pre-determined destinations on the highway. The research and development activity during this Phase I project includes, but is not limited to: reviewing safety codes and regulations, investigating different highway scenarios and associated challenges, and performing computer simulations; developing proof-of-concept hardware that can demonstrate the proposed system’s technical feasibility and its integration to automated CMVs; determining the optimal placement and assembly of the autonomous reflective triangles by collaborating with auto manufacturers; investigating the potential cyber-security risks to develop secure communications between CMV and aWTS; and identifying the operational challenges and the design targets while considering the cost of deployment, lifecycle costs, functional use, and interoperability.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CRYSTAL SONIC, INC.
SBIR Phase I: Sonic Lift-Off (SLO) for Lower Cost Wide Bandgap Devices
Contact
311 W VIRGINIA AVE
Phoenix, AZ 85003--1020
NSF Award
2233368 – SBIR Phase I
Award amount to date
$275,000
Start / end date
05/01/2023 – 01/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to accelerate the emergence of next-generation semiconductors, beyond silicon, with increased performance and efficiency across a wide range of applications, including those essential for the Nation’s future energy and communications infrastructure. By replacing the most wasteful and costly step in the semiconductor manufacturing process, the proposed research will use sound (acoustic) energy to precisely cut semiconductor materials in a way that minimizes waste and enables materials reuse. This technology will dramatically decrease manufacturing costs and, as a result, lead to the accelerated development of faster, smaller, and more efficient devices.
This SBIR Phase I project will develop a new approach to lower the cost of advanced semiconductor manufacturing by using sound energy to lift off thin devices from their host substrates. The innovative approach taken in this project uses a technique to propagate a crack front at a precise depth below the device layer of a wafer with the application of acoustic pulses. This acoustic method wastes no material and, most importantly, makes it possible to reuse the substrate, which is currently wasted using standard approaches such as mechanical back grinding. As the single largest cost in manufacturing next-generation devices based on gallium nitride, the substrate material plays a key role in achieving higher performance and efficiencies compared to silicon devices. Making more efficient use of this material directly addresses a pressing need to find solutions that drive down manufacturing costs and accelerates the adoption of new semiconductor innovations ranging from power electronics to communications devices and beyond.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CULTURALLY RESPONSIVE SOLUTIONS, LLC
SBIR Phase I: Artificial Intelligence (AI)-powered platform for evaluating and developing cultural competence and diversity, equity and inclusion awareness
Contact
1 OAK KNOLL DRIVE
Wallingford, PA 19086--6315
NSF Award
2303937 – SBIR Phase I
Award amount to date
$274,880
Start / end date
07/01/2023 – 03/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project focusses on developing culturally-competent, humanlike,m and empathetic artificial intelligence (AI) agents for use within the pre-kindergarten (pre-K)-graduate school educational context. In the broadest sense, cultural competence refers to an organization's or individual's capacity and effectiveness in engaging with individuals from cultural backgrounds different from their own. The proposed innovation seeks to create a new market for social science-inspired AI agents, with significant roles for two Historically Black College or Universities (HBCUs) in the software development. The project’s impact on social advancement is crucial as diversity, equity, and inclusion (DEI) training is a high strategic priority for many organizations but often not part of the work environment. The success of the training program can lead to more equitable and inclusive workplaces, educational environments, and institutions, ultimately contributing to a more equitable and inclusive society. The project aims to increase user satisfaction and trust in AI technology, driving innovation and generating significant revenue streams by creating US jobs, annual revenue, and exports.
The traditional routes for developing cultural competence through human-led interventions can disrupt the work environment, be difficult to measure without human bias, and may lead to discord within the work environment. This SBIR Phase I project aims to develop culturally competent and empathetic Artificial intelligence (AI) agents to codify and label users' subjective responses to complex diversity, equity, and inclusion (DEI) issues while maintaining high social trustworthiness. Through the use of research-based social science frameworks, the project will develop bias-free and DEI-aware AI agents to assess and codify cultural competence among organizational stakeholders. Key objectives include sourcing large scale human-generated data to train AI agents to identify users' subjective responses to complex DEI observations and prompting via evocative vignettes or open-ended survey questions. Phase I aims to transform a proof-of-concept into a scalable platform with sophisticated AI capabilities for the educational marketplace, leading to more inclusive workplaces and a deeper understanding of pre-Kindergarten-20 environments.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
CURIEDX, LLC
SBIR Phase I: Development of a Machine Learning System to Identify Streptococcal Pharyngitis with a Smartphone Image
Contact
634 REGESTER AVE
Baltimore, MD 21212--1917
NSF Award
2304268 – SBIR Phase I
Award amount to date
$275,000
Start / end date
06/01/2023 – 05/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project addresses the lack of instant, remote medical tests for telehealth. This project could develop an accurate machine learning-based predictive model for strep throat. The business model delivers an artificial intelligence (AI)-based clinical decision support system as a Software as a Service subscription to urgent care telehealth services. The total addressable market for all telehealth point of care tests (beyond strep throat) in urgent care and primary care is $10.4 billion. This solution impacts antibiotic overprescribing and economics of health services. Currently, 34% of children and 75% of adults with pharyngitis receive unnecessary antibiotics, and this is 10-21% worse with telehealth. A remote point of care prediction for strep throat can potentially reduce the $22 million/year costs in unnecessary antibiotics and reduce drivers for drug-resistant bacteria. When pharyngitis is treated on telehealth it saves patients up to 1-3 hours per clinical visit and saves health insurance companies up to $100-400 per visit, compared to an emergency room or urgent care facility.
This Small Business Innovation Research (SBIR) Phase I project advances the field of machine learning by combining smartphone image analysis and deep learning. These strategies are applied to a novel use case in digital health as remote screening for clinical decision support. The technical challenge is the development of a predictive model to achieve sensitivity and specificity acceptable for clinical adoption, at a target of > 80% (similar to the rapid antigen strep test). The strategy to meet this challenge is to increase the size of the dataset and experiment with multiple prediction models until goal performance is achieved. The project will also include designing an authentication system that validates sufficient images as recorded by an untrained patient and creating an intuitive user interface that enables consistent recordings by patients.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
DCAN BIOSCIENCES LLC
SBIR Phase I: Advanced microfluidic systems enabling development of novel circulating tumor cell diagnostics
Contact
310 E 67TH STREET, SUITE 1-47
New York, NY 10065--6275
NSF Award
2234009 – SBIR Phase I
Award amount to date
$275,000
Start / end date
02/01/2023 – 01/31/2024 (Estimated)
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve the diagnosis, risk assessment, and monitoring of cancer. Nearly 40% of Americans will be diagnosed with cancer in their lifetime. Diagnosis of cancer in later stages dramatically reduces treatment options, leading to poor prognosis and low survival rates. In addition, the average cost of treatment for late-stage patients can be 3–5 times higher than that for early-stage patients due to the potential need for multiple rounds of expensive therapies. These multiple rounds of treatment contribute to the high economic burden of cancer. Thus, detecting cancer earlier will not only lead to improved patient outcomes but will likely reduce the overall costs of cancer treatment. Moreover, a minimally invasive and highly accurate diagnostic could be broadly administered to effectively identify those with various cancers, enhancing the commercial potential further.
This Small Business Innovation Research (SBIR) Phase I project seeks to develop an advanced microfluidic system for the isolation and assessment of circulating tumor cells (CTCs) and CTC clusters (CTCCs) for cancer diagnosis and monitoring. Microfluidic devices in various forms have been developed to isolate the extremely rare CTC population from billions of blood cells, but these technologies suffer from three major problems: 1) low sample purity, 2) low numbers of isolated CTCs/CTCCs, and 3) CTC/CTCC heterogeneity. To overcome these limitations, this project will develop an innovative device for the simultaneous isolation and assessment of single and clustered CTCs and their molecular signatures, enabling the implementation of new, highly sensitive and accurate liquid biopsies for cancer. The key objectives for this project are: 1) Design and develop a hybrid microfluidic system for simultaneous isolation and concentration of CTCs and CTCCs with high purity, 2) Develop and optimize two devices for single CTC and CTCC analysis, and 3) Testing and validation using prostate cancer patient specimens. This research will lead to the development of a new cancer diagnostics platform that is minimally invasive and more sensitive and accurate than current methods, expanding treatment options and improving patient outcomes.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
DRIVENDATA, INC.
SBIR Phase I: Open Machine Learning Competitions with Private Data
Contact
1644 PLATTE ST STE 400
Denver, CO 80204--4033
NSF Award
2038067 – SBIR Phase I
Award amount to date
$256,000
Start / end date
08/01/2021 – 09/30/2023
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to expand access to artificial intelligence (AI) talent and spur innovation to solve hard problems while protecting privacy. Machine learning and AI are bringing transformational change to governments, private companies, and social sector organizations. Yet in the coming years, innovation will be hamstrung by limited access to AI talent. Open innovation, such as machine learning (ML) competitions, provides governments and firms the ability to tap into a global talent pool to solve some of their most pressing and vexing challenges. Yet there is currently an immense barrier to running these competitions: the data must be made available to participants, which can preclude running a competition if the associated data are too sensitive to release due to concerns about privacy, security, or confidentiality. With data talent in increasingly high demand, government agencies, companies, and others have demonstrated a willingness to invest in this fashion. The proposed project develops a method to maintain data privacy at scale.
This Small Business Innovation Research (SBIR) Phase I project will develop an end-to-end competition system that provides privacy guarantees for data used to build crowdsourced algorithmic solutions. Open ML challenges typically work by providing participants with training data to learn underlying patterns, then evaluating resulting predictions on unlabeled test data. For many important problems, making training data available in this way violates concerns about privacy or enables abuse. The critical gap is preserving the privacy of training data while enabling participants to build models that can learn from it. This project will bring together recent advances in three of the most promising approaches in privacy-preserving data analysis: homomorphic encryption, federated learning, and differential privacy. Each technique will be developed and tested in a dedicated challenge structure with two core properties: 1) to preserve the privacy of sensitive data; and 2) to ensure competitors are able to get feedback on submitted models during the competition to inform algorithm improvements. Each competition system will result in a set of performance measures, including benchmarked algorithm performance and data privacy guarantees, to assess system feasibility.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
EARLY ALZHEIMER'S DIAGNOSTICS LLC
STTR Phase I: Saliva Screening Test for Alzheimer’s Disease
Contact
66 JEFFERSON RD
Glenmont, NY 12077--3318
NSF Award
2233317 – STTR Phase I
Award amount to date
$274,713
Start / end date
12/01/2022 – 11/30/2023 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project entails the development of a simple and affordable saliva-based test that will enable the detection of early Alzheimer's disease. Today, there is no single test that can determine if a person has Alzheimer's disease. Because the diagnosis is complex, invasive, and expensive, these tests are only performed once symptoms, such as memory loss, begin to manifest. These symptoms of Alzheimer's disease only become apparent once the disease has already cause significant damage to the brain. The proposed test will be widely accessible and detect the disease before symptoms arise, enabling the patient to start active prevention strategies and even therapies to preserve brain health. As the American population ages — nearly 1 in 4 Americans will be 65 years of age or older by 2060 — Alzheimer’s disease and other dementias are becoming a great challenge for health and social care. As part of a broad approach to prevention, this technology can potentially extend early Alzheimer’s care to millions of Americans, allowing interventions, monitoring, and treatment initiation years earlier than what is currently possible.
This Small Business Technology Transfer (STTR) Phase I project is applying sophisticated chemical analysis methods to develop a novel test to detect signs of early Alzheimer’s disease using a person’s saliva. Diseases can cause changes in the biochemical composition of body fluids such as blood or saliva. Using a modern, highly sensitive type of spectroscopy based on light scattering, and combining it with advanced statistical approaches (i.e., machine learning), this project is developing a method that can reliably detect biochemical changes in saliva specific to Alzheimer’s disease. This project aims to demonstrate the feasibility of the approach by showing its ability to distinguish saliva donated by healthy individuals from saliva collected from Alzheimer's patients at both mild and moderate stages of the disease. The project will also investigate whether known biomarkers for Alzheimer’s disease are being identified by this method and develop statistical approaches to interpret spectroscopic data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ECOTUNE, INC.
SBIR Phase I: Fully Bio-Based High-Performance Biomimetic Material for Sustainable Fabric
Contact
123 WHITE FLOWER
Irvine, CA 92603--0121
NSF Award
2233212 – SBIR Phase I
Award amount to date
$275,000
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to develop a sustainable, scalable, and high-performance alternative to natural leather fabric. Leather is one of the most widely used fabric materials in the world, with over two billion square yards produced through animal agriculture every year. The production of animal-derived leather emits greenhouse gases and pollution from the toxic chemicals used to process, tan, and dye animal hides. Current synthetic alternatives are made of non-renewable polymers such as polyurethane and polyvinyl chloride, contributing to petrochemical consumption and plastic pollution. This project aims to develop an alternative leather material that is 100% bio-based and environmentally friendly, and that meets industry requirements for mechanical, physical, and aesthetic properties. By engineering composite materials with superior performance and quality, this technology has the potential to reduce the environmental impact of leather-utilizing industries such as fashion apparel, footwear, furniture, and automotives.
This SBIR Phase I project proposes to use a biomimetic approach to developing high-performance materials that replicate the collagen microstructure and properties of natural leather. Current synthetic alternatives contain petroleum-derived binding or coating agents. This project aims to meet objectives to 1) develop novel compositional and processing methods to produce 100% bio-based crosslinked materials, 2) systematically characterize the mechanical, physical, and surface properties to evaluate performance features, and 3) demonstrate reproducibility and tunability in alignment with industry metrics. The proposed technology leverages innovation in chemical crosslinking to produce high-strength, ultra-durable, soft-to-the-touch materials for the next generation of sustainable fabrics.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
EDEN CONCEPTS LLC
SBIR Phase I: A Precision Autonomous Fluid Planting System for Pre-Germinated Vegetable, Flower and Specialty Crops
Contact
621 SUMMIT LAKE COURT
Knoxville, TN 37922--3152
NSF Award
2050274 – SBIR Phase I
Award amount to date
$256,000
Start / end date
07/01/2021 – 09/30/2023
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide economic benefits to farmers and consumers as well as broader societal benefits in the U.S. and globally for many different farm operations (e.g., vegetable, flower, and herb growers as well as greenhouse operations) and farm customers. Higher farm productivity directly benefits society by expanding the availability of nutritious, affordable food. Vegetables are vital U.S. crops in terms of food sources and farm profitability. One of the main challenges for vegetable farmers that limits productivity and total yield is the need to grow starter plants for the vegetables in greenhouses (or to purchase them) and then transplant them into tilled soil, which is highly labor-intensive and expensive. This system replaces that process with a system that plants pre-germinated seeds directly into the field with precision at a projected cost of 26% of the incumbent methods. The technology is an all-electric system that reduces the use of fossil fuels used in planting 100% and is 75% lighter weight than incumbent transplanting solutions using diesel tractors. The technology also addresses the increasing scarcity of farm labor with an autonomous planting system using automatic guidance and machine learning.
This Small Business Innovation Research (SBIR) Phase I project will work to solve the problem of separating pre-germinated seeds in a batch of thixotropic gel such that one and only one pre-germinated seed is planted at each desired location in real-time under actual field planting conditions. Plant scientists have proven in numerous scientific studies that planting pre-germinated seeds increases production and lowers costs. Reliable field planters have not been successful, however. The primary research objective is to prove that an autonomous seed-singulation system combining flow controls, seed detection, active machine learning, singulation, and extrusion in real-time can meet the requirements of the field to prove commercial viability. The project research will begin by using discrete event simulation to model the singulation process used to obtain optimal parameters for the system. The model’s output will be used to develop control algorithms and programs. A prototype autonomous system will be developed to test the algorithms in actual field conditions. The anticipated result of this research is that the system can reliably plant one and only one seed at each desired location at least 90% of the time.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
EDNA BIOTECH, INC
SBIR Phase I: Point-of-Care Diagnostic Tool for Identifying Extended Spectrum β-Lactamase E. Coli in Urinary Tract Infection
Contact
2265 E FOOTHILL BLVD
Pasadena, CA 91107--3658
NSF Award
2233653 – SBIR Phase I
Award amount to date
$274,975
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project lies in demonstrating the commercial viability and clinical benefit of a point-of-care diagnostics device based on novel technologies for patients with urinary tract infections. There are 10 million primary care visits for urinary tract infections per year in the United States. More than 15% of these patients suffer antibiotic-resistant infections. Clinical studies have shown that accurate identification and early diagnosis of the antibiotic resistance of the infecting bacteria shorten the treatment period, eliminate antibiotic misuse, and reduce average patient costs. However, on-site diagnostic testing that returns molecular information about the bacterial identity and antibiotic resistance remains a significant challenge. Current laboratory-based tests are costly, have long turnaround times, and require skilled technicians and well-equipped laboratories. In most cases, this results in empirical antibiotic prescriptions before the information is returned from test results. Developing rapid, on-site, easy-to-implement, and frequently used antimicrobial resistance screening is essential to combat the ongoing threat of antimicrobial resistance and antibiotic misuse. This device will be suitable for outpatient clinics, home healthcare, and inpatient facilities. It will enable a shift from empirical to evidence-based treatment of bacterial infections.
This Small Business Innovation Research (SBIR) Phase I project will develop a point-of-care device for the on-site identification of E. coli and associated extended-spectrum β-lactamase (ESBL) antibiotic resistance genes in urinary tract infections from unprocessed urine samples. The device uses novel, patent-pending electrochemical nucleic acid sensing techniques and a loop-mediated isothermal amplification assay to semi-quantitatively measure target bacterial genes. The device consists of a disposable microfluidic cartridge and a portable multifunctional reader. The self-contained microfluidic sensing cartridge supports sample preparation, isothermal amplification, and sequence-specific electrochemical detection. This novel combination of sensing technology and highly integrated sample handling microfluidics will enable the development of critically needed point-of-care diagnostic solutions. The project will demonstrate the specificity of bacterial detection, the ability to identify multiple genotypes, the ease of device operation, and the portability of this point-of-care device.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ELEKTRODA, LLC
SBIR Phase I: Low-Cost Bipolar Plate for a Proton-Exchange Membrane Fuel Cell
Contact
6901 LYNN WAY
Pittsburgh, PA 15208--2438
NSF Award
2208556 – SBIR Phase I
Award amount to date
$253,773
Start / end date
03/01/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project addresses the high cost of proton-exchange membrane (PEM) fuel cells. As the world transitions from internal combustion engine vehicles to electric vehicles, the market for fuel cells is expected to increase substantially. Fuel cell electric vehicles (FCEVs) have a number of advantages, such as high durability, long range, and fast refueling over battery electric vehicles (BEVs). Additionally, fuel cells are less susceptible to the supply chain issues that lithium-ion batteries are beginning to suffer from. However, FCEVs are currently at a significant disadvantage to BEVs in terms of cost. Reducing the cost of the fuel cell stack will help to make FCEVs cost competitive with BEVs and will accelerate their adoption into the marketplace.
This SBIR Phase I project proposes to reduce the cost of PEM fuel cells by reducing the cost of the bipolar plate component. Bipolar plates currently account for approximately 30% of the full fuel cell stack cost. Current bipolar plates are made from either metal foils or molded carbon composites. Metal plates can be produced by high-speed, low-cost forming techniques, but must be made from expensive, corrosion resistant materials. Carbon bipolar plates are made from inexpensive precursors but are manufactured by processes which scale poorly. This project aims to demonstrate that a bipolar plate can be produced from a novel, low-cost, carbon-based sheet utilizing forming techniques analogous to those used for producing metallic bipolar plates. The effort focuses on optimizing the carbon sheet for low hydrogen permeability, demonstrating that flow channels can be stamped into the sheets, and quantifying functionality and durability by testing in small scale fuel cells.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
EMPALLO, INC.
SBIR Phase I: Developing Artificial intelligence Models to Predict In-hospital Clinical Trajectories for Heart Failure Patients
Contact
809 PEACHTREE BATTLE AVENUE NW
Atlanta, GA 30327--1313
NSF Award
2304358 – SBIR Phase I
Award amount to date
$275,000
Start / end date
09/01/2023 – 02/29/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project includes improving cardiovascular management, personalized medicine, inclusivity for historically underserved populations, and clinical trial design. The project could improve the health and wellbeing of heart failure (HF) patients while saving billions of dollars in HF hospitalization costs. If the technology proves feasible, it could shift the paradigm of HF management from reactive to proactive. The proposed machine learning model extracts latent features and detects subtle patterns from clinical data, which derives digital biomarkers that can potentially enable novel phenotype discovery and eventually personalized medicine. The digital biomarkers derived from the proposed innovation, when used in clinical trials, could also improve inclusivity and greater generalizability of novel therapies when applied to diverse populations. The proposed technology could enable clinical trial sponsors to achieve the desired statistical power with smaller patient populations. This, in turn, would enable faster, cheaper, and more effective clinical trials.
This Small Business Innovation Research (SBIR) Phase I project mitigates the burden of heart failure (HF), which afflicts over 6.5 million Americans. As the leading cause of hospitalization in the U.S., HF results in more than $29 billion in hospital charges and $11 billion in hospitalization costs, annually. A large portion of hospitalization costs are driven by readmissions, with about 20% of heart failure patients readmitted within 30 days of discharge. The fundamental challenge is the variability of this disease. A treatment regimen that works for one patient might not work for another, even if they show similar symptoms. Anticipating clinical trajectories, treatment response, and potential complications, and translating those insights into actionable interventions is key to improving outcomes for HF patients. To help clinicians anticipate a HF patient’s response to treatment and adverse events during hospitalization and enable personalized intervention planning, this project will develop explainable and generalizable multimodal artificial intelligence (AI) models that predict a HF patient’s clinical trajectory shortly after admission. This technology is a methodological innovation grounded in large-scale, multi-center, clinical data. The key milestone in Phase I is to yield a reasonably accurate predictive AI model, cross-validated between the data of two large healthcare systems.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
EMPIRI, INC.
SBIR Phase I: A cancer diagnostic instrument to measure empirical treatment response
Contact
7505 FANNIN ST.
Houston, TX 77054--1953
NSF Award
2322382 – SBIR Phase I
Award amount to date
$274,930
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve personalized cancer care by developing a first in class cancer diagnostic instrument that can deliver clinically actionable, personalized, drug response data from individual cancer patients using the E-slice assay. The E-slice assay is a novel 3D culture-based assay which has been shown to accurately predict individual cancer patients’ responses to treatments. The E-slice assay is currently registered as a Clinical Laboratory Improvement Amendments Laboratory Developed Tests (CLIA LDT). The cancer diagnostic market is expected to grow from $56 billion in 2022 to $162 billion by 2027, and this test and automation could capture a significant share of this rapidly expanding market. Beyond improving outcomes for cancer patients, the automation of this assay could have far-reaching implications, such as accelerating and economizing drug screening, discovery, and development for pharmaceutical and biotechnology companies, and academia.
This Small Business Innovation Research (SBIR) Phase I project addresses the most challenging and risky portion of automating the E-slice assay. The novel engineering solutions that the team proposes to develop will automate the processing of live human tissue samples from a needle biopsy or surgery, generate precision-cut slices, and then precisely position them in a tissue culture plate for downstream culture and analysis. The new device will do so in a manner that maintains sterility, minimizes thermal, chemical, and mechanical stresses, and performs in a highly reliable way. The primary technical challenges are ensuring reliable performance that is equal to or superior than manual methods. The technical milestones include meeting thresholds for reliability, sterility, and tissue viability compared to manual processing.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
EMPRISE CONCEPTS LLC
STTR Phase I: Underground Live - an innovative, advanced analytical tool for characterizing the subsurface and reducing underground construction risk
Contact
31553 SNOWSHOE RD
Evergreen, CO 80439--7651
NSF Award
2304544 – STTR Phase I
Award amount to date
$275,000
Start / end date
05/15/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to reduce the cost of underground civil infrastructure construction and make new infrastructure more sustainable by developing a computational software-based tool to create and update ground models in real time using data-driven advanced analytics. Civil infrastructure is increasingly moving underground, including roadways, transit, utilities, and facilities. However, risk in underground construction remains a critical barrier to attracting investment. A significant risk in building underground is the high uncertainty in ground conditions and physical properties influencing design and construction, resulting in increased costs due to over-design and/or delays and failures during construction. This project strives to improve the understanding of ground conditions by providing a solution to update the ground models during construction in a routine and autonomous manner, making full use of the wealth of data collected during construction.
This Small Business Technology Transfer (STTR) Phase I project aims to develop a technical solution that automates the process of back-analyzing ground properties and updating ground models in real time. Several technical challenges will be addressed. Current backanalyses practice is extraordinarily labor-intensive and expensive in managing and integrating data from underground infrastructure projects. The dynamic environment during construction requires 4D inversion analysis on potentially hundreds of unique tunnel-structure interactions. Furthermore, the efficacy of the inversion in estimating geotechnical parameters, which has been demonstrated for only limited situations during the fundamental development of the techniques, needs to be validated. The goals of the proposed research are to (1) develop algorithms to automatically integrate data from geotechnical instrumentation and monitoring, construction process monitoring, existing infrastructure, apriori geostatistical model, etc., (2) develop algorithms to dynamically update the geotechnical parameter inversion in spatial proximity of tunnel construction to adjacent structures, sensors, and ground conditions, (3) characterize the geotechnical parameter inversion efficacy across a broad variety of ground conditions and tunneling-structure interactions, and (4) learn the influence of sensing layout and optimized sensing on inversion efficacy. This Phase I work will lay the foundation for the development of a ‘live’ ground modelling tool.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ENCOORD INC
SBIR Phase I: A hybrid phasor/waveform simulation tool for the accurate and efficient simulation of large electric power systems with high shares of inverter-based resources
Contact
1525 RALEIGH ST
Denver, CO 80204--1594
NSF Award
2321329 – SBIR Phase I
Award amount to date
$274,375
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to develop refined approaches to power system dynamic stability assessments, enabling the efficient, mass integration of renewable energy into systems worldwide. Decarbonization goals and economic opportunity necessitate the increase of inverter-based resources, such as solar, wind, and battery energy storage. A dynamic stability assessment is required before the interconnection of every renewable, inverter-based resource on all power systems. Current simulation approaches do not capture the critical details of inverter operation or are too computationally complex and expensive to be effective with real-world systems. This results in the enormous potential for unique simulation capabilities that streamline this process. There is a global market opportunity for more effective and efficient planning solutions that enable power system operators to meet this need. In the United States, alone, the licensing opportunity for a solution is hundreds of millions of dollars. The proposed hybrid approach combines computational flexibility with
accuracy. This solution will leverage the maturity of these approaches and eliminate their weaknesses. The final solution will yield an invaluable, novel simulation tool for power system operators and planners navigating the challenges of the energy transition.
The intellectual merit of this project results from the development of mathematical methods that will comprise the foundation of this hybrid power system dynamics simulation tool. Existing tools have clear weaknesses. For example, reduced-order, phasor domain simulation approaches do not capture the critical aspects of inverter operation. Detailed waveform domain approaches are sufficient to capture relevant dynamics but are too computationally expensive to be effective with real-world systems. These domains are mature, but separately they do not meet the changing need. Hybridizing them in a single platform is a solution, but it requires research in the following three foundational pillars of the proposed tool: 1) autonomous boundary determination – identifying the spatial (across the network) and temporal (across the simulation length) boundary that partitions the two simulation domains; 2) intra-simulation model order adjustment – applying dynamical model granularity for all simulations, but singularly perturbing the differential systems to create algebraic relations and reduce computational burden when substantial detail is not required; and 3) seamless simulation mode switching – identifying criteria necessary for switching between domains. With the successful completion of this SBIR Phase I project, the viability of the hybrid approach will be confirmed, and a roadmap for implementation will be realized.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ENERGAO, INC.
SBIR Phase I: Development of Fire-Safe and Low-Cost Flow Batteries using New Membranes for Long-Duration Energy Storage
Contact
754 STONE CREEK DR
Makanda, IL 62958--2748
NSF Award
2212748 – SBIR Phase I
Award amount to date
$256,000
Start / end date
03/01/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is derived from providing energy self-sufficiency to residential buildings. Fire-safe and low-cost redox flow batteries, combined with rooftop solar panels or backyard wind turbines, can meet the increased electricity needs of American families without relying on the national grid. During the daytime, solar arrays convert solar energy to chemical energy in batteries. During evenings, batteries will power houses and charge electric vehicles in family garages. Batteries play a critical role in harvesting and storing clean electricity for residential uses. Compared with lithium-ion batteries, the improved fire-safety profiles and reduced production costs of flow batteries will give homeowners peace of mind and allow the wider adoption of clean energy by society.
This SBIR phase I project proposes to develop novel proton-conductive polymeric membranes that show reduced metal electrolyte crossovers and build the first 7 kW flow battery system that meets the energy need of a single-family house. To this end, the project will synthesize a group of phosphorylated polybenzimidazoles (PBIs) that have a phosphoric acid side chain. Such phosphoric acid groups can form unique zirconium phosphonate clusters that transport protons but deny the unwanted migration of metal cations. PBIs provide mechanical support to the zirconium-phosphate clusters. Zirconium and PBIs are bulk materials that cost significantly less than commercial perfluorosulfonic acids. The second task of this phase I project is to construct a 7 kW iron-titanium redox flow battery system. The peak electricity demand for the average American single-family house is around 7 kW. Such a battery will be connected to a rooftop solar system to meet residential electricity demands. The team plans to study various cell configuration designs. In particular, the project will focus on examining the effects of solvent channels of bipolar plates on the performance of the stack. In addition, other design parameters, such as pump flow rates and the choice of sealant materials, will be thoroughly investigated.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ENVONICS LLC
SBIR Phase I: A real-time precision nutrient analysis and management system for hydroponic farming operations
Contact
13499 BISCAYNE BLVD STE 709
North Miami, FL 33181--2027
NSF Award
2210046 – SBIR Phase I
Award amount to date
$256,000
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to promote the viability and sustainability of small-to-medium indoor, urban, and controlled environment agriculture (CEA) farms. As the global population grows to 10 billion by 2050, the agriculture industry will need to produce 70% more food using only 5% more land. Indoor farming can make a significant contribution to meet this demand sustainably. Indoor farmers are also seasonally and geographically independent, which means they can help meet demands for locally produced fresh foods and are protected from extreme weather events. These farms primarily use soilless growing methods, such as hydroponics, that currently suffer from critical needs for efficient and affordable methods to monitor and manage nutrients and water in order to be financially viable and environmentally sustainable. The proposed project provides an innovative solution for nutrient management in hydroponic farming, thereby lowering the costs, increasing the yield potential, and supporting the viability of such farms. By supporting the expansion of the national hydroponics industry, this project will increase the local production of and expand access to fresh produce.
This SBIR Phase I project will develop a nutrient management system to provide CEA farmers with real-time information about the nutrients in the growth solution of their crops. The proposed solution will utilize ion-selective electrode (ISE) technology and a decision support system powered by machine learning (ML). This project will focus on the critically needed engineering and data analytics research and development to de-risk major technical challenges in the development of the nutrient management system, providing proof-of-feasibility. The key objectives of this project are to: 1) design a special chamber for the sensors to minimize the interference and increase accuracy, 2) validate the feasibility and accuracy of this new design in a greenhouse setting, 3) develop a predictive algorithm to automatically calibrate the sensors, and 4) measure and predict deficiencies in leafy greens production: collecting empirical evidence of nutrient deficiency to train ML models to identify, and ultimately, predict a deficiency prior to when it is observable.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
EPIXEGO INC.
SBIR Phase I: A novel method to scaling mentoring and career development in Institutes of Higher Education
Contact
146 CELADA CT
Fremont, CA 94539--3011
NSF Award
2232502 – SBIR Phase I
Award amount to date
$275,000
Start / end date
04/01/2023 – 09/30/2023
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to increase post-secondary student success via academic and career guidance. A large body of research on career navigation has studied how post-secondary education, career readiness (understanding viable career paths at graduation), and its interconnectedness are important for a growing number of first-generation, low-income, and underrepresented students. With increasing undergraduate degree program offerings in response to an evolving future of work and student-to-counselor ratios of 1: 1,800 in public colleges, career guidance, and academic navigation risk being unavailable. As a result, during the pandemic, Institutes of Higher Education (IHEs) that serve students of color and students from low-income backgrounds saw declines in enrollment that far outpaced their predominantly White peer institutions. The proposed platform intends to increase the visibility, accessibility, and discoverability of competencies to potential career and academic paths for students at IHEs. The platform envisions doing this via near-peer role models who are similar in their dimensions of self-efficacy. With more than 85 million jobs that could go unfilled by 2030, the proposed platform may help alleviate part of that shortage by widening the talent aperture.
The intellectual merit of this project is in the company’s patented technology of a unified, multi-dimensional, data representation model that creates a ‘competency fingerprint’ for each user. The data representation method enables better machine learning models to ‘infer’ competency from unstructured data of a student’s traditional and non-traditional learning experience, rather than degrees, majors, grade point averages (GPAs), or test scores. The platform uses a consistent, scalable, competency nomenclature for hard and soft skills gained via traditional academic and outside-of-the-classroom experiences to discover academic-career paths where the students’ learning competencies may be in demand. There is a significant technical challenge in adopting this technology for the inter/cross-disciplinary jobs of the future: such a platform requires a robust, larger data set to evaluate the relevance of matching, in a discipline-agnostic context. Reduction of this variability is the key technical risk to be overcome by the proposed research and development.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ESTE(TM) LEVERAGE, INC.
SBIR Phase I: Artificial Intelligence (AI)-enabled Personalized Employability Curriculum (APEC)
Contact
1431 S WESTGATE AVE
Los Angeles, CA 90025--2244
NSF Award
2230864 – SBIR Phase I
Award amount to date
$274,299
Start / end date
05/01/2023 – 01/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this NSF Small Business Innovation Research (SBIR) Phase I project begins with an online self-assessment by middle-school girls to identify their innate interests within the fields of entrepreneurship, science, technology, or engineering. Current U.S. trends show a high attrition of girls with interests in these fields, beginning at the middle school level. There is a subsequent drop-off over the ensuing academic years, and this results in small numbers of women occupying these types of roles in their adult careers. The assessment analysis and personalized roadmap will help clarify, support, and nurture the individual’s journey in their growth and development towards their career choices including careers in STEM and entrepreneurship. Ongoing refinement and enhancement of the assessment tool will help inform needed changes to the educational curriculum and/or shifts in societal thinking to help close the ongoing gap in women occupying highly skilled roles. The potential commercial and socioeconomic impact of the assessment and follow-on resources defines a marketable product with associated workforce that spans across the family, academic, governmental, and societal institutions.
The technical innovation in this project is a unique framework assessing innate interest in the fields of entrepreneurship, science, technology, or engineering and leveraging these data to create a personalized artificial intelligence (AI)-driven career exploration, skills development, and employability curriculum. The goal is to confirm that the use of deep learning to provide these girls with a dynamic career exploration roadmap can successfully counter the common societal forces that negatively impact their pursuit of innate interests and development of the skills necessary for careers as entrepreneurs, scientists, technologists, and engineers. It is hypothesized that early identification of these innate interests preempts identity stereotypes. To combat confirmation bias that girls aren’t good at the fundamental skills needed for these careers, machine learning and AI-enabled data aggregation is used to correlate these innate traits with resources that foster associated job skills, offer opportunities and challenges that are suitable to the user, and provide opportunities to connect with successful role models to address the lack of representation of women in these areas. The initial scope of the project will target middle school girls and their parents/guardians with expansion to the broader audiences of teachers, mentors, coaches, and society in general.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
EXOPOWER INC.
SBIR Phase I: In-Motion, Capacitive, Wireless Charging System for Material Handling Vehicles
Contact
2514 LAKE MEADOW DR
Lafayette, CO 80026--9162
NSF Award
2228928 – SBIR Phase I
Award amount to date
$251,522
Start / end date
08/15/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader and commercial impacts of this Small Business Innovation Research (SBIR) Phase I project will eliminate the downtime for battery charging of material handling vehicles (MHVs) (i.e., mobile robots and forklifts) with in-motion capacitive wireless charging, thereby increasing the productivity and economic competitiveness of warehouses. Implementation of in-motion capacitive wireless charging in warehouses and roadways would enable the use of much smaller batteries (up to 80% smaller) for most electric vehicles (EVs) and MHVs, dramatically reducing costs and making them less expensive than their gasoline powered counterparts. This dramatic price reduction will speed up the transition from internal combustion engines to electric vehicles. Mass roadway deployment of in-motion capacitive wireless charging would significantly reduce air pollution and the US dependence on oil, increasing US national security.
This Small Business Innovation Research (SBIR) Phase I project will develop a capacitive wireless charging system for in-motion charging of MHVs. The project will develop a robust, safe, and fully automated system capable of wirelessly charging an MHV on demand, at power levels up to 1 kW. The research will address three technical challenges: 1) achieving high power transfer in the presence of coupler misalignments; 2) activating and deactivating the charging apparatus in a seamless and safe manner; and 3) maintaining thermally stable continuous operation and achieving efficient rectification at 1 kW, 6.78 MHz with commercially available power semiconductor devices. The misalignment tolerance will be enabled through an enhanced coupler design that ensures full power delivery over a substantially enlarged area of overlap compared to conventional coupler designs. The automated activation and deactivation will be enabled by a sensing, control, and communication system comprising a modulated optical actuation scheme and power-transfer based decision making. The continuous power delivery will be enabled by a custom-designed thermal management solution. The efficient rectification at high frequency will be enabled by innovations in matching network design that mitigate the impact of the rectifier’s parasitics. Addressing these challenges will enable the technology to be commercializable.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
Earth Mapping International, Inc.
SBIR Phase I: Dynamic OneSource Geospatial Information System for Maximizing Agricultural Yields
Contact
1365 COMMERCIAL CT
Norcross, GA 30093--3857
NSF Award
2313340 – SBIR Phase I
Award amount to date
$274,979
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is the creation of a dynamic geospatial database that can be mined to further develop precision models of soil moisture and farm productivity that will aid small- to medium-sized farms. These farms cover almost 75% of the operating farmland in the U.S. Large companies in the agriculture industry benefit from the collection of digital farm data however, smaller farms are disadvantaged by the inaccessibility of agricultural digital innovation systems. The availability of timely digital information as inputs to field conditions will help small- to mid-sized farmers optimize their yields and potentially generate greater revenue. The proposed database will integrate multiscale and multivariate imaging and non-imaging geospatial and meteorological data into a single source. The merging of satellite-based geospatial data with airborne-geospatial data, a challenging task, will improve the accuracy of earth science data. Potential applications of the technology include medium- to long-term food security planning, drought mitigation, soil conservation, diversification, and expansion of climate-resilient and sustainable farming.
This SBIR Phase I project focuses on developing an integrated, one-source, dynamic geospatial database as well as precision soil moisture and farm productivity forecasting models to benefit small- and medium-size farms. The data inputs include (i) global navigation satellite system observations on a newly designed geodetic/meteorological network for significantly increased accuracy; (ii) airborne digital geospatial data from a fixed-wing platform to capture high precision and resolution nadir multispectral and color oblique imageries, lidar point cloud data, and data from un-manned aerial systems to capture seasonal hyperspectral imageries; (iii) satellite remote sensing data from commercial satellite high-resolution imageries; (iv) weather satellite data for hourly global precipitation measurements (GPM) fused with multi-satellite retrievals for global precipitation measurement mission (GPM); and (v) terrestrial data from existing geographic information systems and meteorological stations.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FARMSENSE INC.
SBIR Phase I: Automatic, Digital Classification and Counting of Mosquitos to Allow More Effective Vector Control
Contact
2025 CHICAGO AVE
Riverside, CA 92507--2201
NSF Award
2233676 – SBIR Phase I
Award amount to date
$275,000
Start / end date
08/01/2023 – 05/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the creation of an end-to-end platform for digital mosquito surveillance that can support the vital work of vector control districts. Effective vector control is essential to reducing the spread of diseases including West Nile, Eastern Equine Encephalitis and Zika. Currently, mosquito surveillance is typically done using mechanical traps, which require significant labor to survive. The project will significantly improve the quality and ease of insect surveillance, thus allowing more effective mosquito control. This effort will improve mosquito suppression efforts, while reducing labor costs and the volume of pesticides that must be used. Reducing the volume of pesticides has further positive benefits to society at large: it will reduce pollution and colony collapse disorder in beneficial bees. Beyond area-wide surveillance, the hardware/ algorithms/ representations/ data-models created in this project will be useful to scientists that study mosquito-vectored diseases. For example, the solutions can be used to measure the effectiveness of a new attractant or repellent.
This Small Business Innovation Research (SBIR) Phase I project will investigate techniques to improve state-of-the-art mosquito classification and counting, with the goal of producing a platform that allows inexpensive, real-time, insect surveillance to support mosquito suppression efforts. Although digital sensors have the potential to remove the burden of manually counting the insects, currently the vector control technicians must still visit the traps frequently to change the carbon dioxide (CO2) gas cylinders (the lure) and the batteries. The reason why both CO2 and batteries deplete so rapidly is because they are left on all day. Because the team is sensing insects in real time, they have the unique ability to actuate the gas cylinders and fan/light to sample the distribution of insect arrivals. The team can also optimize the trade-off between conserving resources and the precision of measurement of mosquito density.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FC RENEW, LLC
SBIR Phase I: Renewable platinum catalyst for fuel cell applications
Contact
710 S FAYETTE ST APT 31
Alexandria, VA 22314--3952
NSF Award
2229006 – SBIR Phase I
Award amount to date
$275,000
Start / end date
03/15/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to overcome a limiting step in creating viable hydrogen fuel cells for the automotive industry. The solution will renew the battery catalyst without its removal from the vehicle. This will result in significant savings toward the total cost of ownership and an increase in overall system reliability, one of the top features potential customers evaluate when making automotive purchasing decisions. The transportation industry has been seeking innovations to transition to sustainable solutions such as zero-emission and green hydrogen fuel cell technologies, but the broad adoption of such vehicles is limited by the short lifespan of fuel cell electrocatalysts that operate, expensive fuel cell stack replacements, and the high costs of components. This project will make hydrogen fuel cell vehicles viable and cost-competitive with diesel and gasoline engine vehicles by extending the lifetime of the electrocatalyst and thereby the fuel cell stack. Such alternative options are needed, as diesel vehicles are responsible for around 20% of anthropogenic pollution precursor emissions, and these emissions are linked to approximately 110,000 premature deaths per year.
This SBIR Phase I project proposes to establish a proof-of-concept approach for in-stack platinum electrocatalyst renewal. Since fuel cell electrocatalysts degrade during operation, this project will develop a breakthrough technology that enables the reuse of what was once considered expended, end-of-life fuel cells by renewing the electrocatalyst. This renewal process can be conducted multiple times after the electrocatalyst inevitably degrades, increasing the lifetime and durability for fuel cell operation. As one of the most expensive precious metals, platinum typically used as the catalyst for automotive applications contributes to about 60% of the total fuel cell cost. This process is the first-in-kind to allow electrocatalyst renewal at the surface of the electrode without removing or replacing the fuel cell stack. This project will establish working parameters for the electrocatalyst renewal and analyze the effects of the process using a custom testing apparatus with a 3-electrode configuration and on commercial fuel cell membrane electrode assemblies in custom single-cell configurations. This team will utilize several techniques including analytical electrochemistry, microscopy, and electron paramagnetic resonance techniques to verify the effects of the process.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FENIX SPACE, INC.
SBIR Phase I: Novel Reusable Launch Platform: Two-Body Separation Under Unique Aerodynamic Circumstances
Contact
294 S LELAND NORTON WAY STE 3
San Bernardino, CA 92408--0131
NSF Award
2233168 – SBIR Phase I
Award amount to date
$274,996
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact and commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in its positive influence on both the growth of the Low Earth Orbit economy and stimulation of innovation in space technology. The growing demand for satellite launches is currently limited by a bottleneck of low availability, flexibility, and high cost of existing orbital launch services. Orbital delivery services enabled by this advanced in-flight separation system will enable a new level of launch responsiveness leading to lower costs, much greater contractual flexibility, and the availability of daily launches without the need for costly launch infrastructure, greatly accelerating time-to-market for satellite service providers and increasing their profitability. Low cost and frequent access to Low Earth Orbit will enable ubiquitous internet access, 5G capabilities, and valuable Earth observation technologies. The flexibility and reduced cost of these launch services will help maintain the United States' position at the forefront of space technology development and space research. The technology will provide the US Armed Forces access to a responsive, secure, flexible, and available gateway to space that will boost reconnaissance, observation, communication, and intelligence capabilities. Moreover, it will represent the most ecofriendly launch delivery service available, able to reduce carbon dioxide emissions by three times (3x) compared to ground launch.
This SBIR Phase I project seeks to demonstrate an innovative concept of in-flight aircraft/rocket separation in which the rocket is launched from the top of the carrier aircraft, instead of the widely used launch from beneath. This new separation system is the core innovation enabling an advanced air-launched orbital delivery system that will dramatically reduce the cost of dedicated satellite launch, minimize propulsion and structural requirements, and enable orbital delivery flexibility and precision, while significantly reducing the carbon footprint of space launch operations. The proposed concept will be the first top-carry air-launch service commercially available. The goals of the Phase I project are focused on building a prototype of the separation system and validating it in a sub-orbital test flight. The main technical challenge is to ensure that the design of the improved separation system will work under a broad range of real flight conditions. This technology will be achieved by research leading to a better understanding of aerodynamic behavior at the separation event and the development of an improved design methodology that considers all relevant design parameters and their aerodynamic effects.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FERMI ENERGY, INC.
STTR Phase I: Manufacturing nickel and cobalt-free cathodes for high-energy and low-cost lithium-ion batteries
Contact
2200 KRAFT DR
Blacksburg, VA 24060--6704
NSF Award
2233272 – STTR Phase I
Award amount to date
$275,000
Start / end date
03/01/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is to facilitate the adoption of battery electric vehicles in the US by securing the supply chain, reducing battery cathode cost, and enhancing US-innovated battery manufacturing. This project will address the several challenges facing the US battery industry. First, the state-of-the-art lithium-ion battery cathode materials use scarce and expensive elements, such as cobalt and nickel. Second, the US battery manufacturing capability needs to be improved in order to meet the rapidly growing demand, cathode materials production is especially important. Third, current lithium-ion battery cathode manufacturing involves costly liquid and gaseous waste management. The proposed technology will create fundamentally new ways to produce next-generation cathodes for American electric vehicles. This project can significantly impact the battery field since the proposed new cathode technology is expected to result in a major cost reduction per electric vehicle battery pack. Advances in novel cathode chemistries and manufacturing processes offer new opportunities for the US to establish the leadership in cathode innovation and manufacturing.
This project develops a fundamentally disruptive technology to enable the use of low-cost, cobalt- and nickel-free oxide cathodes in high-energy lithium-ion batteries. The dry manufacturing technology will be uniquely combined with new materials development to enable stable battery cycling with a 700 Wh/kg specific energy at the cathode materials level. The technology is compatible with mainstream lithium-ion electrolytes and anodes, which makes full-cell integration feasible and practical at the commercial scale. The research and development objectives include: (1) design, manufacturing, and characterization of new cobalt- and nickel-free oxide cathode chemistries with abundant and low-cost elements, (2) develop cathode electrodes with controllable physical properties based on an all-dry electrode preparation process, and (3) integrate the graphite anode and electrochemical measurements under various practical testing conditions. This project proposes to combine materials synthesis and electrode powder mixing and to avoid the costly materials storage and handling between cathode powder production and cathode electrode manufacturing. The successful development of the technology will enable low-cost, high-energy, dry-processed, and US-manufactured battery cathodes for more affordable and reliable electric vehicle batteries.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FIRST WATER TECHNOLOGY CO.
SBIR Phase I: Compact, low-maintenance water treatment plant
Contact
5321 S CHARITON AVE
Los Angeles, CA 90056--1354
NSF Award
2212882 – SBIR Phase I
Award amount to date
$255,849
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the development of a low footprint drinking water treatment plant (WTP) suited to facilitate much-needed upgrades to the nation’s water infrastructure. Today’s drinking water treatment systems are suffering from high volumes of generated waste (~100,000 tons sludge per typical plant per year) and high maintenance requirements and costs (4x/year week-long clarifier decommissioning for cleaning). The aging infrastructure of many of the nation’s water treatment plants has left millions of Americans with inadequate drinking water. This situation, paired with the aging and shrinking water operator workforce, translates into a growing threat of water supply interruptions and noncompliance. Owing to its small footprint and low energy and maintenance demands, the proposed technology would deliver an easily implementable solution that could be adopted by communities and water systems around the world, even in remote areas. Successfully developed, the WTP will offer a unique economic opportunity to meet critical global sustainable development goals and promote human health and welfare. This technology will lower the barriers to upgrading the nation’s water infrastructure, creating jobs through the introduction of long-delayed upgrades through plant implementation.
Elements of the innovation under development for the proposed water treatment plant include a self-modulating feedback/feedforward controller driving automated coagulant dosing, which is paired with a high-efficiency hydraulic flocculator that leverages turbulent flow to promote floc formation while remaining free of failure-prone moving parts; a self-cleaning clarifier fitted with settling plates and a sludge blanket system for efficient contaminant removal; a stacked sand filter that decreases the hydraulic loading rate for a given flow and bed volume, resulting in improved stability of the deposited particles (i.e., reduced shear and particle breakthrough); and a continuous sludge dewatering and treatment system that decreases the volume of produced sludge while also providing a continuous waste stream for further processing. While early efforts have established a proof-of-concept demonstrating operations at 50% less energy demand than conventional systems, continued research and development to improve system performance and autonomy are needed. In line with this effort, the Phase I effort will focus on: 1) development of an automated control system for precise coagulant dosing; 2) design modification to minimize waste stream volume; and 3) design and construction of a pilot plant with 15 gallons per minute capacity, suitable to meet the water treatment needs of communities of ~300 people.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FORM FINDING STUDIO LLC
SBIR Phase I: Computer Aided Design and Simulation Software for Origami
Contact
1267 WILLIS ST STE 200
Redding, CA 96001--0400
NSF Award
2233133 – SBIR Phase I
Award amount to date
$275,000
Start / end date
07/01/2023 – 06/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to enable widespread adoption of origami design principles in industry to create manufacturing efficiencies and promote technological innovation. Origami-inspired engineering has applications across virtually all Science, Technology, Engineering, and Mathematics (STEM) fields, yet existing computer-aided design (CAD) tools are extremely limited in their ability to model folded geometries. This Phase I project and subsequent commercialization effort will fill a gap in the market by creating powerful and user-friendly software to model folding, with target customers spanning a broad range of industries: product design, architecture, packaging, papercraft, education, manufacturing, and materials engineering. Academic publications resulting from the research and development conducted during this project will advance fundamental knowledge of the mathematics of folding. Furthermore, this work seeks to create positive educational impacts through ongoing collaborations with K-12 STEM educators to create engaging curricula in geometry, digital design, and manufacturing through papercraft.
This SBIR Phase I project will create an intelligent software system that facilitates the design and simulation of folded geometries via powerful origami editing techniques grounded in fundamental research. The Phase I research and development builds on the team’s prior work on origami design algorithms and efficient origami simulation methods to establish a novel design framework that supports intuitive editing of folded geometry by novice users. A particular focus of the project is the under-explored domain of curved crease origami, which promises new opportunities for high-performance materials engineering and expressive design. Phase I research studies will investigate key open research problems underpinning the CAD system; this work will establish efficient methods and mathematical bounds for critical origami design algorithms to be implemented by the software system. Primary technical hurdles of the Phase I project include: architecting a novel constraint management system to support interactive editing of origami designs, establishing novel computational design algorithms for curved crease origami, and developing core data structures and geometry processing methods to be used by the CAD system. Throughout Phase I, the team will work with customers and strategic partners to identify specific use cases of folding in key vertical markets, develop a Minimum Viable Product (MVP) software application, and evaluate technical progress in terms of its commercial potential.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FREE TO FEED, INC.
SBIR Phase I: Real-Time Allergen Detection Technology for Dietary Proteins Transferred to Human Milk
Contact
5545 N BEAHAM AVE
Meridian, ID 83646--5819
NSF Award
2321861 – SBIR Phase I
Award amount to date
$274,946
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide the first test to determine the allergen content of human milk accurately, quickly, and cost-effectively. As much as 25% of infants have reported allergic responses to dietary proteins found in human milk which often results in breast/body feeding termination. Breast/body feeding is incredibly beneficial to health and continuation of nursing is a human health issue. Studies indicate that human milk is superior to hypoallergenic formula, providing natural antibodies to fight illness, lowering the risk of sudden infant death syndrome, and reducing the probability of developing disorders such as diabetes and leukemia. There is currently no at-home, real-time test for milk allergens on the market.
This Small Business Innovation Research (SBIR) Phase I project seeks to develop a real-time at-home allergen detection test. This project lays the groundwork for providing the first mechanism to research early and often allergen introduction through human milk when the immune system has been shown to be susceptible to allergy reduction strategies. The company’s patent-pending technology has the potential to provide a tool to identify allergens quickly and cost-effectively, allowing parents to monitor the presence of likely allergen triggers. The test is performed by the user in a real-time, in an at-home setting, which is a significant advantage over existing tests that require samples to be sent to a laboratory for labor and resource-intensive assays. The lateral flow technology provides a rapid and easy-to-read result, allowing the user to quickly determine whether the milk contains specific non-human protein fragments which are also known to elicit allergic responses in some patients.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FRESHKUBE INC.
SBIR Phase I: Adaptable Refrigeration Cycles for Smart Mini-Containers
Contact
2128 S PASEO LOMA
Mesa, AZ 85202--6485
NSF Award
2212910 – SBIR Phase I
Award amount to date
$255,981
Start / end date
12/01/2022 – 11/30/2023 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact of this SBIR Phase I project includes reductions in food waste, energy use, and carbon emissions by developing a highly efficient and adaptable system for refrigerated transport. Mini-containers, which are small, insulated boxes with environments controlled by a central driving unit that contains a refrigerator and other environmental controls are proposed. The technology may increase the economic feasibility of small farms by allowing them to target a more resilient, efficient, and potentially carbon-neutral cold supply chain. The potential impact of the research includes improved availability of nutritious food for the general population and mitigation of negative environmental effects of cold logistics. Additionally, mitigating food waste will create savings up and down the supply chain.
Two key technical challenges are addressed in this research. The first area has, as its main objective, the development of a volume-adaptable refrigeration system that allows for the efficient operation of several levels of control to best adapt to high levels of cooling capacity needs. The second area of research corresponds to the development of the methodology and solutions algorithms to exercise a hybrid control strategy for the optimal scheduling operation of the refrigeration system. The archetype refrigeration system would be subject to constraints imposed by the heterogeneous loads stored in the different special storage and transportation units known as mini-containers which receive cooling and other environmental services. This technology will allow for efficiently aggregating, storing, disaggregating, and distributing fresh products in the mini-containers. The mini-containers will allow the emergence of efficient cold logistics for small loads by enabling the capacity of large transportation containers to be split among multiple conveyances. The proposed solution will also make it possible to convert almost any truck into a refrigerated truck and any warehouse into a cold storage facility, supporting the emergence of sharing economies in cold logistics.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FRIA LLC
SBIR Phase I: Revolutionizing the Menopause Marketplace: The Potential of Cooling Jewelry to Provide Non-Medicinal Relief for Hot Flashes and Night Sweats
Contact
1705 MADALINE DR
Avenel, NJ 07001--1374
NSF Award
2235189 – SBIR Phase I
Award amount to date
$274,990
Start / end date
08/01/2023 – 01/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commerical impact of this Small Business Innovation Research (SBIR) Phase I project is to provide an alternative to hormone replacement therapy for women going through menopause. The technical design and integration are unique in that the end product will operate in the form of hydro-activated, fashion jewelry. This product offers a discreet, stylish, and efficient solution for mitigating vasomotor symptoms, allowing women to remain comfortable as they go through menopause without having to resort to medications with unpleasant side effects. Additionally, the technology has the potential to positively impact the well-being of adjacent groups who suffer from other conditions that lead to overheating. The research and development of this product will increase access to health and wellness options for patients adversely affected by health challenges around the globe.
The proposed project seeks to advance the knowledge and understanding of evaporative cooling for use in wearable tech applications. The primary purpose of this research is to provide external cooling relief, via a passive-tech fashion accessory, to individuals experiencing vasomotor symptoms or overheating in general. The goals of the project include improving the efficacy and timing of cooling, while overcoming perception challenges that can vary by individual user experiences. Furthermore, new materials will be explored to further enhance the cooling sensation with testing and optimization being a consistent factor. Finally, customer feedback will be gathered regarding product efficacy and marketability. Upon completion, this research will provide a greater understanding of how to optimize and refine the evaporative cooling technology for wearable tech applications, leading to improved consumer satisfaction. Knowledge and understanding of evaporative cooling for use outside of the typical, large-scale industrial and residential home applications will be advanced and utilized for the proposed application.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FRONTLINE BIOTECHNOLOGIES INC.
SBIR Phase I: A Versatile Nucleic Acid Collection and Purification Technology for Wastewater-Based Epidemiology
Contact
2143 FOLWELL AVE
Falcon Heights, MN 55108--1306
NSF Award
2224172 – SBIR Phase I
Award amount to date
$275,000
Start / end date
03/01/2023 – 02/29/2024 (Estimated)
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide private and public stakeholders with new technological tools to better monitor and predict pandemics. This project is expected to modernize the tools used today by private and public laboratories to collect and purify pathogens, particularly human coronaviruses from community wastewater, for testing and diagnostic purposes. The new technological tools are expected to simplify the workflow, reduce costs and time, and enable the prediction of COVID-19 outbreaks and other pandemics several weeks before observing clinical cases. Such early prediction would provide the public and government agencies with important data and sufficient time to take preventive measures. The technological products of this project are expected to empower the growing number of companies and laboratories offering wastewater-based epidemiology (WBE) services and help establish WBE as a routine, cost-effective and reliable tool for public health monitoring.
This Small Business Innovation Research (SBIR) Phase I project will address a major technological barrier for the detection of viruses such as SARS-CoV-2 in wastewater. Commercially available nucleic acid collection and purification kits are designed for clinical samples with small volumes. These kits are not generally used for large wastewater volumes where the virus is present at low concentrations. As a result, current processes are time-consuming, and result in the recovery of less than 30% of viruses and nucleic acids, significantly reducing the sensitivity. The goal of this SBIR Phase I project is to demonstrate the feasibility of a novel virus and nucleic acid collection and purification technology from wastewater. Specifically, the project tasks aim at enhancing understanding of virus properties, particularly human coronaviruses in wastewater and their interactions with filter media. The project’s innovative approach is to design a streamlined workflow that includes all the steps from sample collection to detection, in a single disposable cartridge containing novel filters with high affinity to viruses. This development is expected to enhance viral and nucleic acid recovery in wastewater to over 90%, while reducing costs. The developed tools will be independently tested and evaluated by third-party laboratories to confirm their performance.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
FUNCTION THERAPEUTICS, INC.
SBIR Phase I: Structure-guided design of anti-inflammatory modulators of protease-activated receptor 1 (PAR1)
Contact
1626 N PROSPECT AVE APT 1901
Milwaukee, WI 53202--2437
NSF Award
2223225 – SBIR Phase I
Award amount to date
$274,710
Start / end date
03/15/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is that a deeper structural understanding will be developed of protease-activated receptor 1 (PAR1), an important target for promising new anti-thrombotic and anti-inflammatory drugs. The project will also support the development of new compounds targeting PAR1 with the potential for improved potency and safety profiles. Such compounds could represent a new drug class for the treatment of inflammation-related diseases, including kidney disease.
The proposed project involves the confirmation of the binding site on PAR1 of small molecule ligands called parmodulins. A detailed characterization of this binding site will support the rapid design, synthesis, and testing of new and improved parmodulins with superior properties as oral medications. A combination of computational, structural biology, and synthetic methods will be combined with PAR1 cell assays to confirm the binding site and develop more detailed structure-activity relationships of the parmodulins. It is also anticipated that novel parmodulins will be identified in this project with improved safety and stability profiles.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
GAIA AI, INC
STTR Phase I: Registration of Below-Canopy, Above-Canopy, and Satellite Sensor Streams for Forest Inventories
Contact
444 SOMERVILLE AVE
Somerville, MA 02143--3260
NSF Award
2234077 – STTR Phase I
Award amount to date
$275,000
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is to increase the volume and improve the accuracy of data on the world’s forests. Presently, when collecting data on forests, surveyors must choose between slow, laborious methods, or quick but inaccurate ones. This project uses recent advances in sensors and machine learning to greatly improve data collection speed without sacrificing accuracy. The resulting rich datasets enable the construction of true “digital twins” of forests and open the door for higher fidelity modeling of forest growth trajectories. This information is useful both for timber firms seeking to maximize the potential of their assets and environmental groups projecting how changes today could impact a forest’s performance as a carbon-sink over the long term. The impacts on United States citizens are widespread. Here are two examples: improved efficiency in the timber industry brings down the cost and improves the quality of raw materials and turning forests into denser carbon sinks helps meet climate change goals. The availability of such broad and deep data on forests could also drive a boom in research and understanding about the more complex and nuanced relationships that drive forest health and productivity, launching entirely new sub-industries around forestry.
The key technological innovations explored in this STTR Phase I project are in constructing the most high-fidelity forest model (digital twin) by combining disparate information sources, each with their own advantages and disadvantages. Light detecting and ranging (LiDAR) and camera sensors on backpacks provide high-quality inventory metrics nearly 1000 times faster than manual measurements, but still require someone in the forest to wear the backpack. Satellite imagery scales almost instantly to entire forests and also through time with historical data but is limited by the top-down nature of satellites and the resolution they offer, especially when historical and free data sources are considered. Drone-based imagery sits in-between, with advantages and disadvantages of both. In practice, combining information sources that measure in such different ways can be very difficult. In this project, the team explores how to express LiDAR-based metrics to best associate them with top-down imagery from satellites and drones. From these associations, one can then build powerful machine learning models and specialize them to individual forests. This ability may enable the company to provide forest inventories and forest management recommendations to timber companies at any scale: with satellite imagery only or with a combination of backpack-LiDAR and satellite for the highest accuracy over the entire forest.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
GEMINATIO
SBIR Phase I: Liquid-Enabled Advanced Pitch (LEAP) Semiconductor Manufacturing
Contact
50 MAKAMAH BEACH RD
Fort Salonga, NY 11768-
NSF Award
2304119 – SBIR Phase I
Award amount to date
$275,000
Start / end date
07/15/2023 – 06/30/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is the development of materials and processes for semiconductor manufacturing that will enable the progression of Moore’s Law and help to strengthen domestic semiconductor manufacturing capacity and capability. Recent supply chain issues have plagued the semiconductor industry, and this has had ripple effects throughout the American economy. The majority of advanced semiconductor manufacturing capacity is outside of the U.S. and this recent shortage has highlighted the need for domestic foundries both for economic vitality in the U.S. as well as national security and supply chain resiliency. In 2019, American semiconductor foundries directly employed 184,600 workers, down from 292,100 (-37%) in 2001. The main loss of manufacturing jobs was attributed to the utilization of offshore foundries. Currently, U.S. semiconductor manufacturing represents just 1% of global capacity and 80% of U.S. semiconductor manufacturing capacity is in the 200 mm (8-inch) format, which is not compatible with the most advanced, high-performance processes, limiting production to >65 nm nodes. This project will increase the competitiveness of currently established U.S.-based foundries as well as increase the performance of foundries that are under construction.
This project seeks to develop and validate the performance of several required materials to enable the integration of a novel semiconductor manufacturing process that has the capability to double the density of features in current cutting-edge semiconductor chip manufacturing processes. This solution may also simplify the overall manufacturing process, without the need for intensive capital expenditures. At the conclusion of this project, the performance of the developed materials and the resulting manufacturing improvement will be demonstrated on both 8-inch and 12-inch formats. The process begins with conventional photolithography on a chemically amplified resist to define a relief pattern. A Trencher material is then coated on top of and diffused into the pattern, creating a self-aligned layer of polarity-switched material at the sidewalls of the resist. A Masker is then applied to fill the openings in the pattern, and the final pitch-doubled pattern is revealed. The diffusion-controlled process achieves a similar result to alternative processes without the need for expensive tool upgrades. The technology can extend canonical lithography methods by up to 2 nodes, reduce production costs by more than 80%, and reduce patterning errors to improve yield. Importantly, the process is applicable to 8-inch wafers, bringing advanced node dimensionality to older fabs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
GENASSIST INC
SBIR Phase I: Creation of antimicrobial MyoMatrix for functional muscle regeneration in a porcine model of volumetric muscle loss
Contact
1713 FREMONT ST
Cape Girardeau, MO 63701--1914
NSF Award
2304420 – SBIR Phase I
Award amount to date
$274,993
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop an antimicrobial muscle-regenerating biomaterial into a commercial-ready product and address volumetric muscle loss injuries. In the context of defense medicine, severe muscle trauma often occurs in environments where external factors such as sterility are not well-controlled. This project is expected to demonstrate antimicrobial properties of this novel muscle-regenerating biomaterial to enable use in these environments. If successful, the broader societal and economic impacts of antimicrobial muscle-regenerating biomaterials are staggering. Volumetric muscle loss affects tens of millions of victims each year. Sixty percent of patients are left untreated, 30% receive a muscle flap transplant, and 10% of injured limbs are amputated. Total average lifetime costs for amputation now total over $700,000. Improved clinical outcomes resulting from the implementation of this technology could lead to hundreds of thousands of dollars in savings over the course of each recipient's lifetime.
This Small Business Innovation Research (SBIR) Phase I project demonstrates significant advances over the existing standard of care for the treatment of volumetric muscle loss, for which no treatment currently exists. The joint loss of cells and extracellular matrix creates an environment where muscle regeneration cannot occur, leading to muscle collapse and atrophy over time. This project effectively replaces the extracellular matrix lost in volumetric muscle loss and creates an environment where satellite cells may proliferate and differentiate into new muscle tissue. A technical concern raised by clinicians, especially those who work in austere environments in military medicine, is the risk of infection caused by implanting a foreign substance into a wound bed. To address this, Technical Objective 1 will focus on incorporating antibacterial agents to optimize the scaffold’s ability to promote muscle regeneration while also having an antibacterial effect. Structural and mechanical properties will be assessed, cellular viability ensured, muscle cell quality evaluated, and antibacterial properties measured. Technical Objective 2 aims to investigate these outcomes with a pilot porcine model of muscle trauma. It is anticipated that the proposed antimicrobial biomaterial will both combat the risk of infection and effectively regenerate functional muscle in traumatic muscle injuries.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
GENCORES LLC
SBIR Phase I: Rapid and scalable production of high-performance 3-dimensional foam cores
Contact
1529 CAMBRIDGE ST APT 2
Cambridge, MA 02139--1012
NSF Award
2136727 – SBIR Phase I
Award amount to date
$256,000
Start / end date
11/15/2021 – 11/30/2023 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to reduce the weight and increase the efficiency of today's ground vehicles. Replacing steel with structural composites, a foam core wrapped in carbon fiber and resin, is a key strategy to reduce vehicular structural weight and to increase efficiency and safety. Current foam cores often exhibit low performance and are costly to produce for non-planar designs. As a result, three-dimensional cored composites remain cost-prohibitive for mass-manufactured automobiles and relegated to niche, high-performance vehicles. Improving core performance and fabrication processes will unlock high volume manufacturing of structural composites and enable manufacturers to increase the efficiency of their fleet by up to 40% throughout the next decade. Such a step-improvement in manufacturability can also accelerate urban air mobility vehicles and electric aircraft development and deployment.
This Small Business Innovation Research (SBIR) Phase I project will support the development of a novel, high throughput additive manufacturing technology for three-dimensional, thermosetting polymer foam parts featuring unique, specific mechanical properties (strength-to-weight or stiffness-to-weight ratios). Combining a unique 3D printing nozzle for thermosetting polymers, material science, and robotics enables unlocking on-demand and the rapid production of net shape, complex foam parts to be incorporated into existing supply chains. The project will develop and test novel resin formulations, optimize material synthesis and deposition, and enable the control of foam microstructure in situ. Using this latter feature, coupled with topology-optimization software, the project will produce the first foam metamaterials. These unique metamaterials will be mechanically and thermally tested using American Society for Testing and Materials standards to ensure the processing meets industry standards.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
GEOLABE LLC
SBIR Phase I: Autonomous Interferometric Synthetic Aperture Radar (InSAR) for surface deformation monitoring
Contact
1615 CENTRAL AVE
Los Alamos, NM 87544--3018
NSF Award
2213289 – SBIR Phase I
Award amount to date
$254,707
Start / end date
03/01/2023 – 02/29/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to enable the global autonomous detection of surface deformation. Measuring Earth surface deformation is fundamental to detect and analyze surface and subsurface changes due to anthropogenic activity, with a myriad of industrial applications that includes the monitoring of oil and gas extraction fields and storage reservoirs, mining operations, carbon dioxide sequestration, and/or infrastructure integrity. Illustrating the economic and social impact of its uses, the market for analyzing Interferometric Synthetic Aperture Radar (InSAR) data is expected to double within 5 years. Beyond the dramatic economic growth of InSAR, its far-ranging applications have broad social and scientific impacts, in particular related to natural hazards and climate change. Advances in InSAR processing and improved signal-to-noise ratios will translate into improved monitoring of earthquake activity, landslides, water supplies, deforestation, floods, ice sheets, etc.
This Small Business Innovation Research (SBIR) Phase I project aims at tackling the lack of automation in InSAR processing and improving detection thresholds in InSAR time series analysis. While the technique can potentially measure millimeter-scale changes in deformation over periods of days to years, atmospheric effects can wreak havoc on repeat-pass InSAR interpretation by introducing errors that may mask small surface deformations. These effects, which are fundamentally due to pressure, temperature and relative humidity variations in the troposphere, can lead to errors that are larger than most of the deformation signals of interest. Current algorithms are not suited for automated, large-scale monitoring without a priori data because they require time-consuming manual intervention, and the final product requires exhaustive expert interpretation. Through the development of machine learning and artificial intelligence methods this project aims at: (i) further automating and accelerating the processing of InSAR time series, via the automation of some key sections of the processing pipeline that still rely on extensive and costly human intervention; and (ii) developing a new methodology to generate InSAR time series, that is robust to noise and allows for a finer temporal and spatial resolution compared to the state-of-the-art.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
GNU COMPANY, LLC
SBIR Phase I: Safety Syringe Needle for Prevention of Unintended/Accidental Puncture (Needlestick Injury)
Contact
147 HIGHLAND AVE
Winchester, MA 01890--1435
NSF Award
2219892 – SBIR Phase I
Award amount to date
$249,369
Start / end date
05/01/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a novel medical device for intramuscular injections that will reduce needlestick injury. Unintended or accidental puncture is the second most common occupational hazard for healthcare staff. An estimated 385,000 needlestick injuries occur in the United States each year impacting 5.6 million healthcare workers. Needlestick injury represents one of the largest risks, both financially ($258 million annually) and medically (e.g., bloodborne pathogen exposure), to healthcare providers. This project aims to develop a novel, flexible hypodermic needle with a safety syringe that enables rapid injections while replacing sharp needles and significantly reducing risks of unintended health care provider injury.
This Small Business Innovation Research Phase I project provides a novel, flexible, polymer needle-based safety syringe for health care providers to perform intramuscular injections in patients. A novel and variable stiffness shaft is integrated with an external safety lumen mechanism to create an integrated delivery system with the same delivery reliability and repeatability as standard needlesticks. The design will be prototyped and evaluated under a variety of human factors considerations. The device will be required to pass several mechanical tests for puncture, insertion, and lumen integrity in a consistent manner, as anticipated during routine clinical use.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
GOODMAN CONSULTING GROUP, LLC
SBIR Phase I: Pathogen Interception: A new method for finding and identifying genetic sequences
Contact
3749 N PLACITA VERGEL
Tucson, AZ 85719--1439
NSF Award
2230484 – SBIR Phase I
Award amount to date
$275,000
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be the ability to quickly and inexpensively determine the presence and genetic sequence of a wide variety of pathogenic organisms. Most importantly, this technology could be implemented without prior assumptions as to which organisms are expected. Sequencing will be accomplished by direct electrical identification of the building blocks, the bases, of the genomic sequence. The potential societal impact of this technology is to provide a method to screen individuals quickly (under a minute) for the presence of infections. Screening at ports of entry and in appropriate community settings will minimize disease transmission and allow for the quick identification and treatment of any infected individuals at US borders. In addition, beyond this immediate application, the technology may also enhance scientific understanding of normal genetic sequences in any organism. If its anticipated speed, high accuracy, and low cost are realized, this technology may find applications in human in vitro diagnostics and human genome sequencing. The studies in this Phase I project will lead to a proof-of-concept demonstration for an automated, commercial instrument.
The project seeks to determine the identity and order of the genetic building blocks, the nucleotide bases, comprising any genomic sequences present in a sample solution. This sequencing will be done by examining the ability of each base in the sequence to modify a tunneling current as it is passed by electrophoresis across two very closely spaced tunneling electrodes. Tunneling is a well-known quantum mechanical effect, and it is quite sensitive to the electrical configuration of the object (here a given specific nucleotide base) present between its electrodes. Experiments with this technology to date have been unsuccessful because genetic sequences have not been able to be moved slowly enough across the tunneling electrodes for their bases to be distinguished. The studies here will overcome this problem by modifications of the geometry and solution conditions of the electrophoresis and possibly with improved methods of tunneling current detection. The data obtained through the application of this technology is expected to enhance the current understanding of nucleotide base chemistry. The solution may permit the detection of nucleotide base modifications of potential biological and medical importance.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
GRIDIRON ROBOTICS LLC
SBIR Phase I: Developing an Automated Outbound Packing System
Contact
31 OAK AVE
Chalfont, PA 18914--0001
NSF Award
2223089 – SBIR Phase I
Award amount to date
$275,000
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable the fast and efficient loading of parcels into shipping containers ranging from small delivery vans to maritime shipping containers. This project will focus on demonstrating the feasibility of an algorithmic approach and robotic development. The technology is a step towards creating a fully autonomous system with expanded robotics capability to further enhance the efficiency and speed of outbound shipping for customers. Over 3,000 parcels are shipped every second. However, in the U.S., one out of every four trucks is empty, two are less than 50% filled, and only one is filled over 50% capacity. Initial projections indicate that the technology under development could decrease trucking costs by 20%, reduce loading costs by 70-80%, and decrease loading time by 30%, all while meeting the demands of peak shipping seasons. Overall, increasing the density of parcel shipping will reduce greenhouse gas emissions (400 tons/per truck/per year), reduce traffic congestion, and enable smaller businesses to compete with large organizations by reducing their logistics and shipping operating costs.
This Small Business Innovation Research (SBIR) Phase I project will focus on advancing a bin packing algorithm to minimize void space in outbound shipping containers. The 3-Dimentional Bin Packing Problem (3D-BPP) is a classic Nonlinear Programming (NP)-hard problem that has been studied for decades. To solve the problem, an effective and easy-to-implement constrained, quantum accelerated, deep reinforcement learning model is being developed. Monte Carlo Tree Search is an unsupervised, heuristic search algorithm technique in which the learning agent learns to predict the expected value of a variable occurring at the end of a sequence of states. Deep reinforcement learning (DRL) extends this technique by allowing the learned state-values to guide actions which subsequently change the environment state. A proof-of-concept assessment showed that the learned strategy meaningfully outperforms the state-of-the-art methods. Outcome success metrics for this project are >90% utilization rate, sub 24 hours of model training time, and >2500 parcels/hour for any given data set. This foundation will be expanded by integrating many unique box sizes, exploring model performance in the face of broader circumstances (e.g., lookahead and stacking parameters, General Processing Unit (GPU) vs quantum training), and developing of a robotic gripper to enact algorithmic output.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
HARPE BIOHERBICIDE SOLUTIONS, INC.
SBIR Phase I: Safe control of herbicide-resistant weeds with a novel natural bioherbicide platform
Contact
501 COLE ST
Raleigh, NC 27605--1207
NSF Award
2223639 – SBIR Phase I
Award amount to date
$274,927
Start / end date
02/15/2023 – 10/31/2023 (Estimated)
NSF Program Director
Errata
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Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in providing solutions to farmers who are facing the culmination of decades of herbicide resistant weed species evolving from applications of synthetic herbicides. Creation of herbicides which are effective, naturally produced, scalable, and deployed using the current agronomic practices could alter the foundations of crop production in the United States and around the world. In view of the projected 8% annual increase in global food and agriculture market, shrinking areas under cultivation that lead to the need for higher productivity per acre, and increasing demand for nutritional food items, the need for sustainable alternatives to current synthetic herbicides that do not promote herbicide resistant weeds is becoming clearer. Widespread adoption of the proposed technology is expected to benefit farmers and crop producers reducing societal strain, financial burden, and environmental stress from crop losses due to herbicide resistant weeds by eliminating these weeds through an environmentally safe method, without the use of excess fuel, time, equipment, and synthetic herbicides.
The intellectual merit of this project is in developing a novel natural herbicide product that, when applied to herbicide resistant weeds, will cause seed or plant cell's membranes to degrade and lose integrity. Thus, the novel product is intended to work both as pre-emergent weed prevention and post-emergent weed control herbicide. The product will be an environmentally safe blend of natural plant extracts and excipients. The herbicide formulations will be sprayable onto soil or onto plant leaves and stems. The cost and time needed to initially screen herbicide rates, outcomes, and best practices typically is many years. The greenhouse screening approach takes months and provides valuable information to ensure that field trials, which are more expensive and impacted by changes in weather, are efficient in cost and outcomes. This project will initially focus on greenhouse validation of weed control of the most resistant weeds known in different geographical locations in the U.S. Dose response data for 50%-to-90% inhibition control/efficacy of herbicide resistant weeds will provide the information to develop a herbicide use label, directions for best practices, and good stewardship by using only the amount of herbicide needed for control without overuse.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
HERA HEALTH SOLUTIONS INC.
SBIR Phase I: Bio-erodible Contraceptive-Releasing Implant
Contact
11141 WELLSHIRE LN
Frisco, TX 75035--3637
NSF Award
2304404 – SBIR Phase I
Award amount to date
$275,000
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
Errata
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Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to provide a highly effective and long-acting implantable contraceptive for the 61 million American women in their childbearing years (ages 15 to 44), 70% of whom are at risk for unintended pregnancies. Unintended pregnancies accounted for half of the total pregnancies in 2022, and over 60% of unplanned pregnancies end in abortion, with an estimated 45% of abortions being unsafe, resulting in 5-13% of all maternal fatalities. Today, an estimated 7.5 million women aged 15–49 receive a treatment or hormonal drug via long-term subcutaneous arm implants. Once they reach the end of their lifespan, these implants must be removed, and complications can quickly arise. Not only are these procedures expensive, but they leave behind heavy bruising and scarring and some instances even require an operation for removal. The proposed product is the world’s first biodegradable contraceptive-releasing implant. The technology combines Food and Drug Administration (FDA)-approved material with a generic drug already on the market. It uses novel manufacturing methods and biodegradable materials, eliminating the need for implant removal and enabling the proper timing and therapeutic dosage. This novel delivery drug technology can be applied to different drug treatments in a sustainable and affordable manner.
This Small Business Innovation Research (SBIR) Phase I project aims to advance the future of long-acting reversible contraception by creating a biodegradable arm implant that delivers a consistent hormone dose and does not have to be surgically removed. The goal of this SBIR Phase I project is to characterize the drug delivery scaffold and demonstrate its utility. This project will de-risk the prototype to be used in Pre-Investigational New Drug Applications (IND) studies required by the FDA. The Phase I strategy will be two-fold: (1) de-risking operations by finalizing the prototype after evaluating the physical and chemical properties and (2) test the long-acting contraception implant prototype in a clinically relevant biological model to provide the necessary data for a successful IND launch. Progress of this project will provide a solid foundation for advancing the biodegradable contraception product toward commercial utility.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
HEXAKIT, INC.
STTR Phase I: Cardiotropic Atorvastatin Liposomes for Myocardial Reperfusion Injury
Contact
2401 CHEVAL POINTE DR
Edmond, OK 73034--6085
NSF Award
2300933 – STTR Phase I
Award amount to date
$275,000
Start / end date
05/15/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project pertains to the critical need for pharmacologic treatments in large populations that suffer from coronary artery disease and that are subjected to procedures such as angioplasty or bypass surgery. While effective in correcting the reduced flow of blood to the heart tissue, these interventions also cause tissue damage categorized as ischemia reperfusion injury (IRI). A pharmacologic treatment is critically needed to address this residual injury that is a major cause of rehospitalization, prolonged hospital-stay, heart failure, and mortality. By deploying the proposed technology of heart-targeted vehicle for drug delivery, it will be possible to achieve effective cardioprotection, which will reduce patient distress and economic burden on the healthcare system for patients undergoing reperfusion procedures. This disruptive technology will impact myocardial reperfusion injury market worth $1.6 billion and influence the standard of care for over 1 million coronary artery disease patients that are treated by angioplasty or bypass surgery each year in the United States.
This Small Business Technology Transfer (STTR) Phase I project will develop Cardiotropic Atorvastatin Liposomes (CATLIP) to deliver cardioprotective benefits of atorvastatin to the heart, safely and effectively. The innovative CATLIP technology will deliver therapeutic amounts of atorvastatin to the heart tissue for treatment of IRI. Current methods of administration are inadequate in reaching the drug concentration needed for clinical efficacy. Serving a long-term goal of developing a myocardia-targeted treatment to mitigate IRI, the research objective of this proof-of-principle project is to attain effective delivery of atorvastatin in the heart tissue. CATLIP’s targeting approach is based on a small molecule targeting vector that selectively binds to the cardiomyosin exposed in the injured heart tissue. The research team will prepare and characterize liposomes loaded with atorvastatin and modified on the surface with the cardiotropic targeting vector. This preparation will be tested in an animal model of myocardial infraction to determine atorvastatin delivery in the heart tissue. Successful implementation of this project will create a proprietary product for treatment of IRI of the heart and demonstrate the potential of a targeting technology for application with other drugs for the same medical condition.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
HISTONE THERAPEUTICS CORP.
SBIR Phase I: Epigenetic Remodeling of Natural Killer (NK) Cells for Blood Cancer Therapies
Contact
5757 S OAKLAWN PL
Seattle, WA 98118--3048
NSF Award
2303792 – SBIR Phase I
Award amount to date
$273,388
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to produce better alternatives to cancer treatment. The new solution will take advantage of the body’s natural anticancer defense system, an immune cell called a natural killer cell or NK cell. NK cells are able to recognize almost any cancerous cell in the body and can target both solid tumors and blood cancers. This gives NK cells a broad appeal for the treatment of many types of cancer. The team proposes a broad-spectrum cancer treatment by modifying NK cells to be more reactive to cancerous cells in the body. These modified NK cells could potentially be combined with current therapies to enhance their effectiveness, without increasing side-effects in patients. This project has the potential to benefit millions of people, especially in the United States where it is estimated that 40% of individuals will be diagnosed with cancer at some point in their life.
This project will use a patented epigenetic modifier to enhance the tumor killing abilities of NK cells. Many immune cell-based therapies rely on altering the genetic code of the cell that will be used to treat disease. However, there are associated risks in altering the genetic code and often the cell therapy may only work on a very specific subtype of cancer. Epigenetic modifiers do not change the underlying DNA sequence but can effectively alter gene expression. Furthermore, NK cells can target a broad-spectrum of cancers but in many cancer patients their tumor killing ability is often suppressed. The research goal is to use the patented epigenetic modifier to increase expression of key NK cells genes that will make them more sensitive to detecting and killing cancer cells. After targeting key genes, NK cells will be assessed for increased tumor killing ability and for how long this ability persists. More specifically, this project seeks to demonstrate that NK cells taken from a healthy donor can be epigenetically altered to enhance their natural function of killing tumor cells. This solution will lay the groundwork to develop a NK cell therapy where NK cells isolated from healthy donors are epigenetically modified to enhance their activity, then delivered to cancer patients to hunt and kill their cancer cells.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
HYPERSOUND MEDICAL, INC.
STTR Phase I: Neuromodulation by Electromagnetic (EM) Energy-Induced Hypersound
Contact
1435 E UNIVERSITY DR STE C-109
Tempe, AZ 85281--8473
NSF Award
2136383 – STTR Phase I
Award amount to date
$256,000
Start / end date
03/15/2022 – 09/30/2023
NSF Program Director
Errata
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Abstract
The broader impact of this Small Business Technology Transfer (STTR) Phase I project proposes to develop a new way to block pain in the human body. This new technology offers the potential for a novel, less invasive, lower cost, non-addicting solution to pain relief. The project develops a device to be applied near or on the skin, significantly penetrating the tissue to inhibit pain. The primary market is to provide relief from back pain, which affects more than 100 M Americans. Other potential markets include pain suppression from peripheral wounds, neuralgias, migraine, cancer, diabetes-related neuropathies, and degenerative diseases, such as the rheumatoid group.
This Small Business Technology Transfer Phase I Project proposes a new approach to noninvasively modulate selected neural tissues to block pain by known principles of neurological competitive inhibition. The technology employs electromagnetic energy in a novel electrostrictive mode of action within the dielectric nature of cellular media to remotely evoke ultrasound as well as higher frequency hypersound forces in-situ. These induced forces are hypothesized to result in biological effects through the well-known action of cellular stretch activation. This project will further develop instrumentation to produce unique microwave device designs and determine the effects of microwave variables on neuromodulation. The research institution will apply the developed instrumentation on rat and neuronal cell models to define the important operating parameters for ensuring therapeutic safety and efficacy.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
Himet Materials LLC
STTR Phase I: Wafer-Integrated Soft Magnetic Composite Films for Inductors with High Power Density and Efficiency
Contact
16433 MONTEREY ST. SUITE 120
Morgan Hill, CA 95037--7168
NSF Award
2304631 – STTR Phase I
Award amount to date
$274,972
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
Errata
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Abstract
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is to revolutionize the miniature-powered Integrated Circuit (IC) converter market, widely used in cellular phones, Internet of Things devices, and microsensors. The technology will provide form factor reductions of various modules, leading to the further miniaturization of on-chip components. A manufacturing facility can service several IC companies by providing foundry capabilities to integrate inductors according to each customer’s design. The technology can provide US manufacturers of such devices with a significant competitive edge in the very large mobile electronics and miniature electronics markets both in commercial and defense markets. Establishing manufacturing capability in the US will support a revival of component packaging and back-end integration business.
Mobile device miniaturization is increasing at a rapid rate. In on-board power converters, passive components such as inductors and capacitors are among the largest components. The non-availability of IC-compatible, low-cost, soft magnetic cores with low loss, high frequency (>10MHz), and high saturation magnetization limits the implementation of on-chip inductors. The project aims to create innovative, soft magnetic composite (SMC) materials for inductor cores. IC-compatible high-performance SMC films, with thicknesses that can be scaled to 50 microns and above without losing performance, will be developed for the first time. The project's initial goal will be to develop physical and electrochemical synthesis methodologies for high magnetic moment, low loss, SMC materials that can be used to fabricate on-chip inductors, replacing ferrite core-based inductors in circuits. The approach can be scaled to handle different ranges of power and can be integrated on wafers, package substrates, or boards. These cores will enable inductor thickness to be reduced by at least 10 times for use in low form factor, point-of-use, direct current (DC) to DC power converters and IC voltage regulator circuitry.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ICONIUM ENGINEERING COMPANY
STTR Phase I: Refrigerant Ionic liquid Separation
Contact
2029 BECKER DRIVE
Lawrence, KS 66047--1620
NSF Award
2232475 – STTR Phase I
Award amount to date
$275,000
Start / end date
04/01/2023 – 03/31/2024 (Estimated)
Errata
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Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project will be to provide the refrigerant industry with the first commercial designs for separating complex, multi-component, azeotropic refrigerant mixtures for economical recycling. The American Innovation and Manufacturing Act authorizes the Environmental Protection Agency (EPA) to phase down production and consumption of refrigerants causing global warming by 85 percent over 15 years. The societal, economical, and environmental benefits are estimated to create thousands of jobs and increase manufacturing by billions of dollars in the U.S. while reducing venting and incineration, and subsequently global temperature rise. There are millions of metric tons of mixed refrigerants that cannot be reclaimed using current fractional distillation technology. The proposed ionic liquid extractive distillation technology will make possible the separation and reuse of these complex refrigerant mixtures. The startup company funded through this project will provide a novel separation technology to the refrigerant industry that is of strategic national interest. Outreach activities, especially toward the inclusion of women and underrepresented minorities will be supported through this project.
This STTR Phase I project proposes to demonstrate that complex, multi-component, azeotropic refrigerant mixtures can be separated using ionic liquids, and that the products can meet industry standard specifications. Refrigerant mixture R-404A, used broadly throughout the commercial refrigeration industry (e.g., grocery stores) is a complex, ternary azeotropic mixture containing HFC-143a (1,1,1-trifluoroethane), which has a high global warming potential. HFC-143a will be one of the first refrigerants to be reused in new low global warming blends containing hydrofluoroolefins if the HFC-143a can be economically separated. Over 100 complex, multicomponent refrigerant mixtures exist in the market. This project will enable the development of processes, technologies, and systems designs required for industry to meet and exceed the phase down goals. The program objectives include the separation of R-404A into components, the creation of a process model, and testing of recovered R-404A from industry to understand the effects of impurities. The results will be used to develop a technoeconomic model and commercial scale designs that the team will build and/or license to the refrigerant industry.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
IDEA51, LLC
SBIR Phase I: A Method to Expand Personalized Experiential Learning
Contact
1151 W MILLER ST
Boise, ID 83702--6965
NSF Award
2150912 – SBIR Phase I
Award amount to date
$245,844
Start / end date
06/01/2022 – 10/31/2023 (Estimated)
Errata
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Abstract
The broader impact of this SBIR Phase I project is to improve education and experiential learning. The proposed tool will support both synchronous and asynchronous learning while providing students with the opportunity to engage independently and connect their learning to internships, job shadows, service projects, and other real-world learning experiences. This project will enhance teacher capacity to guide and assess student learning, increase transparency and objectivity in assessment, and expand the learning ecosystem of schools by empowering students to engage in and validate authentic, real-world learning experience.
This Small Business Innovation Research (SBIR) Phase I project will develop a novel competency-based evaluation tool and learner recommendation engine designed to: assess a range of currently needed skills and competencies, aggregate assessment data for the purpose of generating a comprehensive learner record in real time, and generate personalized competency pathways and recommendations for learning and growth. The key intellectual merits of this proposal are the formulas used to aggregate assessment data over time and the algorithms used to generate personalized competency pathways and learner recommendations. The technical challenges are the development and testing of the associated algorithms to guide and evaluate learning in both academic and authentic, real-world contexts. Research conducted during this Phase I project enable testing and refining of the data aggregation formulas utilized by the assessment tool, as well as optimization of the learner recommendation algorithms used for the creation of personalized competency pathways.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
IIAM CORPORATION
SBIR Phase I: Predictive Analytics and Machine Learning Modeling for New Patient Cancer Referrals
Contact
27 ANDERSON ST
Boston, MA 02114--3637
NSF Award
2304498 – SBIR Phase I
Award amount to date
$275,000
Start / end date
08/15/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to decrease patient referral wait times. Referral wait times are often long since offices need to retrieve a large amount of medical information on a patient before they are seen by a doctor. Unfortunately, medical records are often not stored in one place, making it difficult to gather the needed medical histories. Quick and complete medical record retrieval is especially important for cancer patients, whose conditions can quickly change. Critical patients need to be seen by doctors in a timely manner to begin treatment. The company is creating a technology that could help quickly retrieve medical information to decrease the time from referral to appointment. The company expects these algorithms to expedite document reconciliation by 7 days, thereby reducing the time from referral for the new patient appointment by 1 week. By facilitating quicker and more meaningful record retrieval, the algorithms are expected to improve treatment initiation by 7-14 days. The company plans to commercialize its technology for use in large academic healthcare systems, first focusing on those with high-volume cancer centers.
This Small Business Innovation Research (SBIR) Phase I project will advance a new patient referral predictive analytics software platform for cancer centers. This platform will streamline referrals, increase resource utilization, and optimize care pathways. The company’s deep learning algorithms will be developed to streamline record retrieval for new patient appointments and recognize critical medical conditions, resource capacity, local referral patterns, and at-risk socioeconomic factors. This intervention may reduce the mortality risk by 3.2-6.4% per week per patient. To achieve these objectives, the software will contain two major components a cloud-based platform for medical information exchange and an machine learning (ML)-based analytics platform. Once fully developed and launched, it is anticipated that real-world deidentified and aggregated clinical data from the exchange platform will be used to further train and refine the ML model. Prior to this stage, data from large publicly available and multi-institutional databases will be used to provide training data points for the model.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
INDUSTRIAL ANALYTICS AND MODELING, INC.
SBIR Phase I: The Automated Forensic Economist: Towards Affordability, Transparency, and Efficiency in Forensic Economics
Contact
1601 E CESAR CHAVEZ ST
Austin, TX 78702--4585
NSF Award
2304596 – SBIR Phase I
Award amount to date
$257,604
Start / end date
09/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial impacts of this Small Business Innovation Research (SBIR) Phase I project capitalizes on the inefficiencies of the existing expert witness industry and brings affordability, versatility (unlimited scenario generation), simplicity, transparency, and
standardization to legal proceedings. The team will develop an ecosystem that will keep experts accountable and credible (using a peer-review system) and allow lawyers and their clients to be better informed with enhanced access to high-quality services. The team has validated that a profitable market exists for a standardized and automated method of evaluating economic losses in civil legal disputes in an automated, fast,
inexpensive, standard, and objective manner. The quality of expert witness services will increase across the industry, as experts will be able to focus more deeply on the more disputed issues of litigation rather than the automatable portions of the estimation process. Better-informed attorneys and firms will be able to counsel their clients and develop better legal strategies throughout the process, while judicial personnel and juries will benefit from improved legal and expert witness services, gaining access to more standardized information to make better, more-informed decisions and be less susceptible to biased or inaccurate opinions.
This SBIR Phase I project consists of a set of deterministic algorithms intaking user inputs (facts and data regarding parties in a lawsuit) and retrieving relevant data series from pre-harmonized external databases, which are then processed through a set of economic and statistical computations, producing a set of outputs, including an estimate of financial gains (losses) for the lawsuit characterized by user inputs. The proposed innovation improves on the inefficiencies of the existing expert witness industry in several dimensions, and as a result brings affordability, versatility, simplicity, transparency, and standardization. In Phase I, the algorithm and a usable prototype for capturing employment and personal injury-related financial gains (losses) estimation will be developed.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
INNOTECH SYSTEMS LLC
SBIR Phase I: Precision Docking for Automated Charging of Unmanned Platforms and Electric Vehicles
Contact
2834 PARAISO WAY
La Crescenta, CA 91214--2018
NSF Award
2230483 – SBIR Phase I
Award amount to date
$274,048
Start / end date
06/01/2023 – 05/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable automated and autonomous charging of a wide array of electric vehicles (e.g., electrified robots, cars and trucks, and drones) in adverse weather conditions. Examples include autonomous systems used in farming and logistics, electric vehicles in ports, and electric trucks. Despite tremendous progress and the proliferation of electric vehicles, an adequate, cost-effective, autonomous, and available charging infrastructure is currently lacking. This gap between need and availability is far worse in high power applications such as trucking. The proposed technology increases the deployment of autonomous mobile robots and drones in industries like agriculture and logistics which are currently suffering from labor issues. The technology may increase the deployment rate of commercial electric vehicle fleets that can contribute to reducing greenhouse gasses.
This SBIR Phase I project proposes to solve a key problem in the automation of the charging process for electric vehicles, namely precision localization and docking in adverse weather conditions. The conventional methods of localization for docking (e.g., infrared or vision-based) have limitations such as insufficient precision and limited performance in less-than-optimal environmental conditions. This team presents a high precision automated docking solution in the presence of clutter and removes objects that are potentially harming the Line of Sight (LOS). The goals of the proposed research and development are: 1) establishing that time averaged, multi-path signal characteristics in multiple spectral bands can identify locations within a known map or during a close proximity approach of the electric vehicle to the charger; 2) developing models suitable for Monte-Carlo modeling and simulation of an indoor environment or region in proximity of a charger benchmarked by some measurements and using such simulations for verification success; and 3) developing a high precision transponder based on wideband signaling.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
INVERSAI, INC.
STTR Phase I: Integrating Vision-Guided Collaborative Robots for Postharvest Processing of Produce
Contact
111 RIVERBEND RD
Athens, GA 30602--1514
NSF Award
2208902 – STTR Phase I
Award amount to date
$212,153
Start / end date
01/15/2023 – 09/30/2023
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Technology Transfer (STTR) Phase I project is to empower the processors of harvested fruits and vegetables with the flexibility to use robotic automation to meet their labor needs. The automation uses collaborative robots (cobots) guided by computer vision, which are potentially safe around humans. The technology will help assure consistent produce quality and processing rates. Through a robust cobot-based solution, the project will provide an affordable, sustainable, and safe means for farms of all sizes to keep up with their production goals, which will sustain competition and the nation’s food supply. This project has the added benefit of upskilling workers in farms by creating openings for more technically oriented positions, both in monitoring and maintaining the cobots. Instead of tediously programming the cobot for each use, the project is introducing a new way of translating the tasks performed by humans to the cobot by learning from camera recordings. It will also improve understanding of how cobots can safely be used alongside humans in a shared working space.
This Small Business Technology Transfer (STTR) Phase 1 project aims to make it possible to use cobots with human workers on tasks that go beyond the traditional pick-and-place. The proposed technology will automate processing line tasks that require computer vision, which is challenging because accurate and reliable perception must guide the robot’s motion. Research has coalesced the technical challenges on the path to a viable commercial product around five steps. These start with a formal description of the task domain followed by using robust implementations of noise-tolerant machine learning algorithms for automatically learning the task, and end with a solution that integrates the learned task behavior with a vision-guided cobot system. Phase 1 will support research toward addressing two problems. The first is to design an intuitive way to elicit a precise specification of the client’s task domain. A digital conversational assistant will utilize multiple modalities for the elicitation. The second is the inability of available implementations to generate coworker-aware and efficient cobot movements. The research will investigate and develop significant improvements to the cobot motion to improve coworker safety while reducing the processing time by an expected 50%.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ITOMOGRAPHY CORPORATION
SBIR Phase I: Dual-Modality Tomography Core Scanner for Oil Saturation Estimation at the Wellsite (iTomoSoS)
Contact
1130 PENNBURY DR
Houston, TX 77094--4108
NSF Award
2213107 – SBIR Phase I
Award amount to date
$256,000
Start / end date
03/15/2023 – 11/30/2023 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to make oil exploration and production (E&P) operations less risky and economically and technically more efficient. The new technology has the potential to achieve significant reductions in oil exploration drilling costs. The technology will also increase long-term commercial benefits for exploration and production companies due to more accurate and reliable estimates of the recoverable reserves and production planning over the decades-long reservoir lifecycle. These benefits will support global economic growth by ensuring a continued supply of hydrocarbons for the US and internationally during the critical transition to renewable energies. The new technology being developed under this SBIR project will also reduce the environmental impact, energy consumption, and overall carbon footprint of offshore/onshore E&P operations by reducing waste, pollution, and carbon dioxide (CO2) emissions, thereby directly benefiting future generations. Successful completion of the project will further reinforce the leadership of the US in utilization of advanced hydrocarbon E&P technologies and support the goal of energy independence from global markets by making US producers more effective and competitive.
This SBIR Phase I project proposes to demonstrate the feasibility of determining volumetric (3D) hydrocarbon distribution throughout large-diameter long cores with high spatial resolution (10 mm3), 95% accuracy, and 1 meter/hour throughput at the offshore/onshore wellsite. Today, this is not possible with any technology, i.e., logging data can be used to derive hydrocarbon estimates on a sparse set of points along the wellbore, while “special core analysis” at onshore laboratories takes weeks to complete. Neither of the current techniques can closely match the resolution and accuracy offered by the proposed solution. The disruptive innovation of this development is to combine 3-dimensional (3D) X-ray Multi-Energy Computed Tomography (MECT) and Electrical Impedance Tomography (EIT) in one wellsite core scanning instrument, thereby allowing for novel, synergistic combination of the information provided by the MECT and EIT modalities. Algorithms will be developed to combine the complementary datasets to yield 3D hydrocarbon distribution in the cores in near real-time. These algorithms will be tested on realistic digital core models. The ultimate goal of Phase I is to demonstrate that the specified target accuracy, spatial resolution, and scanning-imaging throughput for assessing 3D distribution of hydrocarbons in large cores can be achieved with the new wellsite instrument.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
KARIOS TECHNOLOGIES, LLC
SBIR Phase I: Development of a Novel, Sprayable, Large Volume Hydrogel Delivery System Platform
Contact
100 NORWICH ST
Charlottesville, VA 22903--6410
NSF Award
2304462 – SBIR Phase I
Award amount to date
$274,954
Start / end date
08/15/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a simplified system for applying compound in situ to form biomaterials during surgical procedures. Controlled biomaterial applications pose significant potential surgical advantages including for anti-adhesion, tissue sealant, and drug delivery purposes. The proposed platform will enable procedural consistency to provide new and improved ways to manage bleeding and reduce scar tissue formation during surgical procedures. This product aims to gain a share of the $1.5 billion adhesion prevention market and $1.2 billion hemostat market, and enable eventual site-specific delivery cells and drugs, depending on the biomaterial delivered.
This Small Business Innovation Research (SBIR) Phase I project will develop a ready-to-use, large volume delivery system for in situ forming biomaterials. The scope of activities includes transferring a novel, proprietary, in situ biomaterial with applicability as a tissue sealant, scar tissue reductant, and drug/cell delivery vehicle, into a novel single use applicator. The prepackaged delivery system will formulate the suspended biomaterial with the resuspension solution using an internal mechanical mechanism which delivers the biomaterial in a controlled aerosolized manner suitable for clinical use. This Phase 1 project aims to complete and validate prototypes with lyophilized biomaterials within good manufacturing practices, engineer the design of the syringe barrel and delivery tips/nozzles, and complete laboratory validation in a manner suitable for first in human use.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
KAZADI ENTERPRISES LTD.
SBIR Phase I: Hydrologic Open Cooling System (HOCS) for low-energy refrigeration
Contact
529 S FOREST AVE
Batavia, IL 60510--2772
NSF Award
2223197 – SBIR Phase I
Award amount to date
$275,000
Start / end date
03/15/2023 – 11/30/2023 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a technology enabling low-energy, resilient cooling that is capable of mitigating the environmental impacts and high costs of conventional refrigeration. There is a critical need for sustainable cooling solutions that provide an alternative to hydrofluorocarbon-based refrigerants that rely on carbon dioxide (CO2)-emitting systems. This project seeks to develop a Hydrologic Open Cooling System (HOCS) that can maintain cooling chambers at 1.5-5°C utilizing 10% or less of the electrical power of conventional systems. This project unlocks environmental heat as an energy reservoir and offsets significant energy costs of conventional cooling systems. The technology can support highly efficient refrigeration at a fraction of the operational cost and electrical demand of traditional systems. Adoption of this cooling technology by small- to mid-size supermarkets would enable significant cost savings for electricity. On a broader scale, widespread implementation of this innovation across a variety of refrigeration applications would reduce greenhouse gas emissions and energy consumption, meeting the need for sustainable technologies to support food and energy security while upholding climate goals.
This SBIR Phase I project develops a renewable, closed container cooling system incorporating a rechargeable vacuum insulation and a novel two-stage heat pump. This project involves 1) developing a rechargeable vacuum insulation technology that does not use vacuum pumps; 2) creating a modified dewar connected to a fully renewable cooling system enabling cooled inner chambers; and 3) evaluating the ability to use environmental heat to restore the solutions that power the cooling system allowing their reuse. The novel heat pump technology is based on two asynchronous stages: the first utilizes concentration gradients as a driving mechanism enabling thermal transfer, while the second uses environmental heat to re-concentrate saline solutions after dilution during stage one. Together, these two stages enable cooling of a closed container. The project will examine system configurations able to generate large thermal gradients, enabling maintenance of 1.5-5°C temperatures within the dewar. Advantages of this system include 1) the system’s ability to cool continues in the absence of electricity making it resilient to power outages from itinerant weather, wildfires, and earthquakes; 2) lossless storage of cooling capacity; and 3) significantly reducing the greenhouse gas footprint compared to conventional refrigeration.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
L-Infinity Labs, Inc.
SBIR Phase I: Secure Image Recognition and Machine Learning Using Advanced Cryptography
Contact
378 EDMANDS RD
Framingham, MA 01701--3068
NSF Award
2304348 – SBIR Phase I
Award amount to date
$274,356
Start / end date
09/01/2023 – 05/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be a significant step towards resolving the access vs. privacy dilemma of the big data era. The use of people’s biometrics, internet traffic, and financial, medical, and genetic data can enable better crime prevention, targeted ads, and health innovation, but at the expense of privacy. Data may also be too sensitive to be given to third parties. The immediate impact of adopting this technology will be greater security for sensitive image data with easier access to useful inferences. The solution will shift the paradigm of institutions storing sensitive data onsite to one in which even sensitive data is stored and accessed in the cloud. With the capability of private outsourced data analysis will come a marketplace for computational tasks, including machine learning as a service, that will spur research and deliver better results to patients and clients faster and without risk of exposure.
This Small Business Innovation Research (SBIR) Phase I project will adapt existing Deep Neural Network models to use a fully homomorphic encryption scheme to perform image classification on encrypted images. The primary challenge is to reduce the computational overhead of operations on encrypted data to make the scheme practical at desired levels of accuracy and security. The proposed research and development addresses this challenge through innovation in machine learning, computational number theory, approximation theory, and computer science. The goal of the proposed research and development is to demonstrate the commercial viability of secure image recognition by achieving a reasonable level of security, accuracy, and server cost. The team will experiment in training and testing modified convolutional neural networks (CNNs) for image classification using carefully chosen activation functions and/or approximations to the testing function, and simultaneously building onto existing homomorphic encryption libraries new functionality to compute these operations homomorphically.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LABFORINVENTION CORP
SBIR Phase I: Scalable Manufacturing Technology for Mobile Signal Penetrating Energy-Efficient Low-Emissivity Windows
Contact
3711 YALE WAY
Fremont, CA 94538-
NSF Award
2233675 – SBIR Phase I
Award amount to date
$273,985
Start / end date
04/01/2023 – 12/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be low-emissivity windows capable of allowing wireless signals to pass through. Presently, almost all commercial low-emissivity windows severely block mobile signals from entering buildings - a serious problem in today's world that demands fast and uninterrupted mobile connectivity. The proposed low-emissivity windows can be manufactured cost-competitively and significantly reduce the costs of installing indoor cellular aids such as routers and antennas. This technology will accelerate the further adoption of low-emissivity windows to reduce greenhouse gas emissions as well as support the further deployment of 5G technologies.
This SBIR Phase I project proposes to investigate scalable manufacturing of mobile, signal transmissive, low-emissivity windows by using lithographic deposition of photoresist structures on the glass substrate prior to low-emissivity vacuum coating. The project proposes to deposit photoresist structures with non-conductive spacers on glass substrate, reducing the low-emissivity coating’s electric conductivity that prevents wireless signal passthrough. Dielectric layers will be deposited over the sidewalls to form protective layers over the low-emissivity coatings to protect against oxidative corrosion. The width of the photoresist structures will range between 2 to 5 micrometers to minimize the degradation of low-emissivity coatings to less than 2%, to maintain thermal performance. After low-emissivity coating deposition, the non-planar spacers will subsequently collapse into the photoresist layer restoring low-emissivity coatings to their original planar position thereby rendering the photoresist structure invisible to the eye. Furthermore, the team will explore additional photoresist designs to address potential lighting diffractions that may form on the finished window.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LABRADOR SYSTEMS, INC.
SBIR Phase I: Assistive Robots for Personal Care and COVID-19 Protection
Contact
5111 DOUGLAS FIR RD
Calabasas, CA 91302--1440
NSF Award
2036684 – SBIR Phase I
Award amount to date
$255,756
Start / end date
01/01/2021 – 03/31/2024 (Estimated)
NSF Program Director
Errata
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This is a COVID-19 award.Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project advances the state-of-the art of an emerging class of vision-based, autonomous navigation technologies to open new possibilities for low-cost/high-performance personal assistive robots. The robotics solution enables mobility-impaired individuals to have more agency over their environment and enjoy a higher quality-of-life. This helps address the severe shortage of caregivers for the elderly and post-acute care patients by empowering individuals to maintain their independence, extending the impact of caregivers, and reducing the cost of care in both home and facility settings. Additionally, by providing affordable and reliable isolation support in COVID-19 care settings, the proposed solution can help decrease the financial burden and increase the public health outcomes associated with COVID-19 disease management. The core robotics solution has an immediate addressable market of 11 million high-needs users in the U.S. alone, with projected revenues of roughly $1.65 Billion five years after product launch. Further commercialization opportunities come from licensing parts of the developed navigation technology for other robotics applications and developing an ecosystem of complementary products around the core robotics solution.
This Small Business Innovation Research Phase I project seeks to enable a new generation of assistive service robots that are comparable to commercial robots in performance, but significantly more affordable for individual use and personal care applications. The innovation adopts emerging visual positioning technologies from Augmented Reality to enable robust navigation for mobile robots using low-cost, consumer-grade electronics, while addressing a key limitation of visual positioning systems namely, that external lighting conditions and other changes in an environment can dramatically impact their performance. The innovation addresses these challenges via a combination of hardware and software that learns and stabilizes the highest value visual elements of the environment to maintain persistency across lighting conditions and long periods of time — a development critical to making assistive robots cost-effective for adoption at a large scale. Research objectives include: fully developing and integrating the visual persistency system, to achieve accurate and replicable robot navigation performance across a representative range of lighting conditions and visual characteristics of the target operating environments and benchmarking the resulting solution against state-of-the art technologies, to demonstrate its superior performance (i.e., it can successfully localize in at least 90% of cases where other solutions fail).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LAKRIL TECHNOLOGIES CORPORATION
STTR Phase I: Effect of Alcohol Concentration on Lactic to Acrylic Selectivity and Deactivation Rate over Na-FAU Zeolite Catalysts
Contact
2225 W. HARRISON ST. STE 102
Chicago, IL 60612-
NSF Award
2151176 – STTR Phase I
Award amount to date
$256,000
Start / end date
04/01/2023 – 03/31/2024 (Estimated)
Errata
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Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to commercialize a process to produce low carbon, bio-based acrylic acid and acrylates for paints, coatings, adhesives, and superabsorbent polymer markets. This $10 billion market has been seeking a bio-based, low-carbon alternative for decades, with economics serving as the major barrier. This project’s high yield lactic-to-acrylic technology will help grow the corn utilization industry and provide high paying jobs across rural America as well as economic competitiveness in global markets. Every plant constructed to convert lactic acid fermented from sugar into drop-in, low-carbon, renewable acrylic acid is estimated to bring $12 million in economic activity to a county with significant bio-based sugar crops (e.g., corn, beets, cane). Approximately 250 plants are needed at this scale to convert today’s petrochemical acrylic acid to a bio-based source.
This STTR Phase I project proposes to utilize high alcohol content feedstocks using catalyst innovation comprising engineered amine treatment of cation-exchanged zeolites. The catalyst has been shown to outperform numerous previously-utilized catalysts with >90% yields of acrylic products during catalyzed dehydration of lactic acid. So far, those in the field have investigated feeds containing aqueous solutions of methyl lactate or lactic acid in concentrations from 10-40%. Methanol has not been used as a co-feed in concentrations greater than 5%, therefore this project will investigate higher concentrations of alcohols (5%-80%, using methanol or ethanol). The project will use both methanol and ethanol as the alcohol size is likely to alter active site reactivity and/or shift the equilibrium of esterified versus acidic acrylate products. These experiments address two areas of concern in the commercialization of the technology and determine the relative dehydration rates of ethanol and lactic acid under competitive adsorption conditions to provide foundational information required for reactor scaling. The proposed research also increases the percentage of alkyl acrylates formed by forcing the gas phase esterification of acrylic acid with alcohol towards completion.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LATERAL.SYSTEMS LLC
SBIR Phase I: Cultivation Assistant Platform
Contact
3121 S MOODY AVE
Portland, OR 97239--4505
NSF Award
2233444 – SBIR Phase I
Award amount to date
$275,000
Start / end date
05/15/2023 – 10/31/2023 (Estimated)
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide indoor farmers with data insights to reduce risks and improve efficient use of resources to grow nutrient dense food. The value proposition is a first-to-market, off-the-shelf smart water quality and atmospheric conditions monitoring platform to support controlled environment agriculture, currently a $4.6 billion market. The proposed on premise smart service will focus on precision agriculture controls and advance state of the art in agriculture technology using a modular approach to enable plugging in multiple sensors into a unified system to capture water and air quality data at routine intervals around the clock. The temperature, pH, and dissolved oxygen measurements will be used to identify healthy conditions, display trend analyses, predict and diagnose imbalances, and provide alerts, alarms, and message notifications to guide coordination of efforts to regain system balance. The target markets are the fast-growing U.S. aquaponics and hydroponics industries.
This Small Business Innovation Research (SBIR) Phase I project involves development of an Internet of Things (IoT) platform service to overcome legacy issues slowing the digital transformation in indoor agriculture. Existing solutions do not provide an open platform approach addressing the handling of open data due to proprietary application interfaces and closed data schemas to fuse disparate dataset sources. The measured data will be fused (i.e., overlaid and triangulated) to monitor relationships between measured parameters. The scope of the project is to develop key features in preparation for field testing. The scientific approach will leverage inherently heterogenous and complex edge technologies. Data connectivity with third party vendor modular sensor arrays, microcontrollers, microservices, and actuators rely on many different operational technology communication standards — the proposal will overcome these challenges with hardware and software solutions that will integrate microservices within a container to enable application portability and simplify application deployment and orchestration. A standard open framework will be used to facilitate interoperable software applications and value add services utilizing industry standard protocols and message structure interfaces. This solution will enable third party module integration to connect into a unified, contained, store-and-forward platform involving open source and proprietary add-on modules to enable monitoring and analysis.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LEAFICIENT INC
SBIR Phase I: Improving indoor agriculture grow light efficiency with adaptive light shaping
Contact
163 TUNNEL RD
Evans City, PA 16033--9378
NSF Award
2304339 – SBIR Phase I
Award amount to date
$274,525
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in accelerating the transition to a more sustainable food supply chain, which could democratize access to healthy foods and lessen food insecurity. The US system for growing and distributing fresh food is inefficient and insufficient in the context of climate change. Controlled environmental agriculture (CEA) is seen as a potentially revolutionary way to supply food demands with limited resources. Despite this promise, the high energy requirements of creating lighting and cooling in enclosed environments to support photosynthesis has caused CEA not to realize its promise. In the project, a novel solution to improve energy efficiency in CEA farms is proposed where advanced optics, machine learning, and computer vision are used to ensure that all of the light that is emitted by synthetic light sources is optimally used for plant photosynthesis and growth. The project offers a plausible way to create reliably profitable operations for CEA producers which would lead to enhanced access to fresh produce for consumers and decreased reliance on conventional agriculture to meet the world’s food needs.
Within current commercial grow systems for controlled environment agriculture, a substantial portion of the photons are wasted as they are not incident onto photosynthetically active biomass and are absorbed by the surrounding grow rack and media. This project will prototype and systematically test a light production system that dynamically shapes light such that it is rendered only onto the photosynthetic areas of the plant. To accomplish this, the project will develop and evaluate (within three crop varieties) a closed-loop system to autonomously detect the three-dimensional shape of the growing plant and dynamically adjust the light intensity and projection area to optimize power efficiency and biomass growth. Successful completion of the work in this project will result in a novel technology that is systematically tested to yield similar quality produce using a fraction of the energy consumption of current state-of-the-art systems. Deployment of this technology would help to improve the unit economics of controlled environment agriculture produce items and accelerate adoption of controlled environment agriculture farming practices that potentially consume less water, utilize land resources more efficiently, and eliminate the need for chemical pesticide/herbicide treatments.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LEAP PHOTOVOLTAICS INC.
SBIR Phase I: Low-cost Domestic Additive Manufacturing for Silicon Solar Cells
Contact
3564 18TH ST
San Francisco, CA 94110--1624
NSF Award
2212740 – SBIR Phase I
Award amount to date
$255,886
Start / end date
03/15/2023 – 11/30/2023 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of the Small Business Innovation Research (SBIR) Phase I project is to demonstrate the feasibility of producing high-efficiency, low-cost, crystalline silicon photovoltaic solar cells without using silicon wafers. For the first time, additive manufacturing processes will be applied to silicon in order to produce equivalent performance to silicon wafers without the wasteful processes used in current manufacturing. If successful, this additive approach can link the parts of the solar supply chain that still exist in the United States—silicon refining and solar module assembly, establishing a full domestic supply chain for this critical energy technology. This supply chain can: (a) be built with off-the-shelf equipment at a third the cost of building traditional silicon wafer and cell factories, (b) cut the cost of photovoltaic solar cell manufacturing in half compared to imported silicon wafer-based solar cells, and (c) reduce energy consumption in solar cell manufacturing by 70% and reduce water consumption by 90%. This combination of low factory and production costs can drive the growth needed in the solar industry to support the nation’s decarbonization goals while creating tens of thousands of domestic jobs.
This SBIR Phase I project seeks to demonstrate the feasibility of a novel architecture and additive manufacturing process for crystalline silicon photovoltaic solar cells that provide equivalent performance to traditional silicon wafer-based solar cells at lower cost with a local supply chain. The steps in the process flow are adapted from traditional solar cell processing or adjacent industries like microelectronics, but they are being combined in new way to realize this solar cell design. These steps will be co-optimized to produce high-efficiency cells using a series of designed experiments. These processes typically fall into three categories: (1) chemical or physical vapor deposition, (2) solution-based coating, and (3) thermal annealing, with their own relevant process variables: (a) time, temperature, pressure, gas flow rates, and magnetic power; (b) solvent, solution concentration, coating gap, and coating speed; (c) temperature vs. time. These process variables for each step will be correlated to physical properties of the layers in the cell stack such as thickness, stoichiometry, and performance of the finished cells to produce a prototype with performance that is compelling to investors, partners, and customers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LEARN COLLABORATE INC.
STTR Phase I: Enabling Student Project Collaboration with Artificial Intelligence Augmented Mentorship
Contact
11220 MOORPARK ST.
Studio City, CA 91602--2659
NSF Award
2243452 – STTR Phase I
Award amount to date
$274,927
Start / end date
03/15/2023 – 11/30/2023 (Estimated)
Errata
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Abstract
The broader/commercial impact of this Small Business Technology Transfer Phase I project is in improving both student learning and workforce readiness through interdependent learning experiences. The project will create project-based environments that promote skills such as communication, critical thinking, problem solving, time management, creativity, and teamwork – all mirroring professional work environments. The technology will also promote development of skills such as project management, writing, business and data analysis, design, and presentation. Project collaboration requires that students interact frequently throughout the project completion process, including frequent mentor or teacher interactions. Such an interdependent environment creates a real-world dynamic that better prepares students to enter the workforce. The platform developed by this project is likely to create significant societal impact while participating in the fastest growing e-learning sector.
The proposal seeks to develop a collaborative community platform using proprietary project collaboration models integrated with Artificial Intelligence (AI) augmented mentorship to enhance student workforce readiness. The technology will be designed to provide the right piece of information to the students and mentors at the right time. By analyzing and unifying all the content under a domain-specific semantic representation, the system will be able to aggregate and organize all the content and identify the piece for intervention that is contextually most useful. To make project collaboration and mentorship easier between students and mentors in a trustworthy manner, modeling will be done utilizing minimal supervision. This modelling will include combining contextual embeddings from language models with graph-based neural networks to capture interactions across multiple facets. The technology will build upon explainability of deep neural networks to provide an appropriate level of transparency into the decision making, both for the users to learn to trust the platform, as well as for the platform developers to build systems that aid in reliable, trustworthy, and fair mentoring.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LEARNING NETWORK, LLC, THE
SBIR Phase I: College Bound Video Game
Contact
1915 NATCHEZ TRCE
Allen, TX 75013--4873
NSF Award
2225635 – SBIR Phase I
Award amount to date
$275,000
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impacts of this Small Business Innovation Research (SBIR) project will result from the production of a useful game, called College Bound, that will entice youth to use their gaming time to learn how to navigate the college preparation, college-admissions, and financial-aid processes. This assistance could help in addressing the problem of an inadequate number of counselors in high schools that serve large minority populations. Studies have shown that underserved high school students see a counselor for less than 20 minutes a year. Since is also known that young people between the ages of 8-18 spend 7.5 hours a day engaged with media- either playing video games or watching television a game platform may enable minority students, who make up less than 4% of undergraduate enrollees in the national four-year colleges, to prepare for college attendence. The College Bound game seeks to recoup some of this screen time for positive benefits such as improving preparedness of the minority students to access higher education, and meeting future workforce demands in Science, Technology, Engineering, Mathematics, and Medicine (STEMM).
This Small Business Innovation Research Phase I project focuses on assisting underrepresented students in learning about what it takes to get college admission, how to prepare for it, and what financial aid is available to afford the education using a gaming environment. The project will focus on measuring students' knowledge about the college application process as the player navigates through different levels of the game. Knowledge about the college application process includes a) information about admission criteria and deadlines, b) the college acceptance and enrollment processes, and c) the ability to pay for college through scholarships, loans, work programs, and/or personal savings. Throughout the pilot phase, the project will develop reliable and valid evaluation tools to measure student learning in the game setting at each level of the College Bound game. These evaluation tools might reflect not only student learning performance but also boost college enrollment outcomes. These measures may also enable the school and other stakeholders to understand students' gaps and help strengthen students’ pathways into college.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LEARNINGCLUES, LLC
SBIR Phase I: Scalable, on-demand, research-based, help-seeking innovation for learners in virtual and recorded training programs
Contact
2019 MARRA DR
Ann Arbor, MI 48103--6187
NSF Award
2151406 – SBIR Phase I
Award amount to date
$256,000
Start / end date
01/15/2023 – 12/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a scalable, on-demand, research-based innovation for learners in virtual and recorded training programs. Research has shown that underrepresented populations arrive at an undergraduate institution less likely to have the advanced study skills or confident awareness to seek assistance when faced with uncertainty in the classroom. This project provides on-demand responses to in-lesson queries and helps develop deep study skills. Additionally, for the students who arrive at college without sufficient familial or academic support, the proposed product becomes a resource to participate successfully in an undergraduate environment.
This Small Business Innovation Research (SBIR) Phase I project will build an engine that automatically identifies the discipline of a course based on the extracted words and phrases spoken or visually presented in class, and then will identify terms important to the learning objectives of the course, garnered via categorical arrays of discipline specific keywords. Automatic creation of personalized study guides is initiated based on the learners' individual queries or class discussions. This innovation is applicable to the remote learning industries, where it may increase the value of their customers’ online content and enable teaching professionals to strive for higher levels of equity in the student population.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LEVIOSA TECHNOLOGIES LLC
SBIR Phase I: Hybrid Computing Techniques for Quantum-inspired Ising Machines
Contact
9955 ARROWWOOD TRL
Woodbury, MN 55129--7537
NSF Award
2233642 – SBIR Phase I
Award amount to date
$275,000
Start / end date
05/01/2023 – 10/31/2023 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from the development of a high-speed, low-power solver based on standard semiconductor technologies that can directly solve combinatorial optimization problem (COP) problems. COP problems have broad applicability across manufacturing optimization, semiconductor wire routing, logistics planning and execution, and financial portfolio management. However, COP problems are notoriously difficult or even impossible to solve via classical computers on a scale suitable for commercial application. Current state-of-the-art COP solvers need tremendous computing power, are unsuitable for edge computing, rely on undeveloped technology, or use similar algorithms to classical computers. The goal of this project is to provide consumers with a drop-in replacement COP solver that is faster, more precise, more mobile, and more energy efficient than state-of-the-art classical computers and COP solvers.
This Small Business Innovation Research (SBIR) Phase I project seeks to develop a complementary metal–oxide–semiconductor (CMOS) based parallel combinatorial optimization problem (COP) computing cluster accessible through a cloud interface. The proposed solution will directly solve COP problems, increasing the speed, precision, and power efficiency compared to classical computers or current quantum-based COP solvers. Additionally, the cluster uses all standard semiconductor technology allowing for near-term hardware manufacturing, unlike current quantum computing competitors. The device also works at room temperature, making it the only suitable edge device for directly solving COP problems. A hybrid computing algorithm combining the custom and classical methods will parallelize the COP solving across numerous custom chips. Many custom chips will be combined into a single cluster to maximize the speed and efficiency of the hybrid algorithms. An introductory cloud service interface will be incorporated with the custom computer cluster to facilitate outside access. The end goal of this project is to create a state-of-the-art scalable, accessible, and economical COP solver that overcomes the inherent disadvantages of quantum and classical computing.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
LIGERO, INC.
SBIR Phase I: Lightweight Scalable Auctions for Decentralized Martketplaces and Beyond
Contact
75 CRESTVIEW DR
Pittsford, NY 14534--2244
NSF Award
2212788 – SBIR Phase I
Award amount to date
$252,754
Start / end date
05/15/2023 – 12/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to increase the security and privacy controls in today's financial (crypto or traditional) exchanges and auction platforms. Currently, such exchanges suffer from information leakage, meaning that third parties can see aspects of the bidding or auction processes and this ability allows them to gain an advantage or unfairly outbid (i.e. front-run) the competition. A related issue is the lack of transparency in exchanges, which often results from their efforts to limit information leakage. The lack of transparency has led to significant issues at several centralized exchanges. This project proposes to build an innovative decentralized exchange that will empower traders with a fair and efficient marketplace. The proposed solution will employ state-of-the-art cryptographic protocols based on secure multiparty computation (MPC) to guarantee the necessary confidentiality without compromising transparency. The project is not bound by the tradeoff between security and transparency - it will seek to provide both in an efficient system. Furthermore, the tools developed under this project will have potential beyond the financial sector, including improving methods to securely and privately operate with sensitive data.
This SBIR Phase I project investigates the design of a decentralized marketplace by scaling secure multiparty computation (MPC) to a large number of parties. MPC provides a decentralized solution for executing an auction that is automatically front-running resistant as the protocol only allows the winning bid (or bidder) to be revealed to anyone. The proposed solution will address a key challenge of developing a concretely efficient mechanism to execute the MPC off-chain and certify the results on-chain with minimal transaction fees while guaranteeing maximal security. The proposed solution will take advantage of quantum secure primitives and star network topology to get robust security and minimal communication overhead. In this project, the first application to validate the designed auction MPC will be a decentralized Non-Fungible Token (NFT) marketplace and instantaneous withdrawal mechanism from L2 to L1.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MACULA VISION SYSTEMS INC.
SBIR Phase I: Automated Gram Stain Interpretation Via Digital Holographic Microscopy
Contact
11630 N DRAGOON SPRINGS DR
Tucson, AZ 85737--9761
NSF Award
2321453 – SBIR Phase I
Award amount to date
$275,000
Start / end date
08/01/2023 – 04/30/2024 (Estimated)
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the improvement in hospital laboratory results by automating and standardizing a manual test procedure that is critical for the diagnosis of infections. Faster, higher quality and less expensive test results will help to ensure that patients receive timely treatment with the appropriate medications, reduce employee burnout through the automation of tedious laboratory tasks, and lower healthcare costs. Just as automated blood analyzers largely replaced manual blood cell counting, the proposed platform will eventually replace manual counting of bacteria and yeast cells. The new imaging technology developed as a part of this project will generate advanced manufacturing jobs and increase the economic competitiveness of the United States by introducing an innovative product to a market dominated by foreign firms. Long-term benefits to society include decreased antimicrobial (medicines that kill microorganisms) resistance as infections are managed with fewer unnecessary antimicrobials.
This Small Business Innovation Research (SBIR) Phase I project involves the development of a novel microscopy platform for automating the interpretation of tests performed in hospital laboratories. This project fills an important gap specific to microbiology labs that are facing a trained labor shortage without affordable automated alternatives. Microbiology labs in hospitals are responsible for examining patient samples (e.g., urine and blood) for the presence, type, and quantity of microscopic organisms (e.g., bacteria and yeast). This project includes the engineering of a special light source, a customized camera, and a suite of artificial intelligence (AI)-enabled algorithms to analyze the microscopic images captured by the camera. Once the platform is built, its performance will be evaluated on real patient samples to demonstrate the feasibility of the technology by comparing it to experienced human lab technicians. The project will measure how often the platform produces the correct answer as well as how long it takes. The platform will also be tested in terms of image quality to demonstrate that it can take pictures of the smallest bacteria and accurately capture color.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MAGMA SPACE LLC
SBIR Phase I: Semi-active magnetic bearing for flywheel energy storage systems
Contact
80 M ST SE STE 100
Washington, DC 20003--3550
NSF Award
2222161 – SBIR Phase I
Award amount to date
$274,995
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to implement a high-efficiency, low-power magnetic bearing that will enable the successful development of high-speed flywheel energy storage systems (FESS) both for space and terrestrial applications. FESS are mechanical batteries that overcome some of the limitations of lithium-ion batteries, such as the loss of energy capacity over time and the need for stringent temperature control. In space, FESS could reduce the overall mass associated with the battery pack and extend the mission life of Low Earth Orbit (LEO) satellites. On earth, FESS can take over some of the applications that are required to deliver high power for a short amount of time, such as electric vehicle charging stations or hospital back-up power units. Ultimately, FESS will help alleviate the demand for lithium-ion batteries while providing reliable, long-lasting energy storage.
This Small Business Innovation Research (SBIR) Phase I project will demonstrate the feasibility of integrating the proposed magnetic bearing into a carbon-fiber flywheel. The complexity of this task comes from having the three main parts of the flywheel (composite rim, metal core, and magnets) created using different manufacturing processes. The magnetic materials need to be protected as they will not withstand the high speeds of FESS. De-risking this manufacturing process is crucial in continuing the development of this technology and in scaling up. Another challenge is that the high speeds of FESS are expected to cause high gyroscopic torques during satellite maneuvers. Therefore, investigating ways to increase bearing stiffness (e.g., by changing magnet size and position, or modifying coil shape), while assessing the effect of gyroscopic torques through numerical models, will be paramount. Finally, the magnetic bearing has the distinctive feature of being able to tilt the flywheel (within its gap tolerances), without requiring an external gimbal actuator. This feature could possibly allow the technology to be used for dual purposes, and its implications will be investigated at a system level.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MAMMAE BIOSCIENCES, INC.
SBIR Phase I: Development of an enzymatic method to produce compounds found in human milk at commercial scale
Contact
3500 S DUPONT HWY
Dover, DE 19901--6041
NSF Award
2304250 – SBIR Phase I
Award amount to date
$271,443
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to assess an affordable enzymatic method for producing marketable natural compounds usually found in human milk. Once this strategy is validated, it could be implemented by domestic and industrial users. The technology is capable of transforming the lactose in milk to highly desirable compounds. These rare and valuable compounds can help enhance growth of beneficial gut bacteria and are building blocks of human breast milk. These compounds are widely desirable to fortify infant food, where they play a role in intestinal health, supporting balanced gut microbiota, benefiting immunity, and improving cognitive brain health. Furthermore, new research suggests that affordable technologies for food fortification containing stable bioactive natural compounds will benefit the healthy gut beyond infancy and across life stages. As such, this technology opens new business opportunities for food and dietary supplement manufacturers aiming to develop unique gut-strengthening nutrition solutions.
The proposed project will enzymatically generate compounds found in human milk, including N-acetylglucosamine (LacNAc). The research plan consists of testing the scalability of enzyme β-hexosyltransferase (BHT) production, which will be heterologously produced by K. phaffi. To validate industrial scalability, product generation, and yields from 100 L working volume bioprocessing reactors, the BHT generated will be utilized to catalyze the repeated addition of galactose to N-acetylglucosamine (GlcNAc). The products, in addition to galactooligosaccharides, will include LacNAc disaccharides, generated by sequential transgalactosylation reactions. The recovered products will be tested in preclinical safety/toxicity studies. Data collected during this study will allow for a more precise cost-benefit analysis, which will include product yields, carbon balances, microbiome benefits, and metabolic data. Cost savings are expected from a more efficient enzymatic biosynthesis method for producing LacNAc due to BHT specificity, synthesis in one-step reactions, low-cost substrates, sustainability, and overall low environmental impact.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MARWELL BIO INC.
SBIR Phase I: Advanced Deep Learning Technologies for Designing Humanized Antibody
Contact
470 NOOR AVE #1011
South San Francisco, CA 94080--5957
NSF Award
2304624 – SBIR Phase I
Award amount to date
$274,822
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact / commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to accelerate antibody design and engineering through the development of proprietary computational approaches. Compared to conventional antibody drug development approaches that are often lengthy, costly, and inefficient, this innovation may offer a more efficient and cost-effective alternative. The proposed approach aims to create better therapeutic-grade antibodies while unlocking novel antibody design possibilities. The market opportunity addressed by the proposed technology is significant, as the global therapeutic antibody market for cancer and infectious diseases is projected to reach $235 billion by 2028. This project has the potential to transform the field of antibody discovery and provide new therapeutic options for patients.
This Small Business Innovation Research (SBIR) Phase I project aims to develop an artificial intelligence (AI)-based platform that efficiently designs novel antibody drug candidates with possible lower toxicity and immunogenicity risks. The research will involve developing novel and proprietary AI based models to create best-in-class antibody therapeutics and validate them through state-of the-art in-silico experiments. To successfully complete this Phase I project the company plans to: a) develop a novel computational model to design antibody hit sequences, b) demonstrate the scalability of the proposed computational model in designing antibody hit sequences against diverse targets, c) assess biological values of the antibody hit sequences predicted by the computational model. The expected technical outcomes involve a more rapid and efficient process for designing therapeutic antibodies, resulting in lower development expenses and a quicker path to market. The AI technologies have the potential to design the most promising therapeutic antibodies to treat infectious diseases and cancer in months rather than years, reducing the time and resources needed for the pre-clinical development of therapeutic antibodies.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MATMERIZE, INC.
SBIR Phase I: A Physics-Informed/Encoded Polymer Informatics Platform for Accelerated Development of Advanced Polymers and Formulations
Contact
850 NEW BURTON ROAD
Dover, DE 19904--5786
NSF Award
2322108 – SBIR Phase I
Award amount to date
$273,706
Start / end date
10/01/2023 – 09/30/2024 (Estimated)
Errata
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Abstract
The broader/commercial impacts of this Small Business Innovation Research (SBIR) Phase I project are to transform the way in which polymeric materials are developed. Adopting the most advanced artificial intelligence (AI) techniques, the proposed technology seeks to dramatically accelerate the exploration of new polymer formulations, efficiently and accurately discovering those with targeted performances and applications, and ultimately minimizing the time and the cost needed to develop new and superior functional materials. This technology will enable the targeted development of polymers for specific applications such as packaging or energy storage, while ensuring full recyclability. New polymer designs of this type can help alleviate the current global problem of plastic waste. Given that polymers are one of the most important classes of materials in use today, the impact of this SBIR Phase I project is expected to be significant and far-reaching.
This Small Business Innovation Research (SBIR) Phase I project aims at transforming the state-of-the-art AI-based technology currently used to discover and design functional polymers. Since the beginning of polymer informatics about a decade ago, this AI-based approach has quickly become a powerful tool to design new functional polymers. At the center of this technology are the machine-learning models, trained on past data and used to evaluate the polymeric materials yet to be synthesized. Currently, the models are developed by purely “learning” the available datasets independently, ignoring numerous physics-governed correlations across data of different polymer classes and properties that come from different sources. Without proper awareness, the models can easily violate the relevant physic rules and render unphysical results, especially when the training data are not sufficiently large. In this project, the company will develop two deep learning architectures in which known and important physics-governed correlations are secured. The architectures will be the most advanced deep learning tools to combat the small and sparse data problems that are very common in and important for polymer informatics. The new technology is expected to significantly transform the development and deployment of functional polymers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MEALMATE INC.
SBIR Phase I: A Deep-learning-based Chatbot and Personalized Recommendations: Application to Nutrition
Contact
6516 W 87TH PL
Los Angeles, CA 90045--3726
NSF Award
2213316 – SBIR Phase I
Award amount to date
$256,000
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
Errata
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Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to advance the health and welfare of the American public. Obesity among American adults has risen from 12% in 1990 to over 40% today, leading to an estimated medical cost of $260 billion in 2016, according to the Center for Disease Control (CDC). According to the National Institute for Health (NIH), 70% of American adults were overweight or obese in 2014. In 2013, American adults were spending $60 billion annually on weight loss, according to US News & World Report. A 2008 American Journal of Preventive Medicine study showed that those who kept daily food journals lost twice as much weight as those who did not. However, existing diet tracking methods are often too time-consuming for maintaining long-term weight loss. A personalized artificial intelligence (AI) chatbot could make food logging fun and easy, benefitting millions of Americans who are trying to lose weight and furthering knowledge on spoken dialogue systems.
This Small Business Innovation Research (SBIR) Phase I project will advance knowledge in the field of spoken dialogue systems in several ways. First, the project establishes a new research area by noting that AI and spoken dialogue systems have yet to be applied to nutrition. Typically, conversational agents focus on factual question answering or tasks such as flight booking, but there is an opportunity to leverage big data for learning relationships between diet and health. Second, this project will develop a neural generative chatbot model with memory, demonstrating the benefit of personalized conversational interactions with intelligent agents that remember the history of conversations and personal details about the user. While manually writing chatbot responses ensures more control over the output, the drawback is that the responses are less interesting, diverse, and flexible. This work proposes generative Transformers in order to generate more realistic, human-like responses and knowledge graphs as a novel method for remembering the conversation and diet tracking history of each user for personalized feedback. Finally, this project proposes the application of causal inference, often used for medical diagnosis, to the new, challenging task of predicting which foods lead to outcomes such as gut symptoms, weight loss, or muscle building.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MECHANO THERAPEUTICS LLC
STTR Phase I: Mechanically Controlled Drug Delivery Platform for Joint Environments
Contact
3401 GRAYS FERRY AVE
Philadelphia, PA 19146--2701
NSF Award
2304235 – STTR Phase I
Award amount to date
$275,000
Start / end date
06/15/2023 – 05/31/2024 (Estimated)
Errata
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Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project seeks to address the strong clinical need for a single injection/dose sparing delivery system that can safely release therapeutics in the joint space over time in a controllable dosing manner for sustained symptomatic relief. Early and efficient treatments that mitigate inflammation are becoming increasingly critical to ease the care and cost burdens associated with musculoskeletal conditions, which impact 1.71 billion people globally. The proposed platform, which can be applied to a wide variety of drugs, including small molecules, proteins, and biologics will address the market need for improved drug delivery systems by providing a tunable drug delivery system that is responsive to different degrees of mechanical force created by different movement types. The solution will allow for more precise delivery of drugs when and where they are needed. This feature will translate to fewer injections, fewer systemic side effects, and overall improved drug efficacy compared to current offerings, in turn providing improved patient quality of life and outcomes. The proposed mechano-activated drug delivery platform is expected to have a major impact in controlling musculoskeletal diseases by improving efficacy of Food and Drug Administration (FDA)-approved treatments and enabling new therapeutic strategies.
This Small Business Technology Transfer (STTR) Phase I project seeks to develop a force-stimulated drug delivery system that uses the body’s natural physiological loading of musculoskeletal environments for controlled release of nearly any drug. The technology is based on the tunable rupture profile of proprietary mechano-activated microcapsules - translating to fewer injections, fewer systemic side effects, and overall improved drug efficacy. Preliminary work has demonstrated the ability of the microcapsules to encapsulate and release viable biological therapeutics upon mechanical force, to provide tunable mechano-activation thresholds, and to stay and rupture within a living joint. For this Phase I project, a proof-of-concept study will be conducted to establish the feasibility of the mechano-activated microcapsule drug delivery platform in a biological joint environment. This study will be accomplished by evaluating the anti-inflammatory therapeutic effects of interleukin-1 receptor antagonist (IL-1Ra), a drug with established ability to inhibit acute joint inflammation, delivered via mechano-activated microcapsules in an established equine model of Interleukin-1-beta (IL-1beta)-induced acute joint inflammation, in comparison to soluble formulations. This study will provide a basis for investigation into more specific disease applications, models, and terminal outcomes where modification of the disease process over the long term can be evaluated.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MED DIMENSIONS, LLC
SBIR Phase I: Novel Artificial Intelligence (AI)-Mediated Orthopedic Implant Design and Selection
Contact
318 TIMOTHY LN STE 2
Ontario, NY 14519--9022
NSF Award
2213118 – SBIR Phase I
Award amount to date
$254,580
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is customized dental implant solutions for animals affected by traumatic jaw injury and disease. Current veterinary implants utilize platforms designed for human use, despite a greater variability in anatomy, bone strength, and procedural requirements. This approach has subsequent shortcomings accounting for anatomical and physiologic variations resulting in trauma, neovascular damage, incomplete healing, and failure to improve or restore functionality. The project proposes a software program and system for generating customized implant designs and criteria based on the specific anatomy and procedure. A successful project would improve veterinary care and the lives of pets and service dogs following injury.
This Small Business Innovation Research (SBIR) Phase I project aims to develop a Machine Learning (ML) software to incorporate bone density screening with 2-dimensional to 3-dimensional reconstruction software to create anatomically derived implants and software guided implant placement. The proposed ML system will identify key anatomical considerations and animal classifications for optimizing implant designs and procedural criteria. By integrating a database of appropriate animal scans, the ML software will identify the optimal regions for implant and refine as needed. The result of this Phase 1 is a database-derived algorithm which generates implant shapes and sizes that avoid anatomical features that interfere with proper placement.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MEMBRANEX, LLC
STTR Phase I: Three Dimensional (3D) Printed Mixed Matrix Membranes for Biogas Upgrading
Contact
119 LAWLOR RD
Tolland, CT 06084--3716
NSF Award
2223083 – STTR Phase I
Award amount to date
$275,000
Start / end date
01/01/2023 – 12/31/2024 (Estimated)
Errata
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Abstract
The broader impact /commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to derisk a new membrane manufacturing technology for biogas upgrading. Renewable natural gas (RNG) is attractive as a carbon neutral energy source as it is derived from organic wastes such as food waste, agricultural waste, and municipal biosolids. RNG is part of biogas and requires cost- and energy-effective separation from carbon dioxide. The company has developed an additive manufacturing technology that enables the fabrication of high performance membranes to enable lower cost biogas upgrading. These membranes offer energy and system design benefits over more traditional separations technologies for the production of RNG. The membrane printing approach may lead to the production of best-in-class membranes for RNG production and could enable carbon capture and utilization from biogas sources.
This Small Business Technology Transfer (STTR) Phase I project seeks to demonstrate the production of mixed matrix membranes (MMMs) comprised of polyether block amide polymer and zif8 zeolite. The proposed method uses electrospray based additive manufacturing to precisely control membrane thickness and zeolite loading, allowing customized membrane properties for biogas upgrading. Furthermore, the additive manufacturing, or printing, method enables increased loading of the zeolite over conventional casting techniques without the formation of defects that would lessen selectivity. The goals of the project include quantification of the maximum zeolite loading while avoiding loss of selectivity. The company will produce small membrane leaves up to 1 ft2 in area and demonstrate consistent performance across the leaf using mixed gas testing.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
METFORA LLC
SBIR Phase I: Detection of Chronic Diseases via Multiplex Analysis of Circulating Metabolites
Contact
13575 S SONOITA RANCH CIR
Vail, AZ 85641--8844
NSF Award
2212865 – SBIR Phase I
Award amount to date
$255,706
Start / end date
02/01/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a novel metabolite diagnostic blood test indicative of early-stage diseases including cancer, chronic heart and lung disorders, and diabetes. The technology analyzes specific panels of blood metabolites indicative of changes in cellular function that occur with disease. This technology reduces the diagnostic interval time from years to weeks or days, diminishes misdiagnosis resulting in improper therapy, and has the potential to alter the use of more invasive diagnostic methods such as biopsies, colonoscopies, and heart catheterizations from an exploratory to a confirmatory role.
This Small Business Innovation Research (SBIR) Phase I project aims to use specific panels of circulating metabolites measured using mass spectrometry (MS) and analyzed using artificial intelligence to detect chronic disease conditions. Pre-clinical models have demonstrated that alterations in metabolism occur at the stage of mild disease, before overt symptoms become evident. The first specific condition to be evaluated in this project is pulmonary arterial hypertension. The objective is to establish calibrated MS methods with quantified values of multiplexed metabolite panels in a reproducible manner as required for clinical use. Multi-label classifications will also be developed in order to detect and account for various co-morbidities that can affect overall metabolic changes.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MICROPURE GENOMICS INC.
SBIR Phase I: Rapid, End-to-end Sample Preparation for Sequencing Applications
Contact
651 N BROAD ST
Middletown, DE 19709--6402
NSF Award
2222688 – SBIR Phase I
Award amount to date
$274,199
Start / end date
01/01/2023 – 12/31/2024 (Estimated)
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project consists of advancing methods for preparing deoxyribonucleic acid (DNA) for sequencing. Prior to sequencing a biological sample, DNA must be liberated from cells and separated from proteins and other unwanted debris, and then mixed with specialty buffers and chemical agents. This skilled task is currently carried out by trained scientists using largely manual manipulations of the samples and expensive equipment. The alternative method proposed in this project will speed-up diagnoses from genomic sequencing by significantly reducing preparation time while also making preparation more reliable through automation. Notably, the proposed approach is expected to prepare DNA without reducing its length; consequently, the process should be ideally suited for preparing samples for emerging long-read sequencing technologies. These improvements have the potential to decrease the burden and costs associated with DNA sequencing, hence expanding the benefits of DNA sequencing technology to wider segments of society.
This Small Business Innovation Research (SBIR) Phase I project relies upon a process for trapping genomic material in a small flow cell through which an electric field and pressure-driven flow are simultaneously applied. The process is highly selective towards strands of DNA or (ribonucleic acid) RNA; proteins and other, unwanted debris that enters the flow cell passes through. Also, the process is relatively gentle, so the length of sample should not be shortened as a result. This project will advance the technology to the marketplace by: (1) Completing cartridge design details and fabrication, including evaluation of material options; (2) Building a custom research and development platform for interfacing with the cartridges; (3) Developing methods for DNA sample extraction and transfer; (4) Developing the library preparation protocols using mixing and heating while toggling the Electro-Hydrodynamic Trapping; and, (5) Integrating the entirety of the preparation process into a single cartridge and validating the process performance.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MIDWEST ELECTRIC PROPULSION COMPANY (MEPCO)
SBIR Phase I: A compact, 3-level, high efficiency, 4-port, modular universal power conversion system with Internet of Things (IOT) using Wide Bandgap (WBG) devices
Contact
4212 N 76TH ST
Milwaukee, WI 53222--2002
NSF Award
2153880 – SBIR Phase I
Award amount to date
$255,920
Start / end date
05/15/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project focuses on developing a universal power conversion system that addresses the unmet needs of the fast-growing electrification and energy storage industries whether it is related to electric vehicles (EV), EV charging, EV charging infrastructure, grid storage, or electric boat applications. The proposed modular and scalable power conversion system is based on the latest generation of power semiconductor devices, silicon carbide (SiC), and can be used across many applications extending over wide power and voltage ranges. The project aims at making the system extremely compact and achieving extremely high efficiencies, which cannot be achieved by silicon-based systems. This modular system configuration can easily be adopted to develop medium voltage-based EV charging application which will be the future for the EV commercial semi trucking industry. Furthermore, due to modularity and scalability, system integration becomes easy and less time-consuming decreasing the cost and helping the adaptation of electrification.
This SBIR Phase I project proposes to develop a multi-input, multi-output modular, scalable, and highly compact wide bandgap-based, four-port universal power conversion system which can be applied to electric propulsion and other power conversion applications. Variants of this system are suitable in electric vehicle charging, grid-connected energy storage, distributed energy, and electric boat propulsion. The intellectual merits of the proposed research and development work is the highly compact and efficient nature with multiple power ports supported by a high frequency transformer switching at hundreds of kilohertz resulting in an anticipated size reduction of 50 times, and a weight reduction of at least 5 times compared to existing technologies. The proposed highly integrated, four port system is based on the combination of next generation wide bandgap gallium nitride and silicon carbide devices. Three ports of the four port system will include, the battery port, propulsion motor port/AC grid connection port and 12V-48 volt auxiliary port, realized by using SiC based 3-level power electronics building block (PEBB).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MOIRES INSTRUMENTS, LLC
SBIR Phase I: A Radio Frequency Quadrupole Stark Decelerator to Identify Isomers and Conformers and Measure their Effective Dipole Moments
Contact
805 BRANDON MILL CT
Elon, NC 27244--8300
NSF Award
2208750 – SBIR Phase I
Award amount to date
$256,000
Start / end date
05/15/2023 – 04/30/2024 (Estimated)
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will lead to a new type of analytical instrument and associated businesses, specializing in the identification and characterization of chemical isomers and their different conformational forms (conformers). Isomers are molecules with the same constituent atoms but arranged differently. The identification of isomers and their conformational forms is of critical importance to pharmaceutical and agrochemical industries since the metabolites of many medications or agrochemical compounds are often isomers of one another. Since some of these metabolites may be harmful, safety and efficacy studies require careful analytical “method development” work to quantitate their presence in clinical samples, soils, and foodstuffs. Unfortunately, current analytical methods for identifying molecular isomers are cumbersome, slow, and involve trial and error work – presenting a significant bottleneck to regulatory approval. The proposed technology seeks to provide a rapid and robust instrument for isomer analysis, dramatically reducing pharmaceutical and agrochemical development costs and extending patent exclusivity sales – while enabling the experimental identification of conformers for the first time. Access to this new information has the potential to transform agrochemical ($220 billion total addressable market (TAM) in 2022) and drug discovery ($82 billion TAM 2022) sectors, while generating new well-paying, high-tech jobs.
This SBIR Phase I project proposes to develop a novel mass spectrometer that works on neutral molecules rather than ions. It uses high electric fields to manipulate and distinguish molecules, separating them by the magnitude of their electrical polarity which, in turn, is highly sensitive to the molecule’s 3D shape. Molecules may be pushed or pulled by the fields depending on their orientations in the field and the magnitude of their polarity (“dipole moment”). Since molecular isomers weigh the same, their identification via mass spectrometry is complicated and typically requires time-consuming “method development” work. The proposed instrument aims to reduce this work by using dipole moments to distinguish all isomers and their conformers in a single spectrum – with an axis labeled by mass-to-dipole-moment ratio, rather than mass-to-charge ratio. This technology uses microfabrication techniques to miniaturize and planarize a previously demonstrated quadrupole device described in the academic literature, creating an array of microscopic quadrupole channels. The additional patent-pending deceleration feature, coupled with velocity selective detection, should result in 2-to-3 orders of magnitude higher isomer/conformer discrimination capabilities over the literature device. Finally, this universal detection methodology will allow for continuous throughput, which is ideal for interfacing with standard analytical instrumentation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MOLECULAR SURFACE TECHNOLOGIES LLC
SBIR Phase I: Catechol Linker Oligosaccharide Combinations for Antimicrobial Surfaces
Contact
33 TECHNOLOGY DR SOUTH
Warren, NJ 07059--5298
NSF Award
2143961 – SBIR Phase I
Award amount to date
$255,862
Start / end date
05/15/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase 1 project is a decrease in the devastating effects of deep implant-related infections. The technology could result in advances to the clinical health and welfare of the American public by improving clinical outcomes and decreasing morbidity and mortality. The technology addressed by this project may protect surgical implants, such as joint replacements and spinal fusion systems from bacterial colonization and developing infections. This technology could significantly reduce the greater than $3 billion cost to the US healthcare system from implant related infections. This antimicrobial technology could be used beyond medical applications for such things as food packaging to decrease foodborne diseases and more than double shelf-life of certain food products. Additionally, the linker technology developed through this project may be used to create super slick or self-cleaning surfaces with applications in the aerospace and marine industries resulting in increased fuel efficiency and performance.
The project aims to develop a homogeneous, covalently bound, linker molecule attached to medical implant material (titanium alloy) upon which a quaternary ammonium-modified oligosaccharide will be subsequently attached. Oligosaccharides are known to be biocompatible and quaternized oligosaccharides are highly potent antimicrobials. A treated medical implant could possess a powerfully antimicrobial surface so that, during surgery, any bacteria that encounter the surface will be killed. In this way, it is hoped that the avascular surface of the implant will not serve as a site for biofilm formation and growth and thus, reduce the incidence of perioperative infections. The key to any successful surface modification is the quality of the chemical attachment of linkers and active molecules to that surface. Polyphenols and catechols such as dopamine are ideal candidates for investigation as these molecules are generally known for their facility in forming thin films onto a wide variety of surfaces. Using dopamine as a model system, catechol analogs will be electrochemically attached, and the resulting thin films analyzed for attachment, thickness, ease of further modification, and morphology. Atomic Force Microscopy (AFM), UV/Visible spectroscopy, soak/stress protocols and microbiology will be used to gauge the success or failure of a thin film plus oligosaccharide combination.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MOLTEN INDUSTRIES INC.
SBIR Phase I: Methane Pyrolysis for High Quality Carbon Black and Low-carbon Hydrogen Production
Contact
2408 MANDELA PARKWAY
Oakland, CA 94607--1739
NSF Award
2151707 – SBIR Phase I
Award amount to date
$256,000
Start / end date
04/01/2023 – 03/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide low-cost hydrogen (H2) with a very low or even negative carbon dioxide (CO2) footprint at a commercial production cost reaching $1.50 per kg of H2 alongside a valuable solid carbon by-product, representing a technology advance for the American public and economy. The United States produces 10 million metric tons of hydrogen per year - 95% of it via steam methane reforming (SMR). SMR emits 100 million tons of CO2 in the process. This represents a >$10 billion market in the US alone and an important opportunity to reduce CO2 emissions. Methane pyrolysis promises green hydrogen with >5 times less energy than water electrolysis at costs competitive with steam methane reforming by producing solid carbon instead of gaseous carbon dioxide. The solid carbon, if produced correctly, can be used in tires, batteries, and concrete. The research in this project could increase the value of the solid carbon by-product produced alongside clean hydrogen in methane pyrolysis. If successful, this technology could lead to expanded domestic production capabilities with net-zero emissions from sectors such as fertilizer, transportation (batteries, fuel cells, synthetic fuels, and tires), and heavy industry (steel and cement).
This SBIR Phase I project proposes to develop a novel method to tune the production of high-quality solid carbon using methane pyrolysis. Carbon black forms in pyrolysis reactors both homogeneously in the gas phase and heterogeneously on catalysts, reactor walls, and on seed particles entrained in the gas. By tuning temperature, residence time, flow profile, and seed materials, this project will result in an improved understanding of carbon precipitation and formation in methane pyrolysis reactors. This improved understanding and the development of a reactor thermochemical model will ultimately lead to better control of a process to produce high quality solid carbon that can be used in tires, batteries, and concrete.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MORNINGBIRD MEDIA CORPORATION
SBIR Phase I: A Fully Electric Space Vehicle Propulsion Engine
Contact
259 CHESWICK DRIVE
Madison, AL 35757--8712
NSF Award
2318600 – SBIR Phase I
Award amount to date
$272,800
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a fundamental change in the way spacecraft propulsion can be achieved, perhaps leading to orders of magnitude faster space travel. Recent physics breakthroughs suggest that the development of an electromagnetically powered engine could enable high-speed travel under the right conditions. Commercially, there is great potential to decrease transit time to destinations on earth, to low-earth orbit, to the moon, and to destinations further in our solar system. Success in developing this engine will initially be developed by improving satellite positioning and accessing orbits. Further scale up of this propulsion system could serve as a platform technology to enable increased access to space due to reduced need for chemical propellant and enhanced speeds.
This SBIR Phase I project develops and tests a prototype engine by verifying the creation of electromagnetically driven propulsion. By utilizing a complex dielectric material as the environment where electromagnetic energy is introduced, the proof-of-concept engine will verify that the weak and strong force conditions are not violated and that a positive energy density can initiate nanoscopic distortions, to demonstrate novel electromagnetic propulsion in the form of further scalable engines. A number of researchers have begun building upon the work of Albert Einstein’s general relativity theory and now Miguel Alcubierre’s metric that suggests that a vessel can be propelled by selective distortion. Two key goals are the development and implementation of the complex dielectric material, and the determinization of the radio frequency power required to achieve sufficient propulsion. The project approach will include: (a) mathematical modeling, (b) comprehensive simulations of different embodiments of the approach, (c) experimental verification of nanoscopic distortions using an established laser interferometry approach, and (d) design and testing of the prototype propulsion engine. Beyond the initial prototype, the next stages include an optimization of the power/distortion metrics, association of the distortion to thrust, and maximization of the thrust to weight ratio. Ultimately, this research is expected to lead to enhanced electric propulsion that will be applicable initially to satellites, but ultimately, to a wide range of on earth and off planet propulsion.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MORPHOBOTICS LLC
STTR Phase I: Subcanopy 3D forest mapping by uncrewed aerial vehicle
Contact
47 WOOD AVE STE 2
Barrington, RI 02806--3503
NSF Award
2304303 – STTR Phase I
Award amount to date
$274,929
Start / end date
05/15/2023 – 11/30/2023 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is a tool to help mitigate the wildfire crisis in the western United States. Prescribed burns are one of the most effective ways to prevent the uncontrolled, large-scale wildfires that devastate entire ecosystems, communities, and economies, but the environmental assessment required for burns may delay the burn by months or years due to overburdened agencies. The innovation addresses this pain point with an automated survey solution, reducing both the paperwork burden and the potential for error in burn area vegetation mapping and spatial fire modeling. The solution ensures that prescribed burn plans use the best available vegetation data, providing fire managers with an accurate prediction of expected fire behavior for determining control strategy, staffing, and resources. This innovation would be the first automated prescribed burn spatial fire modeling solution using an autonomous Unmanned Aerial Vehicle (UAV). This innovation meets the STTR program’s focus on unproven, high-impact innovations because sub-canopy mapping by UAV is a cutting-edge application of autonomous flight, with challenges in optimization and decision-making. The application of UAV technology to prescribed burn environmental assessments will help to address the growing wildfire crisis in the United States by reducing the delay between the decision to burn at a selected site and the execution of the burn.
The technical hurdles to be addressed by the proposed project include both real-time, sub-canopy 3D mapping and optimization for constraints across sensor requirements, cluttered environment exploration, and SWAP (size, weight, and power) limitations. The goals are to produce a high-fidelity, open-source. UAV exploration environment, to implement 3D mapping on a resource-constrained computer that meets UAV payload requirements, to incorporate species-specific decision-making criteria into the UAV exploration algorithm, and to conduct validation testing to verify technical and early commercial feasibility. To achieve these goals, the development plan includes a series of milestones following a test-driven, development project management strategy, including simulation testing (software in the loop), tabletop testing (hardware in the loop), and field tests.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
MOTION GRAZER AI, INC.
STTR Phase I: Swine Automatic Lameness Sensor (SALS)
Contact
325 E GRAND RIVER AVE
East Lansing, MI 48823--4384
NSF Award
2232959 – STTR Phase I
Award amount to date
$275,000
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase 1 project is to provide an in-farm sensing system that will notify sow (adult female swine) farmers of early signs of animal lameness, and thereby reduce early sow mortality and enhance farm productivity. The technology uses artificial intelligence to analyze pig locomotion in order to spot subtle patterns indicative of lameness. Early detection of lameness will enable farmers to take corrective actions rather than waiting for lameness to deteriorate to sow death or culling. Early culling or sow death is a major economic cost to farmers and a large fraction of death and culls is due to animal lameness. Successful application of the technology being developed in this project promises to reduce early sow mortality and culling, leading to additional litters per sow and so provide a significant economic boost to farmers. With patent applications for key components of the sensing system, farmers will install sensors in hallways and obtain health measures for each sow when she moves between rooms. The projected annual revenue is $3.0 million.
This Small Business Technology Transfer (STTR) Phase I project proposes combining an imaging sensor with artificial intelligence to create a unique sensing system to unobtrusively and remotely diagnose lameness in sows (adult female swine) as they traverse hallways. This project seeks to validate two key technical contributions. First, precise 3D animal posture and locomotion are estimated for sows moving beneath a ceiling-mounted sensor. High accuracy is achieved through a novel annotation technique that overcomes difficulties in inaccurate manual location of skeletal landmarks. Second, a data-driven approach is used to train a deep neural network to learn the most discriminating combinations of posture and gait for determining lameness in walking sows. A self-supervised neural network sidesteps the need for extensive manual annotation and expert annotation is only required for lameness assessment. Together, these two contributions will enable a transformative technical capability of a remote sensor that can automatically diagnose early-stage lameness in sows.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
Metal Oxide Technologies Inc.
SBIR Phase I: Development and Integration of Reactor Enhancements for High Temperature Superconductive Wire Production
Contact
11331 TANNER RD
Houston, TX 77041--6901
NSF Award
2232610 – SBIR Phase I
Award amount to date
$255,938
Start / end date
07/15/2023 – 12/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop technology for the next generation of high-temperature superconducting wire production that will increase the throughput and quality of power transmission through the wire. Lowering wire costs is an integral part in the commercialization of cables that carry 5-10x more power than traditional copper cables and can meet increasing electricity demands using existing rights-of-way. High-temperature superconducting technology also enables magnetic-confinement fusion which has the potential to provide abundant clean energy.
This SBIR Phase I project proposes to perform research, development, and testing for the mass production of high-temperature superconducting wire at high current capacity and low cost. The goal is to understand the technologies, validate performance at a process level, and then transfer the validated technology into reliable high performing designs that will be integrated into the system design. The research focuses on technologies that increase the conversion efficiency of the precursor material into high-temperature superconducting film deposited on a textured micron-thin metal base tape. The work focuses on the following technologies: design of solid precursor feeds for solid to vapor flow generation and higher growth rate films; development of photo-activation and ultraviolet-enhanced technologies; enhancement of tape temperature uniformity for high current capacity via susceptor material selection and radiation properties.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
NASUS MEDICAL INC.
SBIR Phase I: Development of a Novel Miniature Spray Mechanism for Nasal Drug Delivery
Contact
245 W 2ND ST
Mesa, AZ 85201--6503
NSF Award
2136461 – SBIR Phase I
Award amount to date
$255,977
Start / end date
12/01/2021 – 03/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will improve patient outcomes through non-invasive nasal drug delivery. The nasal cavity is a non-invasive drug delivery conduit that is rapid-acting and inexpensive. Topical intranasal medications treat sinus-nasal, neurologic, and systemic diseases. The proposed innovation will significantly improve nasal drug delivery and patient outcomes by getting medication exactly where it needs to go. It will be the first miniaturized atomization technology and can be broadly applied to other fields. The initial envisioned application is chronic sinusitis, which affects 10% of the population and costs US healthcare $12 billion/year.
This Small Business Innovation Research (SBIR) Phase I project advances a new nasal drug delivery system. Current topical drug deliveries have limited effectiveness because they rely on distant particle atomization through complex nasal anatomy. To overcome this, a self-administered, catheter-based atomization drug delivery device that delivers drug directly to the anatomic target will be developed. However, atomization only exists in larger, rigid form factors. The objective of this research is to develop an novel atomizer that maintains a small, flexible form factor to be incorporated into the catheter tip. A new system like this is crucial for effective, tolerable nasal drug delivery. Computational Fluid Dynamics simulations will be performed to determine prototype design and construction. Designs will be manufactured through micromolding, extrusion manipulation, and overmolding with micro-3D printing. Designs will be tested using laser diffraction to evaluate spray characteristics (droplet size distribution, spray angle, velocity, and plume geometry) and anatomic models to evaluate fluid deposition.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
NATIONAL CURRENCY TECHNOLOGIES, INC.
SBIR Phase I: Security Gateway Processing Software for an Inclusive Public-Facing, Limited-Purpose Destination System
Contact
23352 MINERVA DR
Ashburn, VA 20148--6877
NSF Award
2208351 – SBIR Phase I
Award amount to date
$256,000
Start / end date
03/15/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to help enable safe and inclusive internet interfaces with the worldwide public for limited purposes, starting with the purpose of providing a ledger system for digital currency. The inclusive aspect of the internet interface allows intermediaries and the worldwide public to use varied mechanisms to send communications, as long as the communications received at the internet interface comply with the uniform format. As an enabling technology for implementing digital currency, this project will reduce reliance on current mechanisms for cash transactions including automatic teller machines (ATMs), checks, and services that require fees for sending money.
This SBIR Phase I project proposes to enable a ledger system to operate without requiring the provider to rely on any particular intermediary, and conceivably to accept and safely process packets from unrecognized sources. Two problems at the heart of this project are the lack of trust in giving third party institutions special access or privileges in interfacing with a digital currency ledger, and the need to increase access so individuals do not need a smartphone with a preapproved application in order to interface with the digital currency ledger. The research objectives ensure that a specific format pushed out for a security gateway for a digital currency ledger can be inspected to ensure a variety of threats are not present. The project will program different cores of a multicore processor with different security checks and synchronize performance of the security checks as a safety protocol that quickly and efficiently processes large volumes of packets for compliance with the required format.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
NEARSTAR FUSION INC
SBIR Phase I: Hypervelocity Gradient Field Fusion
Contact
13935 WILLARD RD
Chantilly, VA 20151--2936
NSF Award
2304408 – SBIR Phase I
Award amount to date
$265,965
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a more rapid lower-cost development path to a commercially attractive highly modular fusion power plant. Although early stage in its conceptual development, this technology has the potential to leapfrog past current fuel cycle models to provide cheaper, advanced aneutronic fuel systems that reduce or eliminate neutron reaction products and may also eliminate the need for tritium. The abundance of fusion fuel from seawater could provide strategic energy security and economic security to the U.S. and allied nations by phasing out need for hostile foreign fossil fuel suppliers. The commercial impact of this project includes grid based clean fusion energy is literally could extend to a $T+ market if expanded to meet global power demand, with a market pull driven by the need for clean abundant inexpensive energy. This technology will support a wide range of science and engineering jobs, and manufacturing jobs in both the energy and aerospace industries. This project will perform computational modeling and analytical calculations to show scientific and engineering feasibility prior to a focused follow-on experimental development program.
This SBIR Phase 1 project proposes to research and develop a new, simpler, and cheaper approach to fusion energy for grid based electric power. In this approach, a small fusion fuel capsule is accelerated to 10 km/s and injected into the throat of a strong magnetic field coil where it is symmetrically crushed to ignite and burn the gaseous fusion fuel contained within. While conceptually appealing and straightforward, some key components are partially unproven and require extensive research to show feasibility. First, the fuel capsule implosion and resulting fusion burn are not yet studied in sufficient detail to understand the potential plasma physics problems, including plasma-wall interactions, end losses, preheat, and overall energy yield and gain. Second, the novel railgun design needs development with a plasma armature and distributed power input using mass-produced moderate voltage capacitors and solid-state switches in order to achieve the estimated 10 km/s required to induce fusion and long life-time components. Extensive computational modeling and analytical calculations and design will be performed to de-risk the concept and establish a point design for a Phase 2 experimental validation of the concept.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
NEPTUNYA OCEAN POWER LLC
SBIR Phase I: Grid-Scale Marine Renewable Energy Technology Unlocked by Cost Reduction Innovations
Contact
901 NW 35TH ST
Boca Raton, FL 33431--6410
NSF Award
2208779 – SBIR Phase I
Award amount to date
$255,052
Start / end date
01/15/2023 – 12/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a novel mixed-mode (wind, wave, current) ocean energy converter that aims to be an improvement over the state-of-the-art offshore wind turbine design. This project will clarify design specifications to target a low-cost product with production scalability. Markets with the largest opportunity to benefit from this solution include US grid power, remote US islands (Puerto Rico, Guam, Hawaii, US Virgin Islands), other remote islands in the Pacific and Caribbean, and dispatchable power sources for emergency power generation like the Department of Defense. The environmental benefit of advancing the design of renewable energy converters and reducing the initial investments required by offshore clean energy may positively impact both the scope and the timeline for adoption of ocean renewables in the US.
This SBIR Phase I project will develop an efficient energy converter that drastically lowers the cost per installed unit through reduced weight of product components, onshore assembly, simplified installation, a lower center of gravity, and by achieving a storm rating. When coupled with offshore energy storage, this technology has the potential to unlock energy capture in ocean sites further offshore than is presently feasible. This project builds upon initial concept modeling and a 1/20 scale prototype construction, which validates the weight/power ratio, device stability, and comprehensive analytical evidence for commercial feasibility. Keeping the final cost-to-kilowatt low, while optimizing the overall design will be the major technical focus of the work plan, along with mitigating strategies for the risk of severe weather survivability. The proposed research will approach the design of each component individually with a focus on reducing cost and weight, while optimizing strength and output. Through the construction and testing of a proposed prototype, the team will conduct detailed analyses on scalability, performance, and cost factors that are critical for commercialization.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
NERRATIVE TECHNOLOGY INC.
SBIR Phase I: A platform to connect underserved and underrepresented communities to science, technology, engineering and mathemetics (STEM) careers
Contact
426 SPRING OAK RD
Camarillo, CA 93010--7529
NSF Award
2232689 – SBIR Phase I
Award amount to date
$275,000
Start / end date
04/15/2023 – 03/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in providing more access to educational pathways, and equitable opportunities for learning, professional development, and career growth to marginalized student communities. The project proposes the development and implementation of a digital human element to meet the needs of these impacted individuals. The project is not such organizations, but also to create pathways to equitable opportunities for those coming from marginalized communities to participate in some of the most innovative learning modalities, while studying for some of the most promising STEM careers.
The intellectual merit of this project lies in the use of big data and machine learning in personality and skills measurement. The artificial intelligence algorithms will be juxtaposed onto game mechanics to facilitate ease of use due to the familiarity with other existing user interfaces. The interface has a complex and dynamic personality profiling engine. The research will deliver standards and methodologies, evaluate existing exchange formats, improve accuracy metrics for neural networks, and deliver an initial digital human prototype. The technology will create a Data Lake containing professions, skills, certificate requirements, social media profiles, resumes, recorded interviews, and other online activities that are shared by the users for establishing personalized, artificial intelligence (AI)-supported career growth profiles. Information in the Data Lake will be curated to facilitate the development of personalized career development strategies. A Delta Lake model will be used to continuously stream data with improved data quality to drive the personalization requirements of both the digital human and the user.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
NEUROSOMNICSLLC
SBIR Phase I: Non-invasive Closed Loop Neuromodulation to Treat Obstructive Sleep Apnea
Contact
8748 DOUBLE EAGLE DR
Las Vegas, NV 89117--5803
NSF Award
2304265 – SBIR Phase I
Award amount to date
$269,942
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a non-invasive, externally worn appliance for treating obstructive sleep apnea (OSA), a condition affecting over 88 million Americans (26% of the American adult population). Those impacted by OSA are at risk of serious comorbidities such as diabetes, stroke, and heart disease. Many sufferers remain untreated due to intolerance to current treatment options with adherence and compliance rates as low as 40%. The economic impact is estimated at $30 billion resulting in $150 billion aggregate indirect costs due to motor and workplace accidents as well as productivity losses each year. The technology aims to capture part of the $18 billion sleep device market which remains significantly under penetrated due to approximately 80 million undiagnosed cases in the US alone.
This Small Business Innovation Research (SBIR) Phase I project aims to develop a non-invasive, dental neurostimulation device capable of activating the motor nerve fibers supplying the muscles responsible for dilating the upper airway in a controlled, non-perceptible manner. The appliance will be integrated with multiple sensing and stimulation electrodes to activate precise nerve branches in order to provide continuous innervation of relevant upper airway muscle groups, without interfacing directly with the nerve branch itself. Algorithms employing machine learning will be used to process neural electrode feedback signals and control electrical field stimulation waveforms. The project will consist of appliance design, benchtop testing, and overnight sleep studies in patients to build datasets and construct new algorithms. A software application will then be developed to automate routines in real-time in order to demonstrate concept feasibility of a new non-invasive therapy for obstructive sleep apnea.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
NEW PAGE STRATEGIC CONSULTING, LLC
SBIR Phase I: Sustainable antioxidants for industrial process fluids
Contact
500 N TARRANT PKWY
Keller, TX 76248--5685
NSF Award
2222215 – SBIR Phase I
Award amount to date
$253,030
Start / end date
01/15/2023 – 12/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop bio-based antioxidant products for industrial processes. Commercial development of peptide antioxidants has been limited for use in consumer, food, or therapeutic applications. This project will develop peptide antioxidants derived from the enzymatic digestion of agricultural biomass as alternatives to synthetic antioxidants used in the processing and storage of industrial fluids. Although synthetic antioxidants are critical for the stabilization of many industrial processes, they are derived from petroleum and tend to have toxicity and/or environmental safety concerns. The products resulting from this effort will be first-in-class innovations; safer and more environmentally responsible than existing products. Project activities will validate peptide performance in the initial target application area: the stabilization of vinyl monomer fluids and processes. This research will initially focus on vinyl monomer processing as well as four additional industrial markets where sustainable antioxidants could have a high impact. The estimated consumption of synthetic antioxidants in the five combined markets in 2022 will be close to 1 million tons.
This SBIR Phase I project seeks to develop a bio-based antioxidant product derived from sustainable materials and suitable for industrial process fluids. Antioxidant peptides derived from the enzymatic digestion of plant-based proteins have the potential to replace synthetic antioxidants in industrial processes. This project focuses on process compatibility, product stability, and antioxidative performance as key technical hurdles. This project will create a library of antioxidative peptides generated by the enzymatic digestion of plant proteins. This library will then be tested for the solubility of peptides with representative fluids under process conditions, potential formulation design, and for thermal and storage stability of the designed formulations. This project will lower technical barriers to advancing the commercial development of peptide antioxidants for applications across many industries.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
NEXGEN CANCER DETECTION LLC
SBIR Phase I: Enrichment of Cancer DNA for Improved Cancer Diagnostics from Blood
Contact
2132 21ST AVE SM
Lino Lakes, MN 55038-
NSF Award
2321908 – SBIR Phase I
Award amount to date
$275,000
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve the diagnosis of residual cancer cells, after treatment of cancer patients. The test will be conducted early and accurately using a simple blood draw. Twenty percent of cancer patients will experience cancer recurrence. Unfortunately, cancer recurrence is not diagnosed until years after initial treatment when the cancer has often metastasized, resulting in poor patient outcomes. As a result, 7% of cancer patients suffer from debilitating fear of cancer recurrence. Early and accurate diagnosing of residual cancer cells will improve the outcome for the 20% of cancer patients who experience recurrence. Additionally, it will help the 7% of cancer patients who suffer from debilitating fear of cancer recurrence. Overall, the costs of treating cancer will be lowered by diagnosing cancer earlier.
This Small Business Innovation Research (SBIR) Phase I project seeks to develop a highly accurate diagnostic test for residual cancer from a blood draw. DNA (deoxyribonucleic acid) from cancer cells circulates through the blood stream. This cancer can be detected because of mutations in the DNA of cancer cells. However, cancer DNA is rare compared to normal DNA, which makes diagnosing cancer from a blood draw difficult. Proof-of-concept data has shown that accuracy can be greatly improved through the enrichment of cancer DNA from a sample. After the sample is collected, the DNA goes through rounds of duplication, except a blocker is added to prevent normal DNA from duplicating. Through this process the cancer DNA becomes a larger percentage of the overall DNA in the sample and can be more accurately detected. This project will develop a collection of tests for accurately diagnosing residual colorectal cancer. The key tasks of this project are: 1) demonstrate the clinical robustness of the optimized test method, 2) develop additional tests to cover most colorectal cancers, and 3) demonstrate the sensitivity and specificity of the test methods. This project will lead to earlier and more accurately diagnosed cancer recurrence.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
NEXTGLASS LLC
SBIR Phase I: Low-Cost, High-Performance, Vacuum Insulated Glass Window
Contact
12009 MONTROSE VILLAGE TER
Rockville, MD 20852--4162
NSF Award
2233584 – SBIR Phase I
Award amount to date
$256,000
Start / end date
08/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is technology to improve the efficiency of building energy consumption. Windows account for about 30% of building energy losses and 7% of US carbon emissions. Vacuum insulated glass (VIG) windows are 4-5 times more insulating than a typical double-pane glass windows and are almost as insulating as the surrounding walls. A low-temperature production method will make vacuum insulated glass much more affordable. Such highly efficient and low-cost vacuum insulated glass will enable replacement of widely used double pane windows thus reducing the building carbon footprint. This decrease in pollution is especially significant given the push for net zero energy buildings and net zero greenhouse gas emissions by 2050. Major sectors that will benefit from such window developments are residential buildings, commercial buildings, and supermarkets (freezer/cooler doors). Market research indicates the VIG market is more than $25 billion.
This SBIR Phase I project will develop low temperature, vacuum insulated, glass windows that dramatically reduce the production cost of the currently available vacuum insulated glass (VIG). Although vacuum insulated glass products are highly efficient and have been produced for more than 30 years, they have not found market acceptance due to very high cost and poor seal reliability. Current high temperature, VIG manufacturing processes are slow and require expensive vacuum furnaces resulting in high costs. The proposed flexible seal VIG manufacturing process is a low-temperature production method that is much faster, thus requiring much lower capital investment, bringing VIG costs much closer to that of double pane windows. The novel flexible seal design of the proposed VIG also improves long-term seal reliability by eliminating thermal expansion stresses experienced by current rigid VIG seals. The performance and long-term durability of small VIG samples made using the low-temperature seal were validated. However, scale-up must be demonstrated before successful VIG development. The current project aims to scale up the proposed VIG manufacturing process and subject full-scale samples to rigorous accelerated weather, temperature, impact testing. A secondary seal will also be developed to prevent it from weather and gas permeation to ensure 50+ years of window life.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
NOMIS POWER GROUP LLC
SBIR Phase I: Novel Structure for Efficient and Reliable Medium Voltage Silicon Carbide (SiC) Power Devices
Contact
22 APPLETREE LN
Newtonville, NY 12110--5303
NSF Award
2126732 – SBIR Phase I
Award amount to date
$255,909
Start / end date
11/15/2021 – 02/29/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Innovation Research (SBIR) Phase I project is to improve the efficiency and reliability power conversion systems (PCSs) while also reducing the complexity and cost. System builders and end-users of power electronics for PCS may benefit from proposed advancements in power semiconductor technology that are translated to cheaper and more resilient and sustainable electricity generation, distribution, and consumption. The interconnections of distributed energy resources and energy storage systems within DC (direct current) micro-grids and interfaces between DC micro-grids and legacy AC (alternating current) distribution grid networks will be made simpler, more efficient, and more reliable when the proposed power semiconductor devices become ubiquitous within future PCS.
This Small Business Innovation Research (SBIR) Phase I project will improve advanced power semiconductor processing techniques. Specifically, the project focuses on: 1) developing a reliable semiconductor-based, high power electronic switch in the form of a SiC MOSFET (Silicon Carbide Metal-Oxide-Semiconductor Field-Effect Transistor), 2) optimizing the electronic switch parameters to achieve the best trade-off between efficiency and reliability, and 3) fully characterizing the electrical performance of the electronic switch. The research will involve the design of experiments to determine the optimal set of electronic switch parameters that take into account manufacturing limitations of the semiconductor processing equipment. The teams seeks to produce a functioning semiconductor-based, high power electronic switch that is capable of operating more reliably and more efficiently than what is presently available in the market. The team also seeks to enable even higher power electronic switches to be made by means of scaling the resultant SiC MOSFET technology.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
Nitrate Elimination Company, Inc
SBIR Phase I: Dual enzyme system to prevent food waste caused by oxygen
Contact
334 HECLA ST
Lake Linden, MI 49945--1323
NSF Award
2208721 – SBIR Phase I
Award amount to date
$256,000
Start / end date
01/15/2023 – 12/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is development of new technology for safe, recyclable, and effective packaging. The system works by protecting packaged contents from damage from oxygen (technically termed oxidation). An estimated 25% of the world’s food supply is lost due to food spoilage, with oxidation a major contributor to the problem. Technology that contributes to food security is a compelling objective in the 21st century. Today’s methods of protection from oxygen require metals or complex and expensive barrier materials. These options inhibit efficient recycling. Reduction in packaging waste is an increasing goal. Industries agree that current products for oxygen protection are not meeting needs or consumer preferences. This proposal describes a solution based on a pair of enzymes that use minute quantities of sugar to consume oxygen by producing a modified sugar and water. Enzymes perform a specific function (catalyze a chemical reaction) and are inherently environmentally friendly. For many foods, the sugar in the food itself powers the system.
The proposed project seeks to determine the viability of a dual enzyme, oxygen removal system for preservation of food quality in real world packaging applications. Prototype materials have demonstrated sufficient performance to justify this research, and a series of technical objectives have been defined. Objectives include determining the oxygen removal activity in a variety of containers and designing optimal product configurations in different containers. A second objective is to determine system performance at various temperatures. Active shelf-life of the system – activity over time – needs to be evaluated. Defining these criteria is required in order to determine the commercial potential of the technology. Enzyme activity assays are key tasks for all objectives. Oxygen removal effectiveness will be determined by an industry standard for measuring oxygen incursion into sealed packages. The proprietary cell lines developed for expression of the enzymes in the system have been screened for production potential at commercial scale. All required enzymes will be produced in house under previously developed standard operating procedures for fermentation and purification. Interfacing with developers of new, environmentally benign packaging materials is ongoing.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
OPAL HTM INC
STTR Phase I: Novel Medical Equipment Utilization Tracking System for Improved Patient Safety and Hospital Efficiency
Contact
3827 FAWN LN
White Plains, MD 20695--3310
NSF Award
2321886 – STTR Phase I
Award amount to date
$275,000
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project relates to the development of a novel system capable of measuring medical equipment utilization with high accuracy and scalability. This innovation will arm healthcare technology managers with the insights needed to optimize inventory size and composition according to actual patient needs, thereby saving hospitals an estimated $23.3 billion annually in equipment-related costs, in addition to making possible usage-based predictive maintenance that can effectively prevent dangerous equipment failures. Beyond these core value propositions, comprehensive medical equipment utilization insights may be leveraged to facilitate strategic resource management in public health emergencies, increase energy efficiency of healthcare facilities, and improve regulatory surveillance of emerging equipment safety issues. The results of this project will form the basis for a hardware-enabled service and clear the path towards development of deployable products, clinical pilots, and early sales. Through commercialization under a sustainable business model, the envisioned product will substantially increase the economic competitiveness of US hospitals, which comprises one of the largest sectors of the American economy. The project will also advance the health and welfare of the American public through improved medical device safety and management.
This Small Business Technology Transfer (STTR) Phase I project will establish technical and commercial feasibility for an innovative, asset-agnostic, medical equipment utilization tracking system which will integrate state-of-the-art techniques for non-intrusive load monitoring, deep learning, and edge computing in order to overcome previously insurmountable asset monitoring challenges posed by the heterogeneity and churn of hospital equipment inventories. Key technical hurdles to be addressed relate to the capture and characterization of medical equipment electrical load data, real-time translation of this data into accurate usage statistics suitable for hospital decision-making, and distributed implementation of this process through non-invasive sensor modules that are broadly compatible with sundry medical equipment. The proposed research will overcome these hurdles through (i) systematic collection and analysis of power consumption data from a representative group of medical equipment under various operational states, (ii) formulation, training, and validation of adaptive artificial neural networks that predict usage from power data, (iii) construction of a proof-of-concept intelligent sensor module, and (iv) system performance testing in a simulated clinical environment. Through completion of these objectives, this project will advance knowledge in the fields of hospital asset management and industrial Internet-of-Things.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
OPERA BIOSCIENCE, INC.
SBIR Phase I: A low-cost, bacterial production platform for the manufacturing of high purity recombinant proteins and growth factors
Contact
1801 MAPLE AVE # 5240
Evanston, IL 60201--3149
NSF Award
2233507 – SBIR Phase I
Award amount to date
$275,000
Start / end date
06/15/2023 – 05/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to introduce a cost-effective way to produce recombinant proteins and growth factors for the cultivated meat industry. Cultivating animal cells to create animal tissues, including muscle and fat, allows manufacturers to create real animal meat as an alternative to animal-farmed meat. Producing meat in this method may provide a healthier, safer, and more ethical source of real animal meat. This manufacturing process avoids industrial animal farming and consequently uses significantly less land and water. It also avoids the greenhouse gases emitted by industrial animal farming and may emit far fewer greenhouse gases. Cultivated meat could result in a healthier public as well since its controlled production will likely result in fewer foodborne illnesses and avoid the cramped conditions of industrial animal farms that can breed new illnesses. The ability to grow meat in nearly any location increases the nation’s food supply chain resilience and reduces dependence on foreign food imports. Finally, by reducing the amount of industrial animal farms, cultivated meat reduces the need for animal slaughter.
The proposed project will demonstrate the ability of the innovation to solve several challenges in the current production of recombinant proteins and growth factors in traditional protein manufacturing platforms that prevent manufacturing at lower costs and with high purity. Cultivated meat has the potential to disrupt the >$1 trillion meat industry, but the ability to source high purity, cheap proteins limits the commercial adoption of cultivated meat products. The innovation could produce proteins at cheaper costs than existing platforms because it bypasses expensive and time-consuming operational manufacturing steps while still achieving high purity. The critical technical objectives of this project include: 1) the establishment and comparison of a production benchmark for recombinant growth factor production against the current industry standards, 2) the optimization of the growth conditions necessary for the protein production platform to achieve improved yields, and 3) the creation of a manufacturing platform optimization toolkit to increase the quality and amount of usable protein produced. The knowledge gained at the completion of this project may provide valuable insights into the ability and conditions to promote high production levels of proteins and growth factors while providing data illustrating the improvement when compared against current industry standards.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
OPTIC FRINGE CORP.
SBIR Phase I: Artificial Intelligence (AI)-Aided Part Identification for Coordinate Measuring Machines
Contact
8 COBBLESTONE WAY
North Billerica, MA 01862--2915
NSF Award
2222967 – SBIR Phase I
Award amount to date
$274,536
Start / end date
01/15/2023 – 10/31/2023 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is the development of a new generation of smart machines used in the measurement of parts and assemblies. The team has demonstrated that this technology can convert existing coordinate measuring machines to self-driving autonomous machines. The ability to automatically measure parts is an important feedback link in the process chain that will enable fully automated manufacturing of the future. Specifically, this automation will reduce the specialized skill required to use a Coordinate Measuring Machine (CMM). The innovation will enable workers to operate a CMM and get a precise part measurement. This device is especially helpful as the skilled manufacturing/metrology workforce is retiring as it gives new employees the ability to provide accurate information with little/no training. This innovation also gives the manufacturing companies an option to buy a new machine or upgrade their existing coordinate measuring machine. While the focus of this proposal is part identification, this technology has ready applications in Computer Numerical Control (CNC) machining, robotics, and automated assembly lines. This capability will make the US manufacturing sector stronger and more technologically advanced.
The objective of this proposal is to develop a new technology to identify machined parts and assemblies. This technology will be implemented on coordinate measuring machines (CMM), which are used widely in the manufacturing sector to measure the shape and size of parts. The proposed technology will enable autonomous measurements of parts allowing a higher level of automation. In this identification technology, the team will use live images from a camera, multiple solid model/Computer Aided Design (CAD)-generated images, and advanced image processing. Applying Artificial Intelligence (AI)/Machine Learning (ML) to the image processing of part images will ensure correct part identification. Correct identification of parts as seen by the camera is the remaining unsolved challenge to achieving self-driven automatic measurements of parts. Most machined parts are textureless and most of the information is contained in the edges. Current image processing techniques work well with texture-rich parts but are unreliable with textureless machined parts. AI/ML enhanced image processing using edge and shape information is a promising approach, solving this problem will lead to the birth of a new generation of CMMs that can measure parts automatically.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
OPTIMIZING MIND
SBIR Phase I: Modular and Updatable Artificial Intelligence (AI) for Robotics
Contact
3168 SOUTH CT
Palo Alto, CA 94306--2949
NSF Award
2127085 – SBIR Phase I
Award amount to date
$254,746
Start / end date
02/01/2022 – 12/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide a novel recognition architecture to computer vision in the robotics industry. The project seeks to enable computer learn without rehearsal, allowing corrections for details that are present in the real world environment. The aim of this project is a solution to be used by computer vision customers to solve their problems immediately (without sending data back to retrain the whole network), reducing machine and customer downtime and disruption while increasing productivity. The initial focus is on robotics with computer vision limitations though the technology may be useful to other industries. Success in improving computer vision-based learning could facilitate disaster responses, augment current physical abilities, and enable exploration beyond the boundaries of Earth.
This Small Business Innovation Research (SBIR) Phase I project will help create a framework to overcome rehearsal requirements that limit automated robots’ utility within life-like, dynamic environments. Artificial intelligence (AI) remains inflexible compared to humans at quickly accumulating knowledge without forgetting what they have previously learned. Robots using AI are currently only used in environments that are very limited and are very tightly controlled. Everything that might happen in the robot’s work environment must be included their training set. The proposed AI solution is suited for learning in dynamic environments without rehearsal while maintaining scalability as information is encountered. This technology may allow robots to be trained within their environment. This project may enable visual capabilities leading to a demonstration of flexible learning without rehearsal within dynamic robotic environments.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ORALIVA, INC.
STTR Phase I: Portable single cell cytology and predictive analysis platform for the early detection of epithelial cancers
Contact
2135 ARIELLE DR APT 2403
Naples, FL 34109--0369
NSF Award
2233372 – STTR Phase I
Award amount to date
$275,000
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project will address the need for an accessible method to identify early-stage epithelial cancers, with high accuracy, earlier and at lower cost than is currently available. In 2020, the total cost of cancer care was nearly $210 billion. Due to the nature of current cancer diagnostics, most cancers are diagnosed and treated during late-stages. This results in a large economic burden to patients, families, healthcare providers, and facilities. To advance the health and welfare of the public and reduce the nation’s healthcare burden, there is a need for cancer screening, diagnostic, and monitoring devices that are non-invasive, cost-effective, easy-to-use, and accurate. The proposed platform for the early detection of multiple types of epithelial cancers 1) addresses the lack of effective non-invasive portable screening devices; 2) provides faster, more discriminatory assessments in near real-time; 3) yields the most precise and accurate results to identify cancers earlier, when interventions are more impactful, less expensive, less invasive, and more likely to improve patient outcomes.
This Small Business Technology Transfer (STTR) Phase I project seeks to establish the feasibility of developing the first portable, programmable, single cell cytology platform for early detection of multiple types of epithelial cancers, suitable for use at the point-of-care. The proposed technology will uniquely combine microfluidics and artificial intelligence (AI) to act as a sensor and provide predictive analysis, allowing for the accurate classification of potentially cancerous tissue. The platform will support near real-time, multiparameter, single-cell cytology measurements and will provide a method for automated analysis of a plurality of key metrics. Proof of concept has been established for the application area of oral cavity cancers, with the approach demonstrating superior performance metrics compared to other diagnostics (tissue reflectance, tissue auto fluorescence, salivary testing, and cytology testing). It is the only adjunct that can distinguish between mild, moderate, and severe dysplasia. The key objectives for this project are to develop methodologies to link different clinical specimen types to the microfluidics environment, and a biomarker discovery process to identify biomarkers for different applications that are amenable to the platform. The successful completion of this project will enable the platform to recognize and assess various levels of dysplasia across multiple epithelial cancer types.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ORION THERAPEUTICS INC.
STTR Phase I: Multifunctional Lipid Nanoparticle Delivery System for Targeted Delivery of Vascular RNA Therapeutics
Contact
7611 DUPREE RD.
Knoxville, TN 37920--6768
NSF Award
2309031 – STTR Phase I
Award amount to date
$275,000
Start / end date
03/15/2023 – 02/29/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is that the proposed delivery system will advance many RNA therapeutic solutions to address intimal hyperplasia (IH) and other vascular conditions. Vascular disease is the most common disease pathology in the US, costing the country’s healthcare system over $437 billion dollars annually. Because vascular interventions have limited long-term success, there is a critical need for effective therapeutics, including to address the condition of IH which, post intervention, can lead to restenosis, or artery narrowing. By delivering therapeutics safely and efficaciously to specific tissues, the proposed technology will result in improved quality of life and decreased morbidity and, in combination with a Contact Research Organization business model, will contribute to the advancement of a multitude of RNA therapeutics targeting the vascular system and eventually other areas of the body. Eventually, the platform will contribute to the development and commercialization of a broad range of RNA drugs, creating jobs in science, sales, and manufacturing. This project will lead to improvements in the treatment of a variety of diseases and conditions that currently lack effective therapy, resulting in improved quality of life and decreased morbidity in the broader society.
This Small Business Technology Transfer (STTR) Phase I project seeks to develop a novel multifunctional lipid nanoparticle (LNP) delivery system for tissue-specific targeted delivery of RNA therapeutics to the vascular system. Intimal hyperplasia (IH), a condition triggered by mechanical injury to blood vessels, causes artery thickening in ~ 60% of peripheral vascular disease patients 12 months post-intervention. IH often results in the need for additional surgical intervention with associated costs and increased patient morbidity and mortality. This project will focus on inhibiting the remodeling pathways that lead to IH via RNA therapeutics delivered directly to the injury site. The project will develop vascular targeting liposomes designed to exhibit multifunctional potential to target exposed collagen matrices specific to areas of vascular pathology and enhanced targeted vascular uptake/delivery. Phase I will advance the LNP technology by showing that an active therapeutic encapsulated by the system is appropriately delivered and results in a clinically relevant drug concentration. If successful at delivering bioactive RNA payloads to injured vascular tissues, the technology will reduce the risk of post-intervention complications and reduce the need for revascularization. Ultimately, this novel LNP packaging and delivery system will provide a new tool for advancing a broad range of RNA therapeutics.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
OSMOSES INC.
SBIR Phase I: Optimization and scaling of ladder polymers for membrane-based gas separations
Contact
750 MAIN ST
Cambridge, MA 02139--3544
NSF Award
2151444 – SBIR Phase I
Award amount to date
$253,815
Start / end date
08/15/2023 – 07/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
This broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project aims to develop membrane solutions to address opportunities in the gas and vapor separation market. Today, this market is dominated by energy-intensive thermal processes that have large carbon footprints, such as distillation and absorption/stripping. The current membrane solutions often lack the flux, recovery, and stability required for many applications. The membranes that will be developed in this project are formed from novel polymeric materials that have the highest combinations of permeability and selectivity out of all polymers reported in the open literature. If deployed commercially for renewable and/or traditional natural gas purification, these membranes could reduce energy consumption and product loss by over 40% and over 80%, respectively, compared to current commercial membranes. In this way, the advanced membranes being developed could save up to $2 million per day in product loss that is currently flared from commercial membrane systems, resulting in both savings for the customer and a reduced environmental footprint. Related opportunities in other gas and vapor separation markets could also be enabled by this research.
The intellectual merit of this project is to develop gas separation membranes from a novel class of polymers with record performance. To this end, this effort aims to scale polymer synthesis, form thin films, test developed membranes using complex gas mixtures, and develop an optimized techno-economic model for market applications. These objectives are of practical importance for manufacturing and commercialization, but they are likewise important for scientific and technical innovation in polymer science and thin-film formation. Moreover, testing these materials in thin film form under complex gas mixtures will provide data on stability under relevant conditions. The research on polymer scaleup and thin film formation is critical for refining technoeconomic assumptions for capital costs, and the testing of complex gas mixtures is critical for refining assumptions on process energy costs and cost savings from product recovery. Accomplishment of these objectives will enable new innovations related to the formation of membrane modules that can be tested and evaluated with industrial gas mixtures.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
OTHERWORDLY LLC
SBIR Phase I: Automated Detection of Confounds and Inappropriate Context to Promote Prosocial Learning and Cognition
Contact
7804 GARLAND AVE
Takoma Park, MD 20912--7712
NSF Award
2304423 – SBIR Phase I
Award amount to date
$274,990
Start / end date
07/15/2023 – 06/30/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is the development of Artificial Intelligence (AI)-based algorithms that generate content for word-meaning video games at an affordable cost. Word-meaning games support literacy, fluency, critical thinking, and cross-cultural understanding for players of all ages and backgrounds via adaptive vocabulary scaling systems and accessibility options for players with visual or motor difficulties. Science, Technology, Engineering, and Mathematics (STEM) literacy is supported by incorporating STEM content in a mix of entertaining and serious content. These prosocial and cognitive impacts are essential for personal and professional growth, cultural competence, and will be measured by game learning researchers. The project will also contribute to the field of natural language processing and machine learning through the addition of new benchmarks to open-source resources. Success in reducing content creation costs could lead to licensing content to other game publishers and the creation of additional word-meaning games on the market, benefiting players. This project is uniquely positioned to help retain game industry jobs in the U.S. and contribute to the growth of the industry.
The technical innovation of the project is threefold: 1) development of algorithms for unrelated word content generation, 2) development of appropriateness and offensiveness filters for natural language content, and 3) evaluation of a word-meaning game’s ability to improve cognitive function and social awareness. This research and development has the potential to address a gap in the field of natural language processing on unrelatedness. Part of this effort contributes to open-source benchmarks for future research. Similarly, social bias is a prevalent and well-known issue in machine learning models, potentially offensive or inappropriate word combinations need to be detected and avoided via newly developed algorithms that explicitly detect and avoid publishing such content. To achieve both goals, a variety of machine learning techniques, including those that leverage existing large natural language models, will be employed and evaluated for accuracy. Using these algorithms as a foundation for content creation, the word-meaning game will integrate the generated content. The program will be evaluated with regard to its ability to increase social awareness and confidence with an expanding vocabulary. Specifically, the study will evaluate both brief gameplay and long-term gameplay and measure efficacy with in-game metrics and surveys.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
OWLFLY
SBIR Phase I: Machine to fabricate a bioinspired insulation material: The Concatenator
Contact
19 HILL RD
Frenchtown, NJ 08825--4008
NSF Award
2232908 – SBIR Phase I
Award amount to date
$275,000
Start / end date
06/01/2023 – 05/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in the development of a novel, and more benign than fiberglass, thermal insulation technology for use in homes across the United States. Improvements in insulation technology have the potential to reduce energy use nationwide, along with all carbon emissions associated with the production and transmission of that energy. According to the Energy Information Administration, 51% of all residential energy in the United States is used for heating and cooling living spaces, which amounts to about 11% of the total energy consumption of the country. This project aims to use the principles of biomimicry to develop a more effective batt insulation. Unlike other insulation materials, unprotected exposure by the insultation installers will not aggravate respiratory issues, which is increasingly important for homeowners and working people who suffer from the long-term effects of COVID-19. This project seeks to push the potential and affordability of this new technology while creating new American jobs.
The project is inspired by the nests of yellowjacket wasps that live in pockets of permafrost high above the Arctic circle. The nests are protected from extreme temperatures by the hollow wall structure surrounding the nest’s interior. This structure can be adapted to create insulation panels that are highly efficient, lightweight, water-resistant, non-combustible, non-toxic, non-dusting, and irritant-free. The project focuses on the development of such a thermal insulation material in an efficient way to keep its price point competitive with the current products. This involves designing a manufacturing machine capable of producing the new insulation quickly and consistently. To further push the thermal performance of the material, the company will also develop a complementary machine that can scan insect specimens in museum collections to assess biological structures that are highly reflective in infrared wavelengths and use the data to address management of radiative heat transfer. The team will apply a six-sigma approach to quality control for improving the insulation manufacturing process.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
OmnEcoil Instruments, Inc.
SBIR Phase I: Prostate cancer diagnosis with an integrated endorectal MRI and targeted transrectal biopsy
Contact
2936 LAKEVIEW BLVD
Lake Oswego, OR 97035--3648
NSF Award
2037190 – SBIR Phase I
Award amount to date
$255,787
Start / end date
12/15/2020 – 11/30/2023 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve detection of prostate cancer, a highly prevalent fatal cancer in men. Approximately one million prostate biopsies are performed annually in the U.S. Unfortunately the standard diagnostic method is imprecise and inefficient. The proposed project will advance a new method that uses Magnetic Resonance Imaging (MRI) to target biopsies for improved detection.
This Small Business Innovation Research (SBIR) Phase I project will advance diagnosis of prostate cancer by developing a system that combines an endorectal MRI coil and a multichannel array of transrectal biopsy needle guides and allows for endorectal MRI with in-bore biopsy as a single rapid integrated procedure. The project will advance a procedure that optimally combines endorectal MRI and MRI-targeted biopsy.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PARADOCS HEALTH INC.
SBIR Phase I: Tackling Healthcare’s Paradoxes: Quality Patient Care, Provider Workflow, and Data Security
Contact
2450 HOLCOMBE BLVD. STE X
Houston, TX 77021--2041
NSF Award
2233197 – SBIR Phase I
Award amount to date
$275,000
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide a new tool for physicians to potentially automate the preparation of insurance documentation and facilitate claim building which may help to lower provider costs and increase patient access to and quality of care. Physicians can spend up to 50% of their time performing non-clinical tasks which have also been associated with physician burnout, a psychological condition known to result in medical errors, lower quality of care, higher costs, and overall poorer patient outcomes. The proposed innovation is a proprietary algorithm that leverages data to automate the completion of insurance form documentation. This new technology aims to resolve workflow bottlenecks and complement existing clinical workflows by delivering a simpler provider experience by streamlining the preparation of medical form documentation.
This Small Business Innovation Research (SBIR) Phase I project aims to develop a machine learning-enabled electronic medical record access toolset designed to automate and streamline the preparation of insurance form documentation. A major issue in the US healthcare system is the process through which healthcare providers seek reimbursement through health insurance companies. Filing claims and seeking prior authorizations on procedures or tests from insurance companies is a manual process that is slow and error prone, often resulting in delays in treatment or even rejection, jeopardizing patient health, and resulting in higher costs. Designed for physicians, the proposed technology will facilitate claim building using pre-trained natural language models to extract medical text and relationships from various inputs including patient and provider demographic information as well as payer information, clinical taxonomy, functional features, and relations.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PARTHIAN BATTERY SOLUTIONS, LLC
STTR Phase I: Novel State of Health Measurements Through Advanced Lithium-ion Battery Modeling for Secure and Scalable 2nd-Life Battery Deployment
Contact
281 DON KNOTTS BLVD
Morgantown, WV 26501--6737
NSF Award
2304417 – STTR Phase I
Award amount to date
$274,951
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is the accelerated adoption of electrification and other next-generation technologies, like renewable energy. This project proposes a technology to effectively manage end-of-life lithium-ion batteries through reuse. Enabling these batteries to be safely incorporated into a circular/reuse economy is essential for society to meet the goals of sustainability and net-zero carbon emissions. Extracting further value from post-consumer batteries through reuse provides an economic incentive for original equipment manufacturers to responsibly decommission up to 100% of their retired battery units, thereby foregoing the need to dispose of the battery and manufacture a new one in its place. The proposed technology’s ability to create a practical and safe battery reuse market will not only create a net-negative carbon footprint for the batteries but will also alleviate the stresses currently faced within the lithium (Li)-ion supply chain and bring forward Li-ion technology at more affordable prices. Additionally, the detailed state of health analysis performed by the technology will allow for more informed decision-making in regard to second-life battery allocation, enabling energy storage project managers to lower upfront investment on energy storage, without sacrificing on safety and performance.
This STTR Phase I project proposes to develop a novel approach to attain rapid and accurate state of health determinations for second-life lithium-ion batteries. The bespoke technology will utilize novel battery modeling and data analytics to establish an understanding of battery health beyond capacity degradation. The technology’s ability to capture the ensemble effect of numerous battery degradation mechanisms is a significant and needed advance over the incumbent state of the art in battery health analysis. As increased rates of degradation in specific cell parameters would lead to unique performance and operating limitations within the battery, it is imperative to take these parameters into consideration with a state of health assessment, or there is a risk of fatal operating event occurrences such as thermal runaway. This project’s objectives include the development of the rigorous electrochemical model to model the highly complex capacity fading mechanisms within Li-ion batteries, and the development of an algorithm-based state of health estimator that will utilize real-time data and project remaining useful life estimates for second use applications.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PEARL BIO, INC.
SBIR Phase I: Consolidated platform to engineer and produce novel biopolymers for improved biologics
Contact
700 MAIN STREET
Cambridge, MA 02139--3543
NSF Award
2233560 – SBIR Phase I
Award amount to date
$274,462
Start / end date
08/01/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a next generation biomanufacturing platform that could redefine the nature of protein building blocks and become a major driver of US innovation and economic growth. By engineering new microbial organisms and their associated cellular machinery, this technology may enable the incorporation of diverse, non-native components into proteins, creating a new class of biological materials for applications in therapeutic development, biomaterial development, and other areas of biomedical research. These newly designed proteins can be specifically engineered to add desired functionalities, leading to intelligent design of novel products in the biomedical field. This Phase I project seeks to address the limitations of current approaches in this field, creating a consolidated platform able to incorporate synthetic starting materials, and characterizing how these modifications impact microbial cell growth and fitness. These advances may establish a new paradigm for design and production of next-generation products with enhanced efficacy and functionalities, driving the development of new therapeutics and materials for transformative societal, medical, and economic benefit.
The proposed project will address major feasibility challenges in the development of a consolidated platform for synthesis of synthetic biopolymers containing multiple, distinct synthetic chemistries endowing novel chemical and biophysical functionality. Efforts to expand the genetic code have shown that the natural translation system is capable of selectively incorporating a wide range of synthetic amino acids (sAAs). However, several roadblocks have substantially limited the field to only one or a few instances of site-specific incorporation of sAAs. These include: biological restrictions to altering ribosome sequence, poor efficiencies of orthogonal translation systems for sAA incorporation, and unavailable open codons that have constrained biopolymer synthesis to tag-and-modify approaches or simple protein decorations. This project seeks to address these challenges in bringing this innovation to market by combining a genomically recoded organism containing engineered translation machinery with Ribo-T to enable the production of synthetic biopolymers with synthetic monomers. Additionally, this project aims to establish the technical capabilities of encoding two distinct sAAs into a single polymer. Together, these Phase I goals will advance a biomaterials platform to produce synthetic biopolymers with multiple synthetic chemistries to de-risk a path toward novel polymer biologics.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PERSEUS MATERIALS, INC.
SBIR Phase I: Fast and low-energy manufacturing of high-performance, fiber-reinforced composites
Contact
550 OAK ST
Mountain View, CA 94041-
NSF Award
2304621 – SBIR Phase I
Award amount to date
$265,072
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will establish the thermal and mechanical limits of novel, fiber-reinforced, plastic composite materials (FRPs) and pioneer a fast-manufacturing process for these FRPs. FRPs have the potential to achieve significant lightweighting and subsequent greenhouse gas emissions reductions by displacing steel and aluminum in many industries including aerospace, automotive, construction, infrastructure, marine, and wind energy. However, difficulties in their processing and slow production have made FRPs too expensive for widespread adoption. If successful, the proposed project will enable FRP adoption in more price-sensitive industries such as automotive and infrastructure for widespread greenhouse gas emissions reductions. A shorter-term market that can be addressed with this technology is the FRP mold market. FRP molds are used to make FRPs but are also themselves made of FRPs. Cheaper FRP molds with shorter lead times are a major need for manufacturers. The domestic market for mold making was estimated to be $21.6 billion in 2020. As the FRP market continues to grow, the mold making market is expected to grow in kind.
The intellectual merit of this project includes new, fiber-reinforced, plastic composite materials (FRPs), associated manufacturing processes, and methods of optimization thereof. The core innovation is resin chemistry with a unique form of low energy, rapid curing. This project will be the first critical step in developing new manufacturing processes that exploit this curing phenomenon for faster, cheaper FRP fabrication. This Phase I research is split into three stages. In stage 1, the effect of different resin components on the final thermal and mechanical properties of the FRP parts will be evaluated. In stage 2, a prototype system will be developed for fast fabrication of large test specimens. Common failure modes will be understood, and optimal processing conditions will be determined. In stage 3, different form factors of fiber fillers will be used. This project will establish the fundamentals and common failure modes of a novel, more efficient FRP manufacturing process.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PHOTONECT INTERCONNECT SOLUTIONS INC
SBIR Phase I: Development of a Chip Technology for Cheaper and Easier Photonic Device Manufacturing
Contact
280 RHINECLIFF DR
Rochester, NY 14618--1622
NSF Award
2304400 – SBIR Phase I
Award amount to date
$274,996
Start / end date
06/15/2023 – 05/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the advancement of manufacturing technologies for industries such as telecommunications, data communications, sensors. and defense. Most of the internet relies on data centers to process data, and this processing is accomplished via a device called an optical transceiver. These transceivers house an optical fiber, which is as thin as a single strand of human hair, attached to a chip device to transfer information to/from the data centers. The optical fiber is so small that it is very difficult to precisely connect the fiber to the chip, often resulting in performance losses. With >100,000 transceivers per data center and >2,700 data centers in the United States, it is important to have good fiber connection for reduced power consumption and increased performance. Technology companies are also looking for chips with multiple fibers, making the need for better fiber placement even greater. In this project, the company focuses a new technology that makes fiber placement on a chip faster, more accurate, and cheaper. This new technology uses a special component that enables fiber placement with precision while improving the device performance 4 times.
This Small Business Innovation Research (SBIR) Phase I project addresses major pain points for optical transceiver companies: cost and time to package an optical fiber to a silicon photonic chip. The proposed product consists of a fusion splicing machine and a novel silicon dioxide mode converter. The mode converter localizes heat from the laser, enabling fusion while simultaneously decreasing the loss level. This technology packages silicon photonic devices without compromising performance. It significantly improves packaging speed from 10 minutes to 2 minutes, increases power efficiency by 4X, and provides a 50% savings. The company has demonstrated coupling losses lower than the industry standard of 3 dB on specialty chips. The research objectives involve improving coupling losses to around 1 dB, demonstrating splicing with foundry chips, and improving the strength of the fusion splice for improved reliability. The completion of these objectives will result in extremely low loss photonic packaging applicable for use with foundry chips, increasing the commercialization potential of the technology. This technology will enable customers to package single or multi-fiber devices with high efficiency, low cost, and at high volumes, ultimately increasing production capacity across many industries.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PHYTOSTONE LLC
SBIR Phase I: A Novel Carbon-Sequestering Biomaterial for Dropped Ceiling Tiles
Contact
501 SILVERSIDE RD, STE 105
Wilmington, DE 19809--1376
NSF Award
2304384 – SBIR Phase I
Award amount to date
$273,652
Start / end date
08/15/2023 – 03/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to validate a new, biochar-enriched building material as a ceiling tile product. The plane of ceiling tiles represents a vast, untapped opportunity in combating climate change through carbon sequestration. With the innovation, a proprietary mineral matrix is enriched with 50-80% biochar. Biochar is the resulting charcoal-like residue from pyrolysis. This stabilized form of carbon is nearly crystalline and resistant to emissions-causing oxidation. Combined with a proprietary mineral binder, the resulting novel material is non-flammable, ultra lightweight, and biodegradable. As a ceiling tile, it can reduce a building’s embodied carbon in an easily quantifiable way, position the building for carbon sink remuneration, boost the green ratings of the building, help qualify a building for sustainability-linked financing - all without compromising on fire safety standards. This project capacitates an innovation that adds to the nation's toolkit in creating a climate-responsible built environment.
The project innovation is a novel, biogenic, cementitious chemistry composed of plant-based biochar, clay, binding minerals, proprietary catalysts, optional reinforcement fibers and optional pigments. The inclusion of biochar is a major characteristic of the innovation, comprising up to 80% of the material. Unlike carbon stored in plant matter, the ocean or in soil, the carbon atoms of biochar are more resistant to losing electrons and being converted into carbon dioxide, therefore making the innovation a novel solution towards converting the built environment into a "carbon bank". There are three major questions to resolve: how could varying biochar particle sizes impact tile integrity, can the innovation perform satisfactorily in standard tile dimensions amidst different ambient humidity levels, and can the tiles achieve the Class A fire standard of competing tiles. The iterative experimental protocols will utilize the classic American Society for Testing and Materials (ASTM) tests used to demonstrate building code compliance in all three of these research areas.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PIKE ROBOTICS INC.
SBIR Phase I: Autonomous Inspection Robot for Seal Inspection of Floating Roof Storage Tanks
Contact
2204 TOM MILLER ST
Austin, TX 78723--5381
NSF Award
2233637 – SBIR Phase I
Award amount to date
$274,703
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
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Abstract
he broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to develop an autonomous, robotic inspection system to eliminate manual inspection methods in confined and dangerous spaces. The innovation will use a robot to provide quantitative condition and emissions data for an estimated 150,000 floating roof tanks in the nation’s aging infrastructure. The Environmental Protection Agency (EPA) and similar agencies across the globe, require tanks to be routinely inspected, but companies also want to remove personnel from confined spaces and avoid exposure to poisonous gasses, asphyxiation, heat exhaustion, falling accidents, etc. Confined space entries caused 1,300 U.S. deaths between 2011 and 2018. The autonomous robot reduces expenses related to maintenance and repair and allows better quantitative assessment of the seal integrity, which allows companies to perform maintenance based on the asset condition and not frequency-based schedules. An industry trend in recent years is to recreate the state of their assets digitally - both visually and quantitatively. The high-resolution data provided through this effort supports these 'digital twins.' Also, once the robot is certified for explosive environments, design features can be reused to produce other robots which further improve development cost and safety.
This SBIR Phase I project will create an autonomous robotic inspection system which eliminates manual, confined-space inspection methods and requires only one technician to deploy the robot from the top of the tank wall. The wall-climbing robot is capable of inspecting both the floating-roof storage tank’s upper (weather) and lower (primary) seals using a bifurcated (inverse periscope) geometry which places sensors in the gap between the seals while prime movers, adhesion elements, and a controller are above the seals. The bifurcated design simplifies safety feature design by minimizing the size and complexity of device components between the seals. The bifurcated design enables the continuous use of a tether for isolating the power source and provides a positive pressure surge system. After insertion, the robot proceeds to circumnavigate the inner tank shell in a stable manner, overcoming the large frictional forces that exist between the tank wall and the upper seal. The robotic system gathers real-time, high-resolution, and continuous data about the state of the seals. High-resolution visual data characterizes the top of the seal, and a proprietary physical “feeler” mechanism characterizes the seal gap. This information results in a fast, safe, and accurate inspection of the tank’s seal.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PLANCK ENERGIES INC.
SBIR Phase I: CAS: Climate-Eco-friendly Biocoating for Passive Cooling of Infrastructure
Contact
150 HUNTINGTON AVENUE
Boston, MA 02115--6806
NSF Award
2321446 – SBIR Phase I
Award amount to date
$275,000
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project seeks to reduce global warming using an innovative passive cooling technique. The project will develop an environmentally friendly, passive cooling coating that can help reduce the temperature of buildings. The coating is expected to provide energy reductions ranging from 5-25%, depending on climate and building characteristics. The solution may reduce the need for traditional compressor-based cooling systems (e.g., air conditioners), which require a constant supply of electricity and coolants, stressing the environment through the greenhouse effect. The company expects to generate commercial revenues from both the paint-like coating featuring biocompatible passive cooling fibers and raw hydroxyapatite (HAP) fibers. Main customer target groups for the paint matrix include commercial and residential building owners that are looking for ways to lower their electric bills while lessening their negative environmental footprint.
The proposed innovation is founded on self-cleaning, fire-resistant, cooling fibers formulated with HAP. HAP cooling fibers will be integrated within a paint matrix for ease of application and cost-effectiveness. The aim is to develop and commercialize this environmentally friendly, passive cooling material as a coating with multiple functionalities. The team will identify environmentally friendly, paint-based materials within cost constraints, determine paint-fiber compatibility, and validate the cooling performance of candidate composites. The durability of the paint materials will also be confirmed. The project will focus on the design of a small-scale manufacturing line capable of producing fiber-based cooling paint at pilot scale for repeatability and field validation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
POLLUX TECHNOLOGIES LLC
SBIR Phase I: Internet of Things (IoT)-Enabled Smart Filter
Contact
10 DARREN CT
East Brunswick, NJ 08816--5154
NSF Award
2228149 – SBIR Phase I
Award amount to date
$269,498
Start / end date
06/01/2023 – 02/29/2024 (Estimated)
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the development of a novel solution for air filter monitoring that will have a positive impact on public health, the environment, and the US economy. The technology is based upon the direct measurement of the filter status using photosensors, smart signal processing algorithms for accurate filter soiling condition determination using the sensor data, and the Internet of Things (IoT) for control and communication. According to the World Health Organization (WHO), "Ambient air pollution kills about 3 million people annually... About 90 percent of the world's population is exposed to levels exceeding WHO limits." While air-filtration technology alone cannot solve the overwhelming problem of ambient air pollution, it must be an integral part of a comprehensive solution. With data driven decision making to eliminate premature filter replacement and to reduce costs, the proposed technology will propel the usage of high-quality filters more ubiquitously, leading to enhanced public health. With 150 million heating, ventilation and air conditioning (HVAC) systems in operation, and quarterly filter replacement, an estimated 600 million filters are manufactured and thrown away every year. A reduction of 50% of the filter waste will have a significant positive impact on the environment because of reduced manufacturing and waste.
This Small Business Innovation Research (SBIR) Phase I project will leverage the accuracy of photosensors in detecting the degree of filter blockage by sensing transmitted light through the filter. Underlying the seemingly straightforward solution is a set of complex technical challenges. Due to the uneven structure including pleats and frame obstructions, the sensor data are inherently noisy. A software-controlled actuator will place the sensor in front of the filter and take data from multiple locations of the filter. A smart algorithm will be developed to extract a parameter from the analysis of the data set that would be an accurate proxy for the particle size removal efficiency defined in the American Society of Heating, Refrigerating and Air-Conditioning Engineers standard which in turn is expected to be a sufficiently accurate indicator of the true filter age. Using the IoT capability, the sensor data will be collected in the cloud, where the smart algorithm and control software will be stored. The final objective is to determine the optimum point for filter replacement by comparing the parameter with a threshold parameter derived from a predetermined maximum particle size removal efficiency and airflow resistance based on indoor air quality requirements.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
POLYMER SOLUTIONS INC
SBIR Phase I: Versatile Polymers for Making New Components in Space and Eliminating Solid Waste
Contact
1820 THE EXCHANGE SE
Atlanta, GA 30339--2088
NSF Award
2231988 – SBIR Phase I
Award amount to date
$275,000
Start / end date
05/15/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to address the need for recycling plastics that have surpassed their useful life. The ‘plastic’s revolution’ of the mid-twentieth century has greatly added value to society but has also created highly durable plastics that are used to make objects of limited life-expectancy. There are limited cost-effective alternatives to many of these polymers. Every day, 8 million pieces of plastic are thrown into the ocean amounting to 10 million tons per year. Although there are approaches to decompose or depolymerize the polymers used in common plastic parts, these methods are not generally efficient across the dimensions of cost, energy use, time for degradation, or scalability. An extreme example of the need for energy and time efficient depolymerization of polymers is in space missions. There is a very high cost of disposing solids in earth’s orbit and removing space-junk which must be monitored and avoided by orbiting satellites. Even more challenging is the need for reusable materials during extended space missions due to the lack of raw materials.
This SBIR Phase I project proposes to develop a polymer-based plastics technology that allows for rapid, low-energy, triggerable disposal of plastics when a space mission has been completed. This project also proposes to carry out the disposal of plastics so that the products can extend their value and be recycled to make the same or different objects in space. Closing the polymer-carbon cycle has potential to extend space missions, lower the amount of supply materials needed, and reduce the amount of orbiting space junk. This project is developing a unique family of polymers which can be easily depolymerized back to the starting monomers via a photo or thermal trigger. The polymers are composed of cyclic, low ceiling temperature polymers. The low ceiling temperature means that once a single chemical bond in the polymer is broken, two ends are formed which instantaneously lead to depolymerization of the entire polymer molecule back to its original monomers. The depolymerized monomers can be evaporated to make the plastic parts ‘disappear’ or can be captured and used to repolymerize a new plastic component. This project will develop specific depolymerization triggers and a continuous-flow polymerization reactor for synthesizing plastic parts in space (and elsewhere for on-earth applications).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
POLYPV, LLC
STTR Phase I: Solution processed flexible semitransparent organic photovoltaic (OPV) modules for greenhouses
Contact
201 PROMONTORY POINT DR
Cary, NC 27513--6000
NSF Award
2213220 – STTR Phase I
Award amount to date
$256,000
Start / end date
05/15/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) project is to develop a disruptive technology that reduces the environmental impact of greenhouse-based agriculture while simultaneously improving its economic potential through the application of semitransparent organic solar cells onto the greenhouse glazing (i.e., windows). Greenhouses can be a form of high productivity farming that conserves land and water making them an attractive form of sustainable and climate resilient agriculture. However, greenhouses consume significantly more energy than conventional farming. For greenhouses to be a part of a sustainable agriculture future, there is a need to reduce their energy demand. Prior research has demonstrated that organic solar modules integrated into greenhouse structures may reduce or even eliminate external energy demand while not negatively impacting crop production. The global commercial market of conventional greenhouses will reach $50.6 billion by 2025. The growing greenhouse market translates to gigawatt solar power market size. The added economic benefit of organic solar module adoption in greenhouses provides a path for widespread adoption of organic solar modules and the growing greenhouse market.
This STTR Phase I project proposes to develop flexible, semitransparent, organic solar cells that are tailored specifically for greenhouse glazing integration. The organic solar cells will contribute to the energy production of such greenhouses and may completely eliminate greenhouse energy needs, providing a more environmentally sound form of agriculture. To make this vision of low energy demand greenhouses, there is a need to make high-performance flexible organic solar cells. This solution will be achieved through the optimization of the active layer, the electrodes, and the encapsulation processes. The three primary research tasks are to: (1) produce photoactive inks that are compatible with large-scale coating and have tuned transmittance; (2) achieve high transparency and physically robust, transparent, conducting electrodes based on silver nanowires produced using scalable coating methods; and (3) develop large area, low-cost, transparent, and flexible encapsulation layers. If successful, these solar cells will have advantageous operating characteristics not achievable with other solar cell technologies, providing a unique commercial opportunity.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
POWDER WATTS, LLC
SBIR Phase I: Low-Cost, Vision-Enhanced, High-Efficiency Heat Cable Control System
Contact
2750 RASMUSSEN RD
Park City, UT 84098--5492
NSF Award
2224907 – SBIR Phase I
Award amount to date
$274,922
Start / end date
07/01/2023 – 06/30/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be realized through the development of vision-enhanced, smart control for heat cables. Heat cables are installed on millions of roofs in North America to prevent build-up of roof-damaging ice dams but they currently have flawed, rudimentary controls, and consume large amounts of energy (tripling the energy consumption of a typical home during the winter months). Combining information from easy-to-install roof cameras, temperature sensors, and local weather forecasting, a machine learning system will turn on heat cables only when needed. A total of 8 billion installed feet of heat cable on roofs and gutters in North America annually consume 135 Terawatt-Hours of electricity and emit 52 Megatons of carbon dioxide and methane. Preliminary data indicate this consumption, the associated costs, and carbon dioxide and methane emissions can be reduced significantly, creating a large commercial impact for residential and commercial building owners, a payback period for the customer of one winter season, and a considerable decrease of the nation's carbon footprint. Because of heat cables' large electrical power consumption, the technology will also provide electrical utility companies with a tool to stabilize the electrical grid and load balance, contributing to national energy security and competitiveness.
This SBIR Phase I project proposes to pursue innovations to enhance the energy efficiency of heat cable systems. This system will including an energy harvesting system to power a roof-mounted, camera-based, sensor system that uses machine-vision and machine-learning to precisely control roof heat cables based on their primary function: the prevention of ice dams. Surprisingly, little is known about optimal heat cable control, including key input variables such as temperature, weather and the variability and role of roof features (type, angle, orientation). Collecting and analyzing these data will further the understanding of optimal heat cable control. Heat cable power consumption will be compared to historical and model-derived power consumption. Technoeconomic analysis will help to fine-tune and scale the revenue model. The energy harvesting technology based on trickle-charging the roof-based camera system battery will make the system cordless, easy to retrofit to existing installations, and low-maintenance.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
POWERSILO INC
SBIR Phase I: UpDraft Tower Technology for Geothermal Power Generation and Rankine Cogeneration
Contact
7250 REDWOOD BLVD
Novato, CA 94945--3269
NSF Award
2222965 – SBIR Phase I
Award amount to date
$255,966
Start / end date
01/15/2023 – 09/30/2023
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of technology that unlocks the use of abundantly available geothermal hot dry rock energy for reliable renewable energy. This technology will be economically feasible and provide an optional zero emission cogeneration configuration for harnessing cooling loop waste heat from zero emissions thermal electric power plants. The additional benefits, broader impacts, and market opportunity for cogeneration applications create an increase in power generation efficiency and capacity. Increases in net zero emissions power will also be available at utility scale. This technology will reduce water use during wet cooling in power plants by replacing the iconic supplemental cooling towers for thermal electric power plants worldwide with cogeneration. Some larger and long-term societal impacts of this research include: a more stable power grid due to reliable geothermal renewable energy generation and a cleaner environment especially for populations living close to traditional power plants and industrial infrastructure. Global technology licensing applications include: grid flexing and resiliency, water desalination/filtration, green hydrogen production, and national security.
This SBIR Phase I project seeks to develop software that uses computation, measurement, observations, and computer models, based on sound theory to find operational boundaries, validate key performance metrics, and optimize functional parameters for more efficient power production. This research includes the examination of critical technology functions and elements that determine peak operational efficiencies. The goal of this research will be to produce analytical computer models to look specifically at: 1) air intake velocity for a given set of pressure differentials, 2) air intake impedance, 3) thermal/pressure gradients generated by heat exchange activity, 4) air flow impedance generated by heat exchangers, and 5) expected exhaust air flow given idealized intake, heat exchange configurations, and designs. Anticipated results will provide quantifiable and measurable data tables including system sizing, energy input requirements, and mechanical and organic inlet air flow with emphasis on modeling of data analysis and determining specific energy inputs and power outputs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PRAG LLC
SBIR Phase I: Bioreactors for Upcycling Pyrolyzed Polystyrene Waste into Organic Fertilizer
Contact
171 FRANKLIN RD
Lake Mary, FL 32746--3609
NSF Award
2303842 – SBIR Phase I
Award amount to date
$274,718
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to enable the recycling of common polystyrene foam waste into soil amendments: creating a valuable agricultural product out of a pernicious and ubiquitous waste. Annually, millions of pounds of polystyrene waste fill landfills, blot roadsides, or pollute waterways – making up 80% of all ocean plastic waste. Taking centuries to decompose, when finally broken down polystyrene may have terrible impacts on health. This project seeks to combine proven technologies with newly discovered abilities in microorganisms to digest polystyrene, to demonstrate a means by which polystyrene can be reconstituted into nutrient-rich material useful in agriculture. Because of the vast supply of polystyrene waste and the great commercial need to dispose of it, this project taps into a commercial potential not only to provide waste disposal services to a much underserved market but can do so while simultaneously producing a valuable agricultural good. This project supports the NSF’s mission by advancing the science of bioremediation, advancing the health and welfare of the nation by removing a harmful waste product from the environment, and supporting national prosperity by providing a much-needed service to a large industry.
This project seeks to use a unique combination of technologies to demonstrate that polystyrene foam waste can be processed and bioremediated rapidly into a soil amending “castings” ready for use in gardening, farming, or landscaping. The project's research and development effort lies in the complex process of converting polystyrene from an unprocessed waste into both bioplastics and organic acids by way of thermal and biological methods, before further amelioration by decomposer organisms and the formation of a usable agricultural product. Bioreactors featuring numerous strains and species of microbes never-before deployed for this purpose will be used in tandem with macro-organisms whose capabilities for this application are likewise mostly or entirely unstudied. This research will uncover the specific abilities of numerous species to digest polystyrene waste at multiple scales and will evaluate several potential pathways through which the resulting digestate could be further processed. Large sample sizes, stepwise variances in conditions, and permutations of species’ combinations will be used to ensure statistical veracity. These methods, coupled with the use of cutting-edge analytical equipment, will ensure a high precision in results.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PRAIRIELEARN, INC.
SBIR Phase I: An online learning and assessment platform for sophisticated and secure exams
Contact
60 HAZELWOOD DR
Champaign, IL 61820--7460
NSF Award
2304241 – SBIR Phase I
Award amount to date
$274,981
Start / end date
08/15/2023 – 07/31/2024 (Estimated)
Errata
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Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to provide a robust and sophisticated assessment tool to a wider range of STEM (Science, Technology, Engineering and Mathematics) educators to improve student learning, make teaching more efficient, and reduce the incidences of cheating. The core technology of this technology is an online platform for creating and delivering high-quality assessments that are auto-graded by artificial intelligence (AI) algorithms, providing immediate feedback to students. The technology provides students with the opportunity to practice questions in a personalized environment until mastery is achieved. The auto-grading features reduce grading effort, allowing instructors to focus on course design, incorporate more frequent and second-chance testing, and have more time to directly help students. The platform can automatically generate and grade personalized assessments for each student, which helps to minimize cheating and enables repeated practice by students. This learning experience is suited to help minorities, first-generation college students, and students of low socioeconomic status, who have traditionally had less access to the highest quality human instructors. Making STEM education more effective will facilitate the creation and continuing support of a highly educated STEM workforce and is important for national competitiveness in related fields.
This Phase I project aims to develop a no-code, graphical authoring environment that will allow instructors without prior programming experience to create AI-based auto-graded content. Instructors will be enabled to create sophisticated, auto-graded assessments by combining the existing core AI technology of this project with the following innovations: a) a no-code, graphical authoring using block-based language and data-flow visualizations; (2) new AI auto-graders for structured data, such as student data analyses within spreadsheets, by using verification algorithms to specify and check constraints on student answers; and (3) a graphical interface to use the new AI auto-graders for structured data, including associated data-flow visualizations. All three of these new capabilities will be evaluated via user-focused studies with a small group of instructors from a variety of backgrounds and programming skill levels, ranging from novice to expert. These semi-structured qualitative studies will follow a grounded theory approach, addressing metrics specific to each objective.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PRENOSTIK, LLC
SBIR Phase I: A Student Learning Dashboard
Contact
21 MEADOW GLN
Irvine, CA 92602--1625
NSF Award
2232826 – SBIR Phase I
Award amount to date
$274,471
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in improving retention in higher education and increasing graduation rates. Currently, the average U.S. college dropout rate is 40%. Moreover, underserved Science, Technology, Engineering and Mathematics (STEM) student populations are more likely to leave school without a degree. Due to the COVID-19 pandemic, increased financial insecurity and mental health challenges have negatively impacted student learning. This project aims to develop a student learning dashboard platform that acts as a co-pilot during students' higher education learning journey by delivering targeted, personalized, and real-time actionable assistance. The solution holistically identifies each student's unique learning motivation challenges (e.g., subject difficulty, relevance to career goals, social and economic constraints, etc.) and provides specific recommendations to overcome barriers. Coaching students to learn how to learn more effectively based on their own context fosters a growth mindset, grit, and agency to help them become successful lifelong learners. The application also significantly improves diversity, equity, and inclusion in higher education, especially in STEM, and thus increases effective workforce training.
This Small Business Innovation Research (SBIR) Phase I project uses machine learning to understand each student's unique learning challenges, map how barriers affect learning motivation, and influences coursework engagement. Machine learning is applied to analyze qualitative and quantitative learning motivation and behavior data to identify gaps so real-time, targeted, and relevant guidance can be delivered while the students are still progressing through the courses rather than waiting until it might be too late for intervention. This project provides descriptive, predictive, and prescriptive recommendations to simulate one-on-one, personalized advising at scale and at a lower cost. The technology also acts as an early detection system when students show the first sign of academic and non-academic struggles affecting their mental state of readiness to learn. When in-person human intervention is required, instructors, academic advising, and/or relevant on-campus student support services can be alerted. This project can be used by any educational institution or private company providing in-person, flipped/hybrid, remote, synchronous, or asynchronous instruction formats.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PROBLOCH LLC
SBIR Phase I: Distributed Ledger Technology Based Collaborative Project Management Platform
Contact
2207 SOUTHERN OAKS DR
Austin, TX 78745--2730
NSF Award
2230205 – SBIR Phase I
Award amount to date
$251,462
Start / end date
05/01/2023 – 10/31/2023 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve the performance and accountability of capital projects which have significant economic and social impact. Large capital projects, such as infrastructure and energy, have long-term benefits to economies, affect local communities, and have geopolitical implications. Projects in important sectors such as defense, science, and healthcare often suffer massive cost and time overruns, making efficient and verifiable project execution highly beneficial. These projects are very complex and challenging to manage; they require multiple internal stakeholders to work together and affect many external communities. The proposed solution is a platform that enables self-regulating project networks and integrates schedules, contracts, and information flow across companies. This platform will be based on distributed ledger technology and smart contracts, making them transparent, efficient, and secure. The proposed solution will create a new, reliable, measurable paradigm for capital projects and allow multiple companies to collaboratively plan and collectively build them. It will also aid regulatory oversight, and projects will include all stakeholders. The platform will be revolutionary for complex projects planning and building, significantly improving cost and schedule performance, and bringing greater accountability to the multi-trillion dollar capital project market.
This SBIR Phase I project proposes to develop a platform using blockchain technology that improves the performance and accountability of capital projects. The technology will enable significantly improved transparency, while making projects more cost and schedule efficient. The platform will result in more responsive and responsible symbiotic project ecosystems and advance the application of directed, acyclic, graph based distributed ledger technology to multi-party enterprise workflows. The collaborative governance platform will enable self-regulating project networks of authenticated stakeholders, utilizing a consensus-based single-source-of-truth, and automate contract execution and value exchange. The platform will synchronize schedules, contracts, and payments into one system with verifiable consensus across all participants. The technology will incentivize project performance by mitigating the effects of complexity and enable artificial intelligence and machine learning applications for projects by providing verifiable data. The focus will be on developing the architecture and business model of the platform. The team will conduct market research to identify the primary commercial consumers and potential revenue streams. The project will build a proof-of-concept to demonstrate the feasibility of the platform. The anticipated results include a functional prototype of the platform and a validated business model.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PROJECT VESTA, PBC
SBIR Phase I: Enhanced Blue Carbon: a novel carbon dioxide removal strategy for climate change mitigation
Contact
1210 26TH ST
Denver, CO 80205--2100
NSF Award
2246965 – SBIR Phase I
Award amount to date
$273,994
Start / end date
08/15/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to combat climate change, enhance coastal resilience, and counter ocean acidification. This project will develop a nature-based approach that deploys Olivine sand safely into salty marshes for atmospheric carbon dioxide removal (CDR) while restoring marsh ecosystems. This project will contribute societal value by driving innovation and new approaches in Earth Sciences, Ocean Sciences, and Marine and Coastal Engineering. It will produce a rigorous, cost-effective method for carbon dioxide removal to generate carbon credits that can be bought on carbon exchanges to achieve net-zero emissions. Once at scale, the technology could permanently capture millions of tons of carbon/year at < $100 per ton. The impact on the lives of US citizens could be significant, both through green job creation and through climate crisis mitigation.
This project will develop Enhanced Blue Carbon (EBC), a novel, nature-based, carbon dioxide removal (CDR) technology that combines and improves two of the most cost effective, scalable, and permanent CDR technologies: Ocean Alkalinity Enhancement (OAE) and Blue Carbon (BC). The proposed research consists of a field trial that will produce a carbon quantification model, ecological impact report, and Life Cycle Analysis (LCA) determining the efficiency and safety of EBC. This experiment is the first of its kind and is a critical step in developing a third-party accredited measurement-reporting-verification (MRV) methodology that directly connects EBC to carbon markets. The efficiency and safety of EBC will be assessed through geochemical and biogeochemical characterization of porewater, sediments, and biomass collected during the field trial. This data will be integrated in a proprietary 1-dimensional reactive-transport model that can be used to estimate carbon removal potential in EBC projects.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PROMEDIX, INC.
STTR Phase I: Electronic Measurement of Capillary Refill Time to Improve Outcomes from Sepsis
Contact
4640 S MACADAM AVE
Portland, OR 97239--4232
NSF Award
2212728 – STTR Phase I
Award amount to date
$255,750
Start / end date
02/15/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is a novel external system for rapidly diagnosing sepsis by measuring capillary refill time (CRT). Independent clinical studies have demonstrated the utility of CRT for detecting sepsis. Current methods for monitoring capillary refill times rely on physical examinations that are both prone to human error and inconsistency. The company aims to develop an automated diagnostic and monitoring device for objectively and repeatably quantifying capillary refill time for use in a clinical setting. If successful, the technology may have widespread potential use in emergency departments, clinics, ambulances, or at home.
This Small Business Technology Transfer (STTR) Phase I project develops a new finger-sensor interface for monitoring CRT that ensures contact between the finger and sensor across a range of finger sizes and validate the system in human use. The objectives are to ensure human factors engineering to enable use in a broad range of patients by a wide range of caregivers. A novel algorithm to improve sensor performance and provide user feedback on noise or aberrant signals will also be integrated. The system will be tested in a group of patients at risk for sepsis to demonstrate the device reliably and accurately measures the CRT across a wide variety of patient demographics and the device is easily usable by a wide range of caregivers including physicians and family members without extensive training. A successful Phase I outcome is a system enabling the consistent ability to collect high-quality measures of CRT in patients at risk for sepsis and to provide the user with ongoing measures of signal quality.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PROTEIN ENGINES LLC
SBIR Phase I: Nonlinear optical method for identifying protein-ligand binding sites
Contact
4500 9TH AVE NE STE 300
Seattle, WA 98105--4762
NSF Award
2111821 – SBIR Phase I
Award amount to date
$256,000
Start / end date
12/01/2021 – 10/31/2023 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to develop a technique that can reveal where precisely a small molecule, such as a potential drug, interacts with a protein on its surface. Proteins, particularly when mutated, are among the most frequent drivers of human disease. Drugs exert their effects by binding to the surface of a disease-causing protein and turning off the protein or otherwise changing its shape, which is critical for a protein’s function, so that the disease process is ameliorated or even halted. To develop effective drugs, a key piece of information is how and where on the surface precisely a small molecule binds to its target protein. This information is often challenging to obtain using conventional techniques such as X-ray crystallography or nuclear magnetic resonance (NMR). This project will develop a technique that could be used broadly for drug discovery.
This Phase I project aims to develop a technique using a nonlinear optical method to identify the binding location of a small molecule on the surface of two cancer-causing proteins. Two proteins with small molecule ligands will be used to demonstrate feasibility of the technique, for which X-ray co-structures exist, providing a means to benchmark the findings of the proposed technique. The proposed technique will be based on a nonlinear optical technique that is sensitive to protein structure and ligand-induced conformational changes. The proteins will be labeled and detected by the optical technique, enabling measurements which will be analyzed to determine a ligand’s binding site.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
PROTIUM POWER SYSTEMS INC.
STTR Phase I: Next Generation Residential Solar Power
Contact
85C BASSETT HWY
Dover, NJ 07801--3819
NSF Award
2208341 – STTR Phase I
Award amount to date
$256,000
Start / end date
07/01/2023 – 12/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is a residential solar power generation technology that could provide a significant improvement to the United States and world goals to reduce carbon emissions. This project combines several breakthrough technology developments to make solar-generated electricity practical for the consumer, installer, and the power company. The breakthroughs are not in photovoltaic (PV) efficiency, but instead, focus on technology that significantly increases dependability (reliability, accessibility, and maintainability); makes installations easier; and makes residential solar energy suitable for large-scale power generation for utility companies. The target customers are regional companies that sell and install solar energy systems on residential rooftops. These companies are sensitive to the reliability (warranty), affordability, performance, and safety of the equipment that they install. There are about 1,000 solar power installation companies in the US, and they are outfitting homes at the combined rate of almost one per minute. The solar energy market is currently valued at $17 billion dollars annually and is expected to double in size in the next 5 years.
This STTR Phase I project proposes to develop an innovative architecture that consists of modular PV panels, unique non-flammable battery packs, patent-pending high efficiency converters, and a sophisticated interface to utility grids. There are no power combiners, external charging systems, communications boxes, current transformers, load switches, sub-panels, inverter boxes, power optimizers, or bulky battery packs involved. The product provides a much-needed breakthrough for the utility industry as it enables residential solar power generation to be a suitable power source for large-scale connection to existing power grids.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
Pensievision, Inc.
SBIR Phase I: An Endoscopic 3D Imaging System for the Evaluation of Cancer and Other Disorders of the Esophagus and Pharynx
Contact
10179 HUENNEKENS ST STE 207
San Diego, CA 92121--2965
NSF Award
2304612 – SBIR Phase I
Award amount to date
$255,947
Start / end date
05/01/2023 – 01/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project regards the ability to achieve early detection of cancers of the esophagus and throat. This project targets a multimillion-dollar annual market and has the potential to positively impact rural and underserved communities without access to specialty endoscopy clinics. The project also has the potential to positively impact the African American community, where the incidence of esophageal cancer is higher.
This Small Business Innovation Research (SBIR) Phase I project aims to assist clinicians in detecting tumors of the esophagus and throat at an earlier stage, which will have life-saving effects. This project will combine liquid lens technology with artificial intelligence software and astronomical imaging techniques, with the objective of generating three-dimensional images of early stage esophageal and throat tumors. This particular study will focus on acquiring quality three-dimensional images of a cadaver’s esophagus and pharynx to prepare for the next stage of testing and development.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
Precast Systems Engineering, LLC
STTR Phase I: Development of an Innovative Ultra High Performance Concrete Foundation System with Bio-inspired Surfaces to Support Renewable Offshore Wind Turbines
Contact
5320 CHESAWADOX DR
Exmore, VA 23350--4302
NSF Award
2222232 – STTR Phase I
Award amount to date
$274,956
Start / end date
01/15/2023 – 12/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact of this Small Business Technology Transfer (STTR) Phase I project is to develop a marketable and cost-effective U.S. manufactured foundation system to support offshore wind turbines (OWTs). The planned offshore wind energy production in the U.S. has been growing rapidly and the industry is expected to be worth more than $1 trillion within the next two decades. Although only 42 megawatts (MW) of offshore wind energy were installed in the U.S. during the last 10 years, planned projects have been growing rapidly targeting 30 gigawatts (GW) by 2030 and 110 GW by 2050, with strong support from coastal states. Achieving the targets of offshore wind energy requires cost-effective and innovative components of these energy systems. One of the main costs for offshore wind energy systems is their foundations, with costs typically ranging from 14% to 34% of the overall project cost. OWTs are commonly supported on large-diameter foundations, which the U.S. does not have the capability to fabricate and instead relies on foundations fabricated abroad. Therefore, a U.S.-manufactured foundation system to support renewable offshore wind energy infrastructure, enhance domestic supply chains, and reduce dependency on foreign manufactured foundations is proposed. The result of this research is a U.S.-manufactured alternative with savings of over half the cost per meter, enabling wider adoption of alternative energy harnessing technologies.
The goal of the proposed project is to develop a U.S.-manufactured, bio-inspired, enhanced capacity foundation system to support offshore wind energy infrastructure that provides technical improvements and cost-saving to currently used systems. The proposed project also provides: (1) ease of adoption by providing similar weight and installation approaches to current means and methods; (2) better durability and longer service life than currently used OWT foundations; and (3) improved speed of construction promoting scalability. Furthermore, the proposed system will allow for optimized design, increasing the foundation capacity and improving the installation process. Preliminary tests show that the proposed design could improve the foundation capacity by up to 100% compared to that of the currently used foundation systems when subjected to long-term cyclic loading similar to those experienced by OWTs. The proposed concept could be used as a driven pile, suction caisson, anchors, or gravity base providing several options for the offshore wind energy industry in the U.S. To achieve the project goal, this research will focus on: (1) verification of key material properties for marine environmental conditions, (2) structural design of foundation cross-sections, (3) installation analyses on proposed foundations in marine environments; and (4) investigations of the effects of the bio-inspired design on foundation capacity.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
QUANTUM ENERGY, INC.
SBIR Phase I: An impact analytics platform combining energy system optimization and life cycle assessment
Contact
10 E YANONALI ST
Santa Barbara, CA 93101--1875
NSF Award
2230578 – SBIR Phase I
Award amount to date
$255,960
Start / end date
07/01/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project focuses on data-driven support for optimal energy decisions. The software platform proposed in this project will allow for commercial deployment of an accessible, user-friendly tool to rapidly determine a more complete picture of human health and ecosystem impacts as a result of energy decisions. Through the development of a public-facing ‘Impact Tracker,’ this solution will provide a means for leaders to communicate the impacts of their energy decisions to the public and climate-conscious international investors, improving the public’s energy literacy and engagement, as well as increasing the economic competitiveness of the United States.
This Small Business Innovation Research Phase I project proposes to develop a commercial software platform to support optimal energy decisions. Energy decisions made by large corporations and governments have substantial impacts on human health, ecosystem quality, and biodiversity extinction. The life cycle impacts of these decisions are often inaccessible due to the time, data and financial resources required to collect the numerous, disparate, non-standardized datasets and evaluate the multiple complex modeling that is required. To overcome these limitations, this team will develop a cloud-based, impact analytics software platform by 1) building an integrated energy system optimization and life cycle assessment model that is compatible with a broad range of geographies and electricity grid configurations and 2) developing a data integration tool for automated collection of the required data from multiple non-standardized, often internationally housed databases. The anticipated results of this work will be a first-in-class, easy-to-use, and highly accessible software platform that is accurate across varying geographic regions and electricity grid configurations, allowing for this tool to have national and global impacts. Overcoming these challenges will require a combination of machine learning approaches with human involvement, known as expert-augmented machine learning.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
QUANTUMMED INC
SBIR Phase I: Metal-implanted materials (MIMs) for fast, cost-effective and reproducible mixing
Contact
201 WEST 5TH STREET SUITE 1500
Austin, TX 78701--0061
NSF Award
2303540 – SBIR Phase I
Award amount to date
$268,521
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project includes improving the reproducibility of automated life science workflows while simultaneously reducing their carbon footprint. Automated life science workflows are increasingly prevalent in medical (e.g., laboratory tests and diagnostics) and research (e.g., next generation sequencing) applications, and mixing is a ubiquitous and often repeatedly performed operation in these assays. The novel mixing technology to be developed will confer myriad benefits. By reducing assay turnaround times and improving assay reliability, it will decrease wait times for medical screening, diagnosis and monitoring, enabling faster diagnoses and treatments. By decreasing the materials costs of the assays, this technology may reduce the costs of medical testing and improve access to care. It also can enhance partnerships between academic and industry laboratories by giving academic laboratories access to industry workflows that are currently prohibitively expensive. Finally, by eliminating a substantial portion of the single-use plastic consumed by assays, this novel mixing technology will help curb the waste generated by life science assays, which will help alleviate the single-use plastic waste crisis.
The proposed project will deliver an innovative mixing technology that is based on a photo-acoustic streaming phenomenon. Briefly, when glass implanted with metal nanoparticles (metal-implanted materials (MIMs)) is excited by a pulsed laser, it causes an adjacent fluid (liquid or gas) to begin streaming for the duration of the illumination. This streaming creates an opportunity to precisely control mixing, but key technical challenges include optimizing the MIMs’ form factor and devising an effective, yet also inexpensive, illumination system. The proposed project’s objectives address these challenges by: (i) evaluating the effectiveness of mixing solutions with a novel MIM form factor that can be incorporated easily into existing automated life science workflows, (ii) determining if functional MIMs can be fabricated in bulk by procuring them from a supplier and characterizing them for nanoparticle implantation and laser-induced solution streaming, and (iii) testing an alternative laser light source that powers mixing and consists of a low-cost light emitting diode (LED) laser.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
QUBITSOLVE INC.
SBIR Phase I: Computational Fluid Dynamics Software for Quantum Computers
Contact
2006 WHITE OAK DR
Morgantown, WV 26505--2465
NSF Award
2318334 – SBIR Phase I
Award amount to date
$275,000
Start / end date
09/01/2023 – 02/29/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project will be to utilize quantum computing to perform computational fluid dynamics (CFD) simulations, initially to solve problems in the aerospace and automotive industries. The technology may also play a vital role in national defense, as CFD is critical in designing aircraft, missiles, armored vehicles, and naval systems. Subsequently, the product will find many other industrial applications, including healthcare applications, such as simulating blood flow in organs or airflow in the lungs. The product will help companies to reduce the cost and time in developing superior technologies, such as safer aircraft that consume less fuel, energy plants that emit less carbon dioxide (CO2), and devices that deliver drugs more effectively. The project includes a partnership between academic and industrial researchers. By involving student interns and exposing them to the emerging field of quantum computing, this project aims to contribute to science, technology, engineering and math (STEM) workforce development. Ultimately, this project aims to create a new generation of CFD technology based on quantum computing, advancing the CFD field, and aiding U.S. leadership in quantum computing.
This Small Business Innovation Research (SBIR) Phase I project will develop a new CFD technology for quantum computers. CFD is a valuable tool for engineers to predict fluid flow and design or troubleshoot various systems such as airplanes, automobiles, and chemical reactors. However, some CFD simulations are too expensive or impossible to run, even though such knowledge could save hundreds of millions of dollars in certifying each new aircraft type. High resolution slows CFD simulations because large amounts of data must be stored in, read from, and written to the computer memory. This limitation can be overcome by utilizing the vast quantum state spaces that have become available with the introduction of quantum computers. An algorithm will be implemented in a software prototype and the flow between fixed and moving plates will be simulated on quantum computers. A performance metric estimated from the simulation results will assess the potential for achieving a quantum advantage.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
RAPA TECHNOLOGIES LLC
SBIR Phase I: Comfortable, Easy-to-Insert Hearing Protection Earplug
Contact
64 BONNER RD
Meriden, NH 03770--5151
NSF Award
2233135 – SBIR Phase I
Award amount to date
$274,976
Start / end date
07/15/2023 – 06/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a novel hearing protection device which reduces the societal cost of noise induced hearing loss. Noise induced hearing loss is the one of the most prevalent occupational injuries in both US industry and the military, affecting more than 10 million workers at a total preventable economic cost exceeding $100 billion. Most employers rely primarily on personal hearing protection devices to limit exposure on a sustained basis. However, issues limit their real-world performance and leave a majority of wearers with sub optimal protection. Up to 80% of workers wear hearing protection in an inconsistent manner which significantly reduces their effectiveness. This project aims to develop a novel passive hearing protection device which significantly increases hearing protection by incorporating a pass-through communications channel in a form factor that enables proper insertion, comfort, and convenience, and is suitable for long term use and compliance.
This Small Business Innovation Research (SBIR) Phase I project will develop an earplug that incorporates a novel geometric design and materials to provide unique, tailored physical and acoustic properties. The design significantly increases sound reduction while preserving frequency balance and speech intelligibility to accommodate pass-through communications, in a form factor that enables greater comfort and convenience than traditional devices. Theoretical modelling of sound attenuation will be translated into prototypes that demonstrate performance measures in laboratory test fixtures, followed by a patient validation study. The project aims to demonstrate that the company’s new ear plug provides superior sound protection of up to 40 dB suppression while enabling improved communication. Incorporation of attributes preferred by users enables greater adoption compared to existing designs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
RAPID FORENSIC CELL TYPING, INC.
STTR Phase I: Advancing DNA Testing with a Novel Platform for Processing Touch Biological Evidence
Contact
800 E LEIGH ST STE 1234
Richmond, VA 23219--1539
NSF Award
2243209 – STTR Phase I
Award amount to date
$270,578
Start / end date
07/01/2023 – 06/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project addresses two societal issues in the justice system: an individual's constitutional right to a speedy trial, and inherent human biases in law enforcement when gathering and processing evidence. Backlogs in processing DNA evidence can lead to extended jail time for defendants unable to post bail and may violate their rights, particularly for crimes they did not commit. Rapid analysis of evidence samples can free up court dockets and save money for institutions holding people awaiting trials due to testing backlogs. In addition, the number of people exonerated by the re-analysis of forensic evidence after serving years in prison rises every year, underscoring the potential impact that testing decisions can have for individuals, families, and society at large. This technology reduces the number of samples tested and the potential for sample selection bias by rapidly identifying which samples may be probative to the investigation and thus warrant DNA testing.
The proposed project will develop a new technology that utilizes flow cytometry to analyze non-genetic attributes of cell populations within forensic evidence. This technology will allow forensic laboratories to rapidly determine the probative value of samples before DNA profiling. Machine learning algorithms will compare morphological measurements and autofluorescence properties of individual cells recovered from ‘touch’ epidermal cells to identify features that vary with attributes of the person who deposited the cells (e.g., chronological age, biological sex, and/or ancestry). This technology will enable forensic laboratories to rapidly identify which samples have biological material that is probative to the case and which samples have biological material that is unrelated. This will allow labs to prioritize samples for DNA testing more precisely and potentially provide key contextual information for the sample. By allocating resources more efficiently, this innovation will reduce costs, speed up results and reporting, and reduce delays in DNA testing turn-around times. The solution will also prevent undue delays in the legal system, improve the accuracy of case analysis, and ultimately improve the quality and reliability of forensic analysis.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
RAPIDECT INC
SBIR Phase I: A near real-time analyzer for MRSA screening and diagnosis of MRSA infections
Contact
32832 SPRINGSIDE LN
Solon, OH 44139--2067
NSF Award
2041861 – SBIR Phase I
Award amount to date
$256,000
Start / end date
05/15/2021 – 12/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project are significant. The prevalence of multi-drug resistant organisms (MDROs) or superbugs, which include S. aureus and methicillin-resistant S. aureus (MRSA), is one of the greatest threats to public health. Annually in the United States, over 2 million people acquire MDRO infections, leading to at least 23,000 deaths. Further, MDRO infections has been recognized as co-infections of COVID-19 that complicates the therapeutics of the pandemic disease in the healthcare environment. The analyzer will lead to fast containment and rapid diagnosis of MRSA and S. aureus infections. When its capacity is expanded to include other MDROs, the analyzer will allow clinicians to significantly enhanced treatment efficacy, leading to decreased morbidity and mortality, reduced costs of treatment and hospital stay, reduced prevalence of MDROs.
This Small Business Innovation Research Phase I project will address the long time-to-result of current MRSA testing technologies. The current culture-based diagnosis of bacterial infections requires 16-48 hours to produce results. The long diagnosis-time leads to overuse of broad-spectrum antibiotics (BSAs), resulting in under-treatment, severe side effects, morbidity and mortality as well as the development of MDROs. The culture-free analyzer will complete the diagnosis in 120 minutes. The analyzer will allow clinicians to limit the use of BSAs and start using narrow spectrum antibiotics in the early stage of treatment to enhance efficacy and reduce the prevalence of MDROs. The goals of the project are: (1) To construct a prototype analyzer, which will provide simultaneous diagnosis on multiple samples in 120 minutes, and (2) To conduct a small-scale characterization of the clinical performance of the prototype with clinical samples to establish its credibility as a clinical diagnostic technology. The prototype analyzer will consist of a multi-channel signal acquisition electronics console and detection plates. The detection plate will contain an array of bacteria-specific detection electrodes and will be inserted into the console and operated by the console for measurements. The analyzer will detect MRSA in clinical samples and distinguish MRSA from S. aureus.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
RATTAN LIFE SCIENCE INC.
SBIR Phase I: Engineered Induced Thymic Epithelial Cells for Novel T Cell Immunotherapies
Contact
893 RATTAN TER
Sunnyvale, CA 94086--8642
NSF Award
2234041 – SBIR Phase I
Award amount to date
$275,000
Start / end date
06/15/2023 – 05/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to develop novel off-the-shelf T cell immunotherapies. Adoptive Cell Therapy (ACT) has revolutionized medicine for cancer patients, as evidenced by the remarkable success of CAR-T therapies in treating advanced and refractory leukemia and lymphoma. Despite the success in blood malignancies, solid tumors, representing approximately 90% of cancers, remain difficult to cure. To eradicate large tumor masses and reach complete remission, successful ACT requires persistent, in vivo anti-tumor effects. Studies have highlighted the correlation between greater ACT efficacy and transferring T cells with capacity of in vivo expansion and memory formation. Among major T cell subsets, naïve T cells have been identified as the optimal cell source for ACT compared to further differentiated cell types. In vivo, naïve T-derived effector cells demonstrate robust proliferation, potent tumor-killing and resistance to terminal differentiation and exhaustion. In vitro, these cells have significantly higher efficiency for blood malignancies and solid tumors. This project may enable large-scale and renewable production of homogenous T cells with optimal and persistent tumor-killing properties. This approach aims to address the unmet challenges of T cell exhaustion, improve scalability, reduce repeated blood collection, and offer broad patient access.
The proposed project aims to develop a platform technology for generation of iPSC-derived naïve CD4+ and CD8+ T cells with fidelity, reproducibility and scalability. The platform employs a proprietary method to generate iPSC-derived thymic epithelial cells as a critical element to enable naïve T cell production. The rationale resides in the natural biology of the Thymus, where the transition of immature CD4+CD8+ double positive T cells to naïve CD4+ or CD8+ T cells requires interaction with thymic epithelial cells through a process called positive selection. Concerns have been raised regarding the therapeutic efficacy associated with current Notch-activation based iPSC-derived T cell methods because T cells developed through sole Notch activation are phenotypically and functionally different from naïve T cells. Major technical limitations in Notch activation-based extrathymic differentiation methods are addressed by providing biologically relevant thymic positive selection signals. The resulting product enables a significant advance in the development of iPSC-based T cell immunotherapies with clinically relevant cell fidelity. Reproducibility and scalability of the proposed platform will be assessed and optimized in a bioreactor to demonstrate viability for commercialization.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
RED SHIFT ENERGY, INC.
SBIR Phase I: Carbon-Free Hydrogen Production by Plasma Dissociation of Hydrogen Sulfide
Contact
5921 KING TRL
Corpus Christi, TX 78414--6312
NSF Award
2233170 – SBIR Phase I
Award amount to date
$275,000
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
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Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is connected to creating a large-scale source of low production cost, carbon-free hydrogen. This hydrogen will be produced from hydrogen sulfide (H2S). Nearly 8 million tons of H2S are processed by the energy industry each year. Sulfur recovery units (SRUs) are used to safely manage H2S. SRUs utilize an old Claus process and are unprofitable because of high capital and operational costs, in addition to low revenues due to sulfur overproduction. In contrast to the Claus process, H2S plasma dissociation recovers sulfur and hydrogen, whereby the sulfur can be used for oil desulfurization or as a commercial product. Dissociating H2S in plasma and producing hydrogen will make SRUs profitable and will reduce the industry's carbon dioxide emissions. This technology will diminish societal needs for fossil fuel production and increase energy security during the transition to renewable energy. The team will develop a numerical model for the high-speed, two-phase, vortex flows that will have a general academic interest and can be applied in the chemical and energy industries.
This SBIR Phase I project proposes to develop a plasma technology for the dissociation of H2S to sulfur and hydrogen, replacing Claus plants in Sulfur Recovery Units. Phase I will focus on three innovations that are critical for the H2S dissociation development program. The major goal is the high energy efficiency expressed as Specific Energy Requirement 1.5 kWh/m3. This goal will be achieved by a special design of the arc plasmatron with an extremely high speed of gas rotation that will result in hydrogen-sulfur separation in the reaction zone, the chemical equilibrium shift, and the internal recuperation of the sulfur clusterization and condensation energy. The second innovation will be the development of a gas-dynamic and chemical-kinetic model for the numerical simulation of two-phase (gas and sulfur particles) vortex flows with a high speed of rotation. Third, the stability of the cathode and plasmatron operation will be tested with different gas mixtures that imitate the composition of real flows at refineries.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
REEGEN INC
SBIR Phase I: A clean, biological solution to sustainable energy’s rare earth problem
Contact
343 CAMPUS RD
Ithaca, NY 14853--6007
NSF Award
2304412 – SBIR Phase I
Award amount to date
$275,000
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to reduce the negative environmental impacts of rare earth element (REE) production through the development of a clean, sustainable system for REE extraction and purification using biology. Such a system would allow for affordable, low-impact REE production in the United States which, in turn, would reduce dependence on REE imports, alleviating a significant supply risk and concerns for national security. REEs are critical for manufacturing many modern electronics and sustainable energy technologies, including electric motors and wind turbine generators, solid state lighting, battery anodes, high-temperature superconductors, and high-strength lightweight alloys. Such applications are increasing demands on the global REE supply, which is predominantly controlled outside of the United States due to the cost of environmental regulations and labor. Nearly all REE production today comes from mining ore, which can cause its own environmental detriment, and will not be able to meet the rising demand for REEs. To bridge the gap between supply and demand, and attenuate the impacts of mining, REEs will be recovered from various waste and end-of-life sources, promoting a circular economy. The recovery of REEs from secondary sources would create new jobs, especially with the development of new infrastructure for the collection and pre-processing of REE-containing materials.
The output of this SBIR Phase I project is an end-to-end biological system for REE recovery that can replace the most environmentally damaging steps from source to market, including bio-extraction, selection, and separation of REEs. The use of microorganisms for each step allows for a much cleaner process, and genomic optimization for rapid customization to a variety of REE feedstocks. REE bio-extraction is done with biodegradable lixiviant produced by optimized microbial strains. Bio-selection is done with REE-specific ligands immobilized in a synthetic biological matrix. Finally, bio-separations are done through the selective sorption and desorption of different REEs to engineered bacterial membranes in columns that bind specific REEs with different affinities. Genetic customization is enabled through comprehensive identification of the genetic elements underlying a trait of interest, followed by incorporation of genetic engineering for optimization of the overall commercial process. In Phase I of the project, efforts are focused on the identification of the variables that most contribute to efficiency, as well as the genetic mechanisms driving those variables.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
REMEDIUM BIO, INC
SBIR Phase I: Development of an Adjustable Gene Therapy Platform Technology
Contact
1116 GREAT PLAIN AVE
Needham, MA 02492--2344
NSF Award
2240683 – SBIR Phase I
Award amount to date
$274,966
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impacts of this Small Business Innovation Research (SBIR) Phase I project include the development of a gene therapy platform that will allow the use of genetic therapeutics in areas beyond rare diseases, tackling large unmet clinical needs such as diabetes, osteoarthritis, and autoimmune diseases. The project will also enable significant reductions in the costs of gene therapies and protein-based therapeutics. Finally, the development of the proposed platform technology will enable the production of medical treatments that are injected less frequently, produce a potentially more optimal treatment profile, and prevent complications related to missed doses or therapeutic overdose. The societal and commercial impacts of this technology are significant, as the proposed technology could greatly expand the potential of gene therapy, while replacing other biologic-based therapies with a lower cost alternative. The technology has significant commercial value but is, at the same time, able to greatly reduce societal medical costs associated with current treatment approaches.
This project develops a dose-adjustable gene therapy mechanism that can be used to up- or down-regulate a gene therapy dose, following initial administration. Despite recent advances and regulatory approvals, gene therapy remains limited due to its inherent shortcomings in dose adjustment – once a gene therapy dose is administered, it cannot be increased or decreased by secondary intervention. On the other hand, many therapeutics require adjustment of the initially prescribed dose over a period of weeks or months to optimize the efficacy and side-effects profile. This project aims to develop and characterize the first, fully adjustable gene therapy, capable of predictable post-treatment dose adjustment. To accomplish this, a number of technological hurdles will be addressed as part of the project including non-viral delivery of genetic material to human cells, the ability to control the gene expression in a predictable and measurable manner, and the assurance that any adjustability is safe to the patient organs, tissues, and cells that neighbor the treatment area.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
REQYRD, INC.
SBIR Phase I: Separations of Critical Materials in Lithium-ion Battery Black Mass
Contact
11901 W 48TH AVE
Wheat Ridge, CO 80033--2166
NSF Award
2224840 – SBIR Phase I
Award amount to date
$275,000
Start / end date
03/15/2023 – 02/29/2024 (Estimated)
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to establish domestic recyclability (circularity) for critical energy materials that have majority or total import reliance. Such elements include including cobalt, lithium, manganese, carbon (graphite), and nickel. Establishing circularity of critical materials can decease dependence on foreign sources while increasing domestic manufacturing of lithium-ion batteries, magnets, catalysts, and superalloys. As the world electrifies light- and heavy-duty vehicles and renewable energy resources increase, the need for battery materials is expected to quadruple over the next decade. Hydrometallurgical processes are highly nuanced and well-established; The configurations are nearly endless, especially when multiple recycle streams are incorporated. Additionally, customers require tight specifications. As battery chemistries evolve, a process technology that can be shown to be adaptable to variable compositional profiles can help proliferate domestic production to anchor advanced industries.
This SBIR Phase I project proposes to fill knowledge gaps in separation chemistries using sulfites. Through exploitation of solubilities, effective and efficient separations of constituents in end-of-life lithium-ion batteries can reduce reagent consumption, decrease equipment sizing, and lead to the formation of battery-grade chemical feedstocks for demonstrated supply chain circularity. Key to the success of the project is the separation of manganese without resorting to expensive solvent-based techniques. The project will explore of the solubilities of Ni/Co/Mn/Li sulfites as a function of temperature and pH, characterize the form of the precipitates, and investigate the oxidation of these sulfite precipitates to a high purity sulfate. The objectives include determining missing data on empirical solubility from the general material property literature, demonstrating lab-scale selectivity and overall efficacy, and optimizing operational parameters for techno-economic and life-cycle modeling.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
RESET WATER LLC
STTR Phase I: Electrochemical Water Treatment Devices to Combat Harmful Algal Blooms
Contact
65 MAIN STREET
Potsdam, NY 13676--4039
NSF Award
2321315 – STTR Phase I
Award amount to date
$275,000
Start / end date
09/01/2023 – 08/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is in the development of a harmful algal bloom (HAB) mitigation technology. HABs are cyanobacterial plumes that are often acutely toxic to aquatic organisms, animals, and humans because of the cyanotoxins released by them. HABs have become an emerging threat to the recreational use of lakes and drinking water supplies. It is challenging for conventional centralized water treatment processes to mitigate HAB events and cyanotoxins that occur frequently and irregularly due to increased nutrient discharge and global climate change. The direct economic impact of HABs in the U.S. is estimated to be $50 million per year. The proposed HAB mitigation technology aims to intercept of eliminate such blooms at the early stage. The technology is based on electrochemical oxidation and features faster removal of both cyanobacteria and cyanotoxins. The solution should also result in fewer disinfection byproducts compared with conventional chlorination and ozonation methods. The technology uses cheaper, locally sourced electrode materials thereby reducing capital costs and potential supply chain challenges. These improvements could make the technology accessible to larger customer base and as such, improve water quality for larger populations. In addition, energy consumption may be reduced significantly allowing for more effective treatment. The reduction in HABs would ensure the continued use of water resources for recreation, reducing health risks and increasing property values of lakeside residences.
The project is focused on the development of an innovative electrochemical technology to remove HABs safely and effectively from bodies of water. Removal of harmful algal blooms is accomplished through an electro-oxidation (EO) process that does not require the addition of any chemicals, is quick, and easy to use. Phase I research and development will include the development of new electrode materials aimed at reducing the electrode costs by up to 50% by substituting the base metal and the dopant in the coated electrode material. The team will also evaluate the environmental impact of the treatment process on non-algae model organisms.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
REVISION AUTONOMY INC
SBIR Phase I: Scientific Discovery Translation of Snow-Covered Road Perception Software to a Lane Detection in Snow (LDIS) Product
Contact
4717 CAMPUS DR STE 100
Kalamazoo, MI 49008--5602
NSF Award
2304352 – SBIR Phase I
Award amount to date
$275,000
Start / end date
08/01/2023 – 07/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is improved automotive transportation safety, usability, and equity for the general public which reduces the annual 5,300 fatalities, 418,000 injuries, and billion-dollar losses from inclement weather crashes in the United States. The technology identifies the driving lane using camera data processing in challenging driving conditions such as congested intersections and bridges, dark tunnels, and during sun glare and active snowfall. Addressing these problems also enables U.S. technology competitiveness in the global automotive market, development of technologies relevant to national defense and energy efficiency applications, expansions of existing university courses, and entrepreneurial engagement from underrepresented communities. The foundation for the proposed research is the utilization of camera and global positioning data specifically for navigation in snow using real-time machine learning methods without an overreliance on deep learning. This technology can be implemented in current vehicles, enabling a widespread commercial impact and a strong means to grow a viable business that is generating tax revenue and offering technology jobs to the local community.
The strong technical innovation of this work is a hierarchical computer vision system built using a resilience engineering methodology, individually tuned classifications, camera and GPS fusion, and fast processing machine learning. This system provides verification of successful performance with respect to human-observed ground truth without an overreliance on deep learning so that it can be successfully validated by automotive companies using standard practices. This innovation allows current driving assistance products to remain functional when they are needed most: in low visibility, low traction situations. This research aims to verify the innovation in two-lane intersections, bridges, tunnels, under sun glare conditions, in 100+ miles of active snowfall, and in instances of misleading environmental information. Data for these instances will be collected and the existing technology will be modified and improved.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ROBIGO, INC.
SBIR Phase I: Engineering the Plant Microbiome to Reduce Disease in Crops
Contact
750 MAIN ST
Cambridge, MA 02139--3544
NSF Award
2232769 – SBIR Phase I
Award amount to date
$274,999
Start / end date
05/01/2023 – 04/30/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is the development of a platform technology that enables a novel mode of action for protecting crops from disease. Facing increasing disease pressure and a changing climate, growers around the world spend $80 billion on nearly six billion pounds of pesticides each year, and yet still experience yield losses of 20-40% due to pests and disease. Broad-acting, chemical pesticides - currently the industry standard - are losing both efficacy and public support as resistance to pesticides spreads and the negative environmental impacts become clear. There is a pressing need to fundamentally redesign crop treatments to create a more sustainable and efficient food system. Leveraging synthetic biology, CRISPR, and data science, this SBIR Phase I project addresses this need by developing a new class of microbial biopesticides that precisely target and kill crop pathogens without adversely affecting beneficial microbes, insect pollinators, or humans. With an initial focus on treating tomatoes (320,000 acres in the US, $32 million addressable market), this project sets the stage for providing solutions for major global markets like citrus ($600 million), olives ($1.8 billion), and rice ($2.7 billion).
The project provides targeted solutions for bacterial diseases in agriculture. Historically overlooked and underserved by the agricultural community, bacterial diseases have become increasingly devastating over the past 10 years due to a lack of effective treatment options, growing antimicrobial resistance, and climate change driving higher disease pressures. Building from a prototype system, this SBIR Phase I project aims to engineer improvements that will increase the efficacy and tractability of the microbial biopesticide in outdoor agricultural environments. This includes applying molecular biology techniques to increase microbial colonization within complex microflora to increase product efficacy, extend microbial persistence in plants to provide longer protection, and reduce the rate of resistance to extend product lifetimes. Furthermore, this project will develop a bioinformatics algorithm to better program the microbes to specifically target only the disease-causing pathogens. Finally, the team will demonstrate product efficacy in lab-grown tomato plants with the goal of surpassing the industry standard of 70% efficacy and will compare performance to two industry standard chemical pesticides. Successful completion of this project will result in a novel method to introduce protective traits to crops without genetically modifying the plant itself.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ROPLAGARIN LLC
SBIR Phase I: Narrative interface technology to support two-way human-computer interaction for the disabled community
Contact
401 RYLAND ST., STE 200-A
Reno, NV 89502--1643
NSF Award
2304553 – SBIR Phase I
Award amount to date
$222,489
Start / end date
08/15/2023 – 01/31/2024 (Estimated)
NSF Program Director
Errata
Please report errors in award information by writing to awardsearch@nsf.gov.
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve software accessibility for the roughly 61 million Americans living with a disability. Interactive technology is not currently standardized, and end-users must rely on accessibility features built into software which are inconsistent or nonexistent. The proposed accessibility interface will enable end-users to utilize 80% of commercially-available software, improving the standard of living for individuals with disabilities and allowing them to engage online professionally and socially. Simultaneously, the innovation will allow software distributors to provide low-cost accessibility solutions, fulfill required mandates, and increase their products’ use and revenue. The innovation will overcome previous limitations for state-of-the-art accessibility solutions as it will be able to accommodate high-intensity software such as augmented reality (AR). It will also be backward-compatible, adding functionality to older platforms and providing a robust and enduring competitive advantage. US-based medical technology companies, game developers, and organizations with accessibility mandates will be initially targeted.
This Small Business Innovation Research (SBIR) Phase I project seeks to provide interactive technology accessibility for blind, deaf, and physically disabled users without necessitating additional accessibility hardware. A machine learning (ML) engine will translate visual data into text blocks delivered via text or text-to-speech, with visual information recognized through embedded data. Natural language processing (NLP) will be used to bind actions to spoken phrases, allowing the end-user complete control of the integrated software. This project will test the interface’s ability to integrate with personal productivity software, web browsers, and a video game, with the goal of enabling users to attain 95% functionality across 80% of software on the market. The research and development will involve: i) training the ML engine to use embedded data to reinterpret visual data as text and produce dynamically-created sentences listing all interactive objects within the user’s field of influence, ii) developing the controller interface, which will be driven by user input, listen for approved phrases, and activate desired software controls, and iii) expanding the interface’s versatility by creating and implementing ML protocols to add accessibility features to any existing or future software. The result will be an early prototype with moderate-to-full functionality across selected software.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. -
ROSEBUD BIOSCIENCES INC.
SBIR Phase I: Drug discovery using stem cell derived organoids
Project Leader