Small Business Innovation Research (SBIR) Phase II: Decentralized Evacuation Intelligence with Generative AI, Personalized Preparedness and Robust Real-Time Navigation

Small Business Innovation Research (SBIR) Phase II: Decentralized Evacuation Intelligence with Generative AI, Personalized Preparedness and Robust Real-Time Navigation is sponsored by National Science Foundation (NSF). This SBIR Phase II project aims to enhance public safety and community resilience in wildfire-prone areas through a personalized evacuation technology that integrates planning and real-time response, leveraging AI and digital twin technology.

Awardees phase 2 details | NSF SBIR For proposal preparation and submission instructions, click here . The SBIR/STTR program looks forward to receiving the submission of new Project Pitches in response to the new solicitations beginning on Tuesday, June 2, 2026. Please direct any questions to sbir@nsf.

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Loading company results... SBIR Phase II: Gaze-independent contactless autorefractor for self-serve eye exam kiosk. Please report errors in award information by writing to awardsearch@nsf.

gov . The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve access to vision care and eyeglasses for all Americans through a self-serve, rapid vision exam kiosk for retail stores and public spaces. An estimated 175 million Americans suffer from blurry vision, of which 30 million live without eyeglasses.

By partnering with retailers, pharmacies, and supermarkets, the company has the potential to reach a large number of Americans via a network of kiosks spread across the US. With 70% of the population benefitting from eyeglasses, the development of the company? s rapid vision exam kiosk aims to democratize vision care and eliminate the gap in easily accessible vision exams.

This project develops a gaze-independent, contactless autorefractor technology (GIPR) for use in a self-serve and autonomous vision exam kiosk. Gaze-camera misalignment is a leading contributor to accuracy drift in autorefractors using the retinal reflex method. Eliminating the gaze alignment requirement marks a significant milestone in the company?

s development. The GIPR design refracts the inner visual field in a single capture, thus providing a measurement of refractive error at the subject? s foveal position.

Building on the success of the Phase I feasibility project, this Phase II project will continue the development of the GIPR module towards commercial readiness by optimizing hardware layout and improving data processing pipeline throughput. 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.

SBIR Phase II: Remote IoT Monitoring Network for Early Warning and Measurement of Structural Movements Barrington, NH 03825--5052 Please report errors in award information by writing to awardsearch@nsf. gov . The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be in deploying an easy-to-use roof monitoring system.

This affordable high-performance geolocated wireless monitoring system detects unsafe loads and movements on buildings, especially flat commercial rooftops found on schools, distribution centers, office buildings, malls, factories, arenas, apartments, condominiums, and warehouses.

This project will innovate scientific and technological advancements that realize the promise of the Internet of Things, generating not just useful products demanded by the marketplace but products that can save lives and fundamentally improve life cycle infrastructure management.

The affordability of our technology directly benefits disadvantaged communities, especially in rural areas where resources are severely limited, and serious infrastructure and substandard building problems tend to linger for years. In 2015, it was reported on a major national channel that more than 160 roofs collapsed or faced imminent collapse in Massachusetts alone due to snow load throughout 2014-2015.

This SBIR project will inform precisely where unsafe rooftop snow and water loads exist to ensure resources are allocated in advance and as required. The proposed project efficiently pinpoints risks in flat roof structures at an affordable cost. All 5.

9 million commercial buildings in the USA are vulnerable to the destructive forces of nature and negligence. Out of building failures that occur for known reasons, accumulated ice, snow, and/or liquid water account for 33% of incidents. Dilapidation caused by a lack of maintenance, which makes up 30.

7% of known-cause building failures, often develops slowly and is not apparent immediately. Complete or partial building collapses leave occupants at risk of mortal injury and the valuable property contained in the structure lost or damaged. Avoiding damage to large-scale infrastructure will save society significant resources and reduce lost productivity.

Even minor collapses impact business continuity, affecting revenues and often the larger community, in the case of grocery stores and similar institutions. The sensor technology improves greatly upon current structural health monitoring methods, limited to single-dimension measurements, higher system costs, and complex installations.

The project will develop sets of battery-powered wireless sensors and deploy them across several building rooftops. An intuitive online application will be developed to display building health data and provide users with alerts when loads exceed safety thresholds.

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. SBIR Phase II: 4D Flightpath-Based Autonomous Separation Assurance Systems (ASAS) Manhattan, KS 66502--6233 Please report errors in award information by writing to awardsearch@nsf. gov .

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project is enabling efficient, safe, and cost effective deconfliction of dense Uncrewed Aerial Vehicle (UAV) operations. Widespread usage of UAVs is expected to bring significant societal benefits. The UAVs that are anticipated to be utilized for package delivery are estimated to have lower carbon impact than their ground transport counterparts.

Delivery drones are currently used for life saving delivery of organs and medications. UAVs currently used for inspection provide safer and more economical alternatives to traditional inspection techniques. To safely get to the flight densities that capture the true societal and economic potential of UAVs, robust autonomous air traffic management solutions are needed.

The technology that will be commercialized in this project will fill this need for the emerging UAV market by providing reliable and fast aerial conflict detection and suggestion of conflict avoidance maneuvers.

This Small Business Innovation Research (SBIR) Phase II project will focus on improving the autonomous Air Traffic Management (ATM) technology developed in the SBIR Phase I project and extending the system to include a wider range of Uncrewed Aerial Vehicle (UAV) operations.

Improvements to the ATM technology will include improved fault tolerance, improved consideration of UAV capabilities when suggesting aerial conflict avoidance maneuvers, adding flight planning tools, and using optimization techniques to avoid compounding aerial conflicts.

Common open-source UAV mission descriptions, developing international standards for the sharing of UAV flight intent, and data sources providing updates of UAV positions will be infused into the ATM technology to detect and avoid aerial conflicts with a more diverse set of UAVs. The result of this SBIR Phase II project will be a safe, reliable, and feature rich software product for providing autonomous ATM services to UAV operators.

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. SBIR Phase II: A Physics-Based Competitive Machine Learning Framework for AI-Driven Robotics Instruction Saint Petersburg, FL 33704--3721 07/15/2025 – 06/30/2027 (Estimated) Please report errors in award information by writing to awardsearch@nsf. gov .

The broader and commercial impact of this Small Business Innovation Research (SBIR) Phase II project is to provide mission-critical drone simulations with embedded robotics and artificial intelligence (AI) to accelerate learning in high school physics courses.

These simulations visually demonstrate real-world applications of physics while teaching students to develop AI and machine learning (ML) models skills essential for robotics automation, critical skills for advancing the next-generation STEM workforce. This platform addresses the urgent need to improve AP Physics Exam outcomes and provide novel, high-quality resources to physics educators nationwide.

By blending core physics education with hands-on applications, the technology bridges science, technology, engineering, and mathematics (STEM) education gaps and offers scalable resources aligned with NGSS and AP Physics standards. Pilot studies demonstrated a 98% improvement in student understanding of kinematics.

By year three, the platform aims to increase AP Physics pass rates by 20% in participating schools and expand into five states, helping address the projected shortage of 186,000 engineers by 2031. This mission-critical drone simulation with embedded robotics framework helps prepare students to succeed in STEM education.

This Small Business Innovation Research (SBIR) Phase II project addresses the foundational physics and engineering skills necessary to prepare students for STEM careers in advanced manufacturing and automation.

The project leverages advancements in artificial intelligence (AI) and robotics to create an innovative bidirectional reinforcement learning curriculum to provide students with a dynamic, hands-on platform to master applied physics concepts such as force, motion, energy systems, and electromagnetic principles, while simultaneously advancing the autonomous capabilities of robotic systems.

The research objectives include completion of the adaptive, AI-assisted platform that enables students to translate physics concepts into Python programming through the integration of machine learning algorithms with drones and robotic systems employed as interactive tools for both teaching and learning. This approach reinforces basic physics principles in real-world mission-critical scenarios.

The research employs a dual-learning methodology as students refine their understanding of physics and programming while collaboratively improve robotic performance in simulated and physical environments. The anticipated technical results include a measurable improvement in student proficiency in physics and programming to provide scalable solutions and bridge gaps in STEM education and workforce readiness.

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. SBIR Phase II : A tool to automate a narrative patient summary of the medical chart for outpatient physicians 07/01/2025 – 06/30/2027 (Estimated) Please report errors in award information by writing to awardsearch@nsf. gov .

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to revolutionize healthcare delivery by leveraging natural language processing to provide concise, clinically relevant summaries of patients? medical records. By reducing the burden on physicians the tool could addresses pressing issues like medical errors, patient safety, and provider burnout.

Commercially, the proposed approach could provide the potential to streamline clinical workflows, improve revenue reimbursement, and reduce administrative burdens for healthcare providers. Its integration with national health information exchanges and leading electronic health record systems positions it as a pivotal tool for digital health companies and medical institutions, creating a scalable solution with broad market applicability.

The proposed project addresses the critical challenge of reducing the time burden associated with processing unstructured electronic health records while ensuring the accuracy and comprehensiveness of patient care. Physicians often lack adequate tools to quickly synthesize patient histories, which can lead to missed follow-ups, medical errors, and inefficiencies.

The project aims to develop and refine a machine-learning-enabled tool to generate clinically relevant, narrative summaries of medical records, enhancing decision-making and streamlining clinical workflows. The proposed research focuses on natural language processing techniques to analyze broad and unstructured medical data.

By integrating state-of-the-art models and a federated learning structure to address data-sharing constraints, the project aims to ensure adaptability across various healthcare environments. Anticipated technical results include high-fidelity summaries, robust integration with electronic health record systems, and real-time capabilities for physicians to access and query patient records.

The research scope includes fine-tuning large language models, implementing speech-to-text integrations, and developing retrieval-augmented generation systems for personalized physician queries. Methods involve annotating diverse datasets, employing advanced evaluation metrics, and rigorous testing with medical professionals.

This project is expected to produce a scalable, clinically validated tool to potentially enhance physician efficiency, reduce medical errors, and ultimately improve 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.

SBIR Phase II: Moisture Control Paint and Primer System for Bathroom Mold Prevention Providence, RI 02906--4256 09/15/2025 – 02/28/2027 (Estimated) Please report errors in award information by writing to awardsearch@nsf. gov .

This Small Business Innovation Research Phase II project aims to commercialize a novel paint and primer system that manages indoor moisture and suppresses mold growth, with an initial focus on residential and commercial bathrooms. Mold exposure in buildings is a significant health and economic issue, contributing to respiratory illnesses and extensive maintenance costs.

This innovation offers a passive, affordable, and easily retrofitted solution for improving indoor air quality and preventing costly moisture damage. By reducing the latent load on heating, ventilation, and air conditioning (HVAC) systems, the coatings can also enhance energy efficiency.

This project will increase the economic competitiveness of the United States by strengthening domestic manufacturing, creating a new high-value export category, and stimulating innovation in the built environment. The technology is designed to deliver broad-based benefits to Americans across all regions and housing types.

Commercially, this effort targets a significant opportunity within the $60 billion global decorative coatings market, with an estimated multi-billion-dollar addressable segment in moisture-prone spaces. Successful deployment could shift market expectations by embedding high-performance functionality into standard architectural coatings.

The intellectual merit of this project lies in its development of a multi-functional coating system that combines high-capacity moisture storage with directional vapor transport (mimicking a "vapor diode") to protect building assemblies from water accumulation. The innovation integrates hygroscopic and thermally responsive materials into a two-layer coating architecture? a primer and a topcoat?

that modulate moisture dynamics in response to environmental conditions. The research objectives include validating the system? s effectiveness in suppressing mold-supportive humidity levels, quantifying thermal and moisture buffering effects, and modeling the system?

s performance in various climates and installation scenarios. The project will involve field pilots in real-world buildings, controlled pre- and post-application testing, and the collection of longitudinal humidity and condensation data. These results will inform the refinement of physical models that predict mold risk behind walls and ceilings, providing deeper insights into hidden moisture dynamics.

Through this research, the project will advance understanding of passive moisture control in building materials and lay the foundation for a new class of functional architectural coatings. 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.

SBIR Phase II: The Development of an Affordable, Compact, and Silent Head-Only MRI Scanner Minneapolis, MN 55455--2009 06/15/2025 – 11/30/2026 (Estimated) Please report errors in award information by writing to awardsearch@nsf. gov . This Small Business Innovation Research Phase II project focuses on the commercialization of a novel magnetic resonance imaging (MRI) platform.

Leveraging an innovative system architecture, the new platform provides silent, high-quality magnetic resonance brain imaging while operating at just one-tenth the cost and footprint of traditional MRI systems. This dramatic reduction in size and cost will make advanced imaging technology accessible to communities across the United States, particularly in rural areas where traditional MRI installations are not feasible.

The platform achieves clinical equivalency to modern scanners while requiring minimal infrastructure, representing a breakthrough solution for widespread medical imaging access. The broad deployment of this affordable and effective MRI technology will accelerate the diagnosis of strokes, dementia, and head trauma, potentially saving thousands of lives and improving patient outcomes in communities throughout the nation.

The intellectual merit of this project centers around B1 encoding, a novel methodology in designing MRI systems. Unlike traditional MRI architecture that relies on large, expensive, and loud electromagnets known as B0 gradient coils, this approach enables their partial or complete elimination.

By removing these components, the system saves hundreds of pounds in weight and hundreds of thousands of dollars in cost, while enabling greater portability. At its core, B1 encoding represents a fundamental reimagining of MRI system design that promises to maintain clinical value while drastically reducing system complexity and cost.

The neuro-imaging MRI system powered by this technology will be the first commercial system to demonstrate high-quality imaging without the use of B0 gradient coils. 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 II: Novel Spectroscopy for the Early Detection of Crop Afflictions 09/15/2025 – 08/31/2027 (Estimated) Please report errors in award information by writing to awardsearch@nsf. gov .

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project is focused on the continued development of the first high-throughput assessment tool of crop health based on hyperspectral imagery that is suitable for vehicle-mounted field deployment. This technology will support crop growers in making data-driven decisions for efficient water and fertilizer management and in the control of crop diseases.

By enabling early and accurate diagnosis of crop stress? crucial for the timely and targeted use of amendments, irrigation, and crop protection? this technology supports farmers in making data driven decisions, reducing crop losses from disease and other afflictions.

As a result, farmers benefit from improved yields and lower input costs, including reduced use of fungicides and fertilizers. For consumers, the proposed technology can lead to increased availability of healthier produce by reducing the use of fungicides and improving the economic viability of small farms.

The intellectual merit of this project centers around a dual-detector system that overcomes the tradeoff between spectral versus spatial resolution currently faced by existing optical scanning technology by sensing a single spectrum representative of the average signal across an entire image.

This system has been adapted into an embeddable, portable spectrometer that combines fast, calibrated, non-contact data and control systems with artificial intelligence models to enable instantaneous in-field diagnosis.

The proposed Phase II work will integrate this portable device and the associated detection algorithms with a mountable rugged hyperspectral camera for motion-based analysis and a reporting dashboard into a complete commercial solution. This will be accomplished through the expansion and refinement of the portable system hardware and the development of a vehicle-mounted hyperspectral camera system.

Additionally, to enable deployment in the viticulture sector, a comprehensive data collection and modeling framework designed to address the complexities of multi-variety viticulture disease detection 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.

SBIR Phase II: Multi Sub-System Miniaturization and Development for Semi-Truck Fuel Savings Device San Diego, CA 92126--4480 Please report errors in award information by writing to awardsearch@nsf. gov .

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is reducing fuel consumption, improving safety and stability, and reducing the carbon footprint of the trucking industry while increasing profitability. Over 70% of US freight tonnage is moved by trucks. At highway speeds, aerodynamic drag uses over 65% of the total vehicle energy.

The proposed device modifies the aerodynamic behavior of semi-trucks using air injection by allowing continuous optimization of aerodynamic performance. This project will bring the pneumatic, sensor and artificial intelligence (AI) control systems from proof-of-concept to commercialization. Having a commercial product capable of determining and delivering the trailer?

s best aerodynamic profile based on real-time operating conditions may be a game-changer for the trucking industry, as fuel is a significant operating cost. Commercializing this system has the potential to create an energy savings for all US fleets, saving more than 3 billion gallons of diesel fuel, reducing the release of more than 33.

5 million tons of carbon dioxide into the atmosphere, tripling trucking company profits, and saving an annual $22 billion. This SBIR Phase II project proposes development of an aerodynamic add-on prototype for semi-trucks to save fuel by dynamically changing the trailer? s aerodynamic profile to accommodate diverse operating conditions.

Objectives of this SBIR Project are to evolve the device from prototype to the first commercially viable release through system miniaturization and encapsulation, controller optimization, and improved overall system performance, reliability, and safety. Research conducted to miniaturize the overall system footprint will minimize any additional operational impacts, ensuring widespread adoption and utilization that maximizes fuel savings.

Research to optimize the Artificial Intelligence-Controller operation will maximize fuel savings because it will allow the device to operate under a broader set of operational conditions. Further development to improve system performance, reliability, and the addition of a safety assist will improve the profit margins of the trucking industry while simultaneously improving on-road safety for the public.

The project seeks to deliver 10% savings in operational costs for the trucking industry while improving the efficiency and safety of their country-wide operations. 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.

SBIR Phase II: Preventative Maintenance AI Chip: Software-Configurable, Tiny, Low-Latency, Always-On, Ultra-Low-Power, Near-Sensor-AI, No Cloud Required 07/15/2025 – 06/30/2027 (Estimated) Please report errors in award information by writing to awardsearch@nsf. gov .

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project lies in advancing distributed artificial intelligence (AI) for predictive maintenance of instruments/vehicles used in manufacturing, aerospace, agriculture, and transportation. Instead of transmitting raw sensor data via an always-on, power-hungry communications link to the cloud - which can introduce cybersecurity risks ?

distributed AI enables secure, fast, and ultra-low-power monitoring that operates efficiently on battery power. This capability allows real-time asset (e.g., a drone) monitoring without requiring continuous cloud connectivity. Predictive maintenance prevents unexpected gear and equipment failures, minimizes downtime, and reduces maintenance costs across industries, where drones, robots, and other autonomous systems rely on ?

smart? monitoring. Traditional cloud-based AI solutions are often impractical for remote or battery-powered assets due to high energy consumption, constant connectivity needs, and security vulnerabilities.

This project provides a compact, low-power alternative by embedding AI and allowing for continuous monitoring without delay. Additional applications include industrial machinery failure detection, environmental monitoring, and public safety improvements through infrastructure resilience.

This project focuses on developing an AI-enabled integrated circuit (IC) for real-time processing of wave-based sensor signals, such as vibrations and sounds, to detect anomalies indicative of potential failures of assets (e.g., motors in drones and robots) before they occur. This IC integrates an ultra-low-power analog front-end with a digital AI engine optimized for real-time wave-pattern recognition.

Designed for efficiency, the IC operates at power levels in the tens of microamperes, making it practical for battery-powered systems. The tiny form factor, measuring a few millimeters per side, allows ease of integration near or into sensor capsules. The system achieves over 92% detection precision with latency measured in a few milliseconds, as per simulations, enabling near-instantaneous failure prediction.

Research objectives include finalizing the IC design, fabricating prototypes, refining machine learning algorithms, and conducting field validation. The chip optimizes analog and digital signal processing IC design with AI software (specifically for sound and vibration signals) to ensure low-power, small-size, scalability, robustness, and a cost-effective solution.

By enabling real-time, always-on AI-driven analytics, this innovation aims to eliminate reliance on cloud processing, offering a tiny, always-on, near-zero-latency, energy-efficient, scalable, and secure predictive intelligence solution across multiple 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. SBIR Phase II: A Hardware-Aware AutoML Platform for Resource-Constrained Devices College Station, TX 77845--7466 Please report errors in award information by writing to awardsearch@nsf. gov .

The broader impact of this Small Business Innovation Research (SBIR) Phase II project will support industries keen on harnessing the power of artificial intelligence and Internet of Things. By simplifying the deployment of artificial intelligence technologies, this project allows a broader range of businesses in the manufacturing sector to join the data revolution.

It empowers businesses to leverage their existing data assets, leading to enhanced efficiency, fostering a culture of innovation, and carving out a competitive edge in the market. On the commercial front, the benefits are multifold, ranging from bolstered business efficiency and substantial cost reductions to potential market expansion for solutions in artificial intelligence of things.

From a societal perspective, this technology contributes substantially to the development of a data-literate 21st-century workforce and strengthens human-technology synergies.

The project will drive efficiency and standardization across diverse industries and streamline the process of analyzing and acting upon extensive data sets which will result in improved product quality, fuels innovation, and pave the way for more efficient decision-making processes in an increasingly data-driven world.

This Small Business Innovation Research Phase II project addresses the complex challenge of efficiently deploying artificial intelligence models on edge devices for real-time defect detection in industrial manufacturing systems. The problem lies in creating a scalable, efficient, and easy-to-use solution that allows for the wide application of artificial intelligence technologies in Internet of Things devices.

The research objectives include developing a modular end-to-end defect detection system, implementing advanced machine learning automation techniques, improving model interpretability, and enhancing model compression for edge devices. The research will leverage machine learning, edge computing, and user feedback to create a practical, robust, and user-friendly solution.

The anticipated results include an artificial intelligence of things system that effectively performs real-time defect detection with improved interpretability and reduced resource usage.

The expected outcomes comprise an artificial intelligence of things system capable of performing real-time defect detection with elevated interpretability, using minimal computational resources, and revolutionizing defect detection in industrial manufacturing 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. STTR Phase II: Development of a Smart Remote Health Management System for Patients with Kidney Disease Pikesville, MD 21208--1839 Please report errors in award information by writing to awardsearch@nsf. gov .

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is to develop a novel technology for the early detection of complications and effective management of kidney disease at home.

More than 37 million patients in the US have chronic kidney disease, which is associated with increased mortality and morbidity, including a greater risk of cardiovascular disease, hospitalization, premature death, and progression to end-stage kidney disease.

According to the Centers for Disease Control and Prevention, in 2018, treating Medicare beneficiaries with chronic kidney disease cost over $81 billion, and approximately 20% of the Medicare budget was spent on kidney disease. This project leverages a medical device and digital tools with advanced analytics capabilities to provide actionable data and key health insights for timely interventions for at-risk patients.

There is a rapid shift towards value-based healthcare from the traditional fee-for-service model accelerated by the significant cost burden and poor outcomes, especially in kidney care management.

This solution is designed to facilitate this transition seamlessly, lowering unnecessary emergency room visits and/or hospitalizations, and minimizing exacerbations and associated healthcare costs in a disease condition that contributes over $100 billion in healthcare expenses.

This STTR Phase II project develops a comprehensive disease management solution for chronic kidney disease by offering a personalized clinical-decision support system for providers as well as a companion diagnostic solution for patients to improve therapeutic benefits.

There is a dearth of reliable and accurate predictive tools that can non-invasively measure critical biophysical and biochemical markers to inform clinical care and empower self-identification of undesirable effects.

The objective of this research is to develop and validate disruptive technologies for the non-invasive assessment of biomarkers critical to chronic kidney disease diagnosis and management such as hemoglobin levels, electrocardiography (ECG)-based arrhythmia, estimated potassium levels, and lung functions, augmenting the existing capability that measure 10+ vital signs.

The innovation also includes a novel concept for any medical device, to record and broadcast personalized messages in a trusted voice (physician/family member), to improve treatment adherence and outcomes. The project further incorporates a proprietary cloud-based analytics system that leverages both subjective and objective data to generate a unique ? digital fingerprint?

for effective management and triage. Consistent use of the proposed solution will improve symptoms management, care delivery, and diagnosis, and ultimately lead to dramatic reductions in hospitalizations, total medical expenditure related to morbidity and lost productivity, and patient anxiety.

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. SBIR Phase II: Edible, water soluble corn zein films for shelf life extension and improved safety of perishable foods 07/15/2022 – 07/31/2027 (Estimated) Please report errors in award information by writing to awardsearch@nsf. gov .

The broader impact of this Small Business Innovation Research (SBIR) Phase II project is in the reduction of food waste and improved nutrition. Each year, more than 30% of fresh produce is wasted. The value of this wasted produce is estimated at more than $60 billion globally.

This project? s main goal is to extend the shelf life of perishable foods with an edible coating that regulates the rate of both transpiration to slow moisture loss and respiration to delay ripening. The project aims to enable produce growers and distributors to preserve the freshness and quality of fruits and vegetables.

Consumers could benefit by having access to fresher, tastier, and more appealing produce. This technology may also promote healthier food choices, especially in currently underserved food deserts. Packers would benefit by being able to offer a high-quality product that will last longer and that can better withstand the rigors of various types of transport.

Extended shelf life also extends the market reach of U.S. exports in global markets. The core innovation underlying this project is the creation of stable Zein colloids that employ only edible, plant-based ingredients, are non-flammable, non-corrosive, and meet most global food regulations. These colloids will be initially developed as coatings for fresh whole or minimally processed foods, root crops, vegetables, nuts, and seeds.

The coating will not impede the delivery of nutrients, flavors, and other functional ingredients that make foods appealing, improve their quality, and encourage consumption. The technology will also reduce food spoilage and improving food safety.

Experimental work will be focused on further improving the stability of Zein dispersions and demonstrating the ability to adjust the film properties (water vapor, oxygen, and carbon dioxide permeances) to respond to the physiology of a broad range of crops. This technology will also be piloted at various packing plants to evaluate its operational fitness with existing operations.

This research will also enable related applications, such as for compostable coatings on food packaging and pharmaceutical and nutraceutical coatings. 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.

AKTTYVA THERAPEUTICS, INC. SBIR Phase II: AI-assisted identification of small molecules for targeted repair of vascular barrier dysfunctions Watertown, MA 02472--1239 08/15/2024 – 07/31/2026 (Estimated) Please report errors in award information