SBIR Phase I: Plient Engineered Recycled Steel Fibers as Cheaper, Faster, Safer Concrete Reinforcement

SBIR Phase I: Plient Engineered Recycled Steel Fibers as Cheaper, Faster, Safer Concrete Reinforcement is sponsored by National Science Foundation (NSF). This Small Business Innovation Research (SBIR) Phase I project focuses on eliminating technical barriers inhibiting commercialization of engineered recycled steel fibers for concrete reinforcement.

Awardees phase 1 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 I: Feasibility of a Novel Minimally Invasive Labor Augmentation System for Stage I Labor Los Angeles, CA 90068--1222 Please report errors in award information by writing to awardsearch@nsf. gov .

The broader/commercial impact of this Small Business Innovation Research Phase I project is the first medical device intervention for augmenting Stage I active labor. In the US, 3. 4 million low-risk childbirths occur in hospitals each year representing the leading cause for admissions, in-hospital bed use, resulting in $50B direct costs.

Despite optimal pharmacological administration, one in five (20%) experience significantly prolonged Stage I active labor durations due to ineffective uterine contractions. This results in increased maternal morbidity, health risks to the mother and infant, and is the leading cause of cesarean sections for low-risk delivery in the US. Rates of prolonged labor continue to increase with obesity, sedentary lifestyle and age of mother.

This initiative aims to develop the first medical device therapy for use by Obstetricians to directly increase the labor forces while relieving uterine muscle demand during labor to make childbirth safer. This SBIR Phase I project aims to develop a minimally invasive intrauterine labor augmentation system (ILAS) providing direct fluidic mechanical pressure modulation.

The trans birth canal system utilizes contemporary Class II medical device catheter-balloon (sac) technology to contain saline which fluidically controls intrauterine pressure synchronized to every labor contraction. In this SBIR Phase I project, the highest risk intrauterine assembly will be developed and mechanically validated through three primary objectives.

First, the sac will be further developed for safe prolonged intrauterine use under simulated conditions by refining the biomaterial? s mechanical properties. This will ensure smooth deployment including low coefficient of friction, ease of insertion and withdraw from the uterus, with a suitable external diameter and mechanical handling characteristics.

Second, the sac configuration and pleating pattern will be finalized for ensuring a low-profile insertion while maintaining durability and reliability. The system will then be validated through rigorous testing using a mock anatomical test system intrauterine cavity and infant. The results will verify the intrauterine catheter-sac?

s ability to deploy properly under simulated use conditions, distribute fluidic pressure evenly to current clinical targets of 50-70mmHg, exhibit durability with a sufficient safety factor. The outcomes are a mechanically validated system using biomaterials suitable for preclinical translation during the next stage of 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. SBIR Phase I: Novel Mechanism for Refreshable Braille Device with Embedded Curriculum 06/01/2025 – 11/30/2026 (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 I project will contribute to the field of refreshable braille technology (RBT) and precision manufacturing. The project addresses the high cost of existing RBT, which limits braille literacy among blind and low-vision individuals, impeding participation in education, employment, and leisure opportunities.

The innovation will enhance scientific and technological understanding by addressing durability, portability, and cost concerns in current RBT. Validating the novel braille system is key to de-risk the technology to enable commercial success. Seven million Americans have blindness or severe vision loss, including the target market of blind adults.

With the digital braille displays market projected to grow at a 20. 5% CAGR value from 2022-2027, there is considerable market opportunity. The commercialization plan involves selling the device to users, agencies, schools and government organizations, as well as selling individual braille cells.

The technology provides a competitive advantage by being low cost and having user-replaceable braille cells. By year three of the device launch, 5,000 individuals are expected to be utilizing the device where the product will enhance braille literacy and digital productivity.

This Small Business Innovation Research (SBIR) Phase I project addresses the challenge of creating a cost-effective, reliable, and user-repairable refreshable braille device. Currently, refreshable braille devices are cost-prohibitive to acquire and challenging to repair, leaving users without dynamic interaction with the digital world.

The project will implement a precision milled mechanically based system for actuating braille pins utilizing pins at braille code specification. Research objectives include refining of the pin mechanism, adjusting the tolerances and geometry of the scaled-down mechanical system, implementing appropriately sized motors, conducting preliminary cycle testing, and integrating the cells into a 20-cell device.

The primary challenges associated with this development will be prototyping within the tight tolerances without binds or jams at an affordable price point that meaningfully reduces barriers to entry to owning a refreshable braille device.

Anticipated technical results are a cell of braille operable at braille code specifications, refreshing in less than 500 ms, durable at 500,000 cycles, sized within a braille-code sized bounding box for single-cell modularity, and manufacturable at a cost of less than $20 per cell.

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 I: Image-Guided Controlled Release Platform for Intratumoral Immunoadjuvant Delivery Brookline, MA 02445--7753 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 I project is in developing an image-guided intratumoral therapy for treating immunotherapy-resistant solid tumors, a $125 billion market. The only FDA-approved intratumoral therapy costs $65,000? 100,000 per patient and is insurance-reimbursed, demonstrating market viability.

Pharmaceutical companies developing immune-oncology drugs are actively seeking strategic partnerships to improve drug delivery, as intravenous and oral formulations face toxicity and efficacy challenges. Intratumoral delivery is an attractive alternative, but current approaches suffer from rapid drug leakage (>70% lost within hours) and require frequent, impractical repeat dosing.

Beyond its clinical benefits, this project has significant commercial potential, offering pharmaceutical companies an innovative drug delivery platform that could expand their oncology pipeline, improve therapeutic efficacy, and increase drug life cycle. The societal impact includes enhancing treatment options for patients with limited alternatives, reducing systemic toxicity, and potentially improving long-term survival rates.

Early discussions with pharmaceutical executives and clinical trial physicians highlight stage IV colorectal cancer with liver or lung metastases as a high-priority clinical need. Additional interest exists in pancreatic, lung, and triple-negative breast cancers, expanding the potential market. This project aims to make intratumoral immunotherapy a viable alternative for cancer patients.

This Small Business Innovation Research (SBIR) Phase I project aims to improve current biologic therapies for solid tumors as they face poor on-target delivery, rapid systemic diffusion, and dose-limiting toxicities due to uncontrolled off-target effects. Systemic administration further exacerbates toxicity and efficacy limitations, restricting broader clinical adoption.

This project develops an image-guided intratumoral delivery system that solidifies upon injection, ensuring localized retention and sustained therapeutic release. Unlike freely administered biologics, this approach prevents rapid leakage, aligns with clinical dosing schedules, and minimizes systemic toxicity.

Our research focuses on biomaterial strategies to enhance stability, localization, and controlled release of biologics within tumors. While we have successfully developed a hydrophobic small molecule delivery system, biologic-based therapies require innovative hybrid formulations that protect, localize, and sustain release.

The anticipated outcomes include improved intratumoral retention, reduced toxicity, and enhanced therapeutic efficacy, ensuring biologics remain active at the tumor site for extended durations. This novel approach optimizes tumor targeting, improves safety, and enables more effective localized immunotherapies, addressing a critical need in oncology.

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 I: Integrating MentorAI into a student success platform Beach Haven, NJ 08008--5625 01/01/2025 – 12/31/2026 (Estimated) Please report errors in award information by writing to awardsearch@nsf. gov .

The broader/commercial impact of this SBIR Phase I project addresses the critical need for scalable, personalized student support in higher education. The project will develop an artificial intelligence (AI)-assisted mentoring platform that enhances peer mentoring programs through data-informed, evidence-based guidance.

This innovation comes at a crucial time, as student distress rates have doubled over the past decade, and institutions struggle to meet growing demands for mental health and academic support. The technology will particularly benefit underrepresented students, who often face barriers accessing traditional support services.

By combining AI capabilities with human peer mentors, this innovation will make technical advances in how to leverage AI tools within the context of human interactions. This will enable institutions to affordably scale high-quality, site-specific support services that improve student retention and success, advancing the health and wellbeing, academic achievement, and economic prosperity of marginalized students.

The commercial potential is significant, with the mentoring software market projected to reach $1. 3 billion by 2027. The platform's unique integration of data-driven insights with affordably scaled peer mentoring creates a competitive advantage in this growing market.

The business model focuses initially on higher education institutions, with potential expansion into nonprofit, government, and professional development sectors. This product enhancement will offer unique features that address growing demands for personalized, evidence-based support.

This Small Business Innovation Research (SBIR) Phase I project will develop and validate an innovative integration of large language models with retrieval-augmented generation technology to enhance peer mentoring effectiveness. The research addresses technical challenges in secure data integration, model fine-tuning, and scalable system architecture.

The project will implement advanced encryption methods and differential privacy techniques to protect sensitive student information while enabling real-time, personalized support. The system architecture employs a modular, multi-tenant design that allows customization for specific institutional contexts while maintaining response times below 500 milliseconds.

The research methodology includes developing secure protocols for data integration, implementing bias detection algorithms, and creating a comprehensive ethical framework for a "trustworthy knowledge-in-the-loop" approach using Retrieval-Augmented Generation technology to ensure accurate and evidence-based responses. Technical objectives include achieving 90% accuracy in contextually relevant responses and 85% user satisfaction ratings.

The anticipated results include a fully operational prototype demonstrating secure integration of multiple data sources, personalized recommendation generation, and scalable performance under peak usage conditions 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 I: Revolutionizing Enterprise Processes with Automated Logical Reasoning Across Unstructured Data 149 NEW MONTGOMERY ST FL 4 San Francisco, CA 94105--3740 Please report errors in award information by writing to awardsearch@nsf. gov .

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to transform enterprise knowledge work by improving how businesses analyze and act on unstructured data. Many industries such as technology, finance, and healthcare, rely on knowledge workers to extract insights from vast amounts of unstructured information, including documents, emails, and reports.

However, existing AI solutions often produce unreliable or inconsistent results, limiting their effectiveness in high-stakes environments. This project introduces a hybrid approach that combines structured algorithmic workflows with advanced AI models, ensuring complex multi-step tasks are completed with high accuracy, transparency, and explainability.

Initially targeting enterprise sales, where data-driven insights fuel revenue growth, this innovation will assist in researching customers, uncovering new opportunities, and streamlining deal-making processes. The technology provides a durable competitive advantage by providing enterprise level automations with high reliability and transparency, allowing businesses to trust and integrate AI in their processes.

As a key enabler of commercial success, it positions the company as a leader in enterprise AI. By year three, this technology is projected to impact over one million knowledge workers and drive measurable gains in productivity and revenue generation.

This Small Business Innovation Research (SBIR) Phase I project aims to develop a novel AI framework that integrates algorithm design (logical reasoning) with machine learning models to enhance the analysis of unstructured data.

Unlike conventional AI approaches that rely solely on large language models (LLMs), this framework structures AI workflows as step-by-step processes, selectively incorporating models like LLMs where appropriate to ensure transparency, consistency, and accuracy. The research will investigate key questions, including: Can a structured, logic driven AI system outperform end-to-end LLM-based methods in precision and recall?

How can AI workflows be designed to enhance user trust and explainability in high-stakes decision-making? What interaction models best support knowledge workers in integrating AI-driven insights into their workflows? The system applies domain-specific logic to critical enterprise tasks such as identifying customer pain points and drafting contracts, ensuring more coherent and traceable AI-driven decision-making.

The project will evaluate the framework? s effectiveness by measuring its performance against state-of-the-art AI systems in real-world business tasks. By demonstrating improvements in reliability, usability, and user adoption, this research will lay the foundation for scalable AI-driven automation in knowledge-intensive 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. AERIS WATER TECHNOLOGIES, LLC SBIR Phase I: Water from Air: An Adsorption-Based Atmospheric Water Harvester 1180 W PEACHTREE ST NW STE 1910 Please report errors in award information by writing to awardsearch@nsf. gov .

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project lies in advancing atmospheric water extraction (AWE) materials and devices for potable water production and humidity management. Access to clean water is dwindling due to climate-induced droughts and growing populations.

Currently, about 800 million people lack access to safe water, highlighting the need for complementary technologies like AWE alongside desalination. Addressing water scarcity requires a broad range of solutions, and AWE offers promising potential.

AWE technologies also apply to humidity control and heating, ventilation, and air conditioning (HVAC) energy reduction, crucial for rising cooling demands driven by growing populations and increasing temperatures. These technologies can efficiently extract water by more than 80-90% compared to traditional air conditioning.

This dual-purpose functionality could make AWE a game-changer for domestic, commercial, and industrial systems, drastically lowering energy input for humidity management. By developing innovative materials and devices, this project aims to alleviate water stress and significantly cut energy consumption in HVAC and humidity control applications.

Its impact extends beyond water production, addressing critical global challenges like sustainable cooling and energy efficiency while contributing to water and energy security.

This SBIR Phase I project aims to develop a functional atmospheric water extraction (AWE) device by addressing four key objectives: device modeling, adsorbent optimization, alternative adsorbent formulations, and testing various form factors for the adsorbent block.

The project will create a heat transfer model using standard heat pipe calculators to optimize radiator fin dimensions, spacing, and heating power, ensuring efficient desorption without requiring a vacuum. The target operating temperature for desorption is 60-100°C. The proposed device is designed to produce or remove at least 1?

1. 5 gallons of water within 12 hours under ambient conditions, with higher water yields in environments with greater humidity. This capability is made possible by an advanced AWE adsorbent, which exhibits superior water capacity across the full range of ambient humidities.

This innovation is crucial as existing adsorptive water harvesting systems fail to deliver cost-effective, energy-efficient water production across the wide humidity spectrum of 10? 80% relative humidity.

By optimizing materials and device configurations, this project will lay the groundwork for a commercially viable AWE system that addresses global water scarcity challenges while offering significant energy efficiency improvements compared to existing 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. SBIR Phase I: Neural Haptics for Next-Generation Wearables Please report errors in award information by writing to awardsearch@nsf. gov .

SBIR Phase I: AI Powered Invisible Fence to Foster Human-Wildlife Harmony Ellicott City, MD 21043--5501 10/01/2025 – 09/30/2026 (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 I project is the development of a humane, AI (artificial intelligence)-powered wildlife deterrent system that helps farmers, gardeners, and land managers prevent crops and landscape damage without relying on fences, chemicals, or lethal methods. Wildlife-induced losses billions annually for mid-sized farms?

create a significant economic burden and discourage entry by new and small-scale growers. This innovation offers an affordable, scalable alternative using computer vision and behavior-informed, non-lethal acoustic deterrence. It promotes biodiversity, reduces chemical runoff, and improves land access.

By integrating open-source tools, behavior modeling, and real-time sensing, the system fosters public engagement, supports AI literacy, and enables interdisciplinary learning. This technology has global potential to advance food security, climate resilience, and ecosystem stewardship in both developed and resource-limited regions.

This Small Business Innovation Research (SBIR) Phase I project addresses the growing challenge of wildlife-related crop loss and landscape damage by developing a non-invasive, AI (artificial intelligence)-powered deterrence system. Traditional solutions like fencing and chemical sprays are costly, ineffective at scale, and often harmful to the environment.

This project aims to create an edge-based, modular system that detects wildlife using computer vision, localizes the animal, and deploys species-specific acoustic deterrents through directional sound waves.

The research objectives include: (1) developing lightweight, real-time object detection models optimized for embedded hardware; (2) designing adaptive acoustic payloads tailored to animal behavior; and (3) analyzing long-term behavioral data to understand habituation patterns and refine deterrence logic.

The system will integrate visual and acoustic components through a low-power, solar-compatible platform and incorporate a cloud-connected repository for feedback, model updates, and collaborative learning. Anticipated technical outcomes include a robust field-ready prototype, behavior-aware deterrence algorithms, and a scalable architecture for real-world deployment.

By merging AI, ecological research, and embedded sensing, the project lays the foundation for a sustainable, responsive solution to human-wildlife conflict. Innovation advances state-of-the-art in species-specific deterrence and enables dynamic coexistence strategies across agricultural, residential, and conservation settings.

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 I: Patient-Specific System for Early Detection and Identification of Epileptic Seizures Hollywood, FL 33026--4941 10/01/2023 – 09/30/2026 (Estimated) 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 I project is to provide epileptic patients, and their caregivers a smart system that can predict seizures before they occur. There are more than 3 million adults and 1 million children in the US, and more than 50 million people worldwide, suffering from epilepsy.

Repeated and unpredictable seizures significantly affect the quality of life of people suffering from epilepsy. These seizures remain the leading cause of economic, emotional, and physical injuries for people with epilepsy and their caregivers.

Design, development, and integration of artificial intelligence (AI) models with instruments that detect abnormalities in brain waves like electroencephalogram (EEG) for real-time seizure prediction may bring improvements for these patients and their caregivers. This technology is poised to capture a portion of the rapidly growing $6 billion US market of AI healthcare solutions.

This Small Business Technology Transfer (STTR) Phase I project supports the development of a novel consumer product that works with caregivers to proactively mitigate the risk of seizure events in people with epilepsy. Current commercial solutions are mostly reactive, and support is available only after a seizure event.

The company will fill this gap by developing, testing, integrating, and evaluating machine learning (ML) models - applied to EEG data - for epileptic seizure prediction. The scientific approach will leverage inherently heterogenous and complex edge technologies.

Data connectivity with third party vendor EEG caps, microcontrollers, smart phones, and cloud services rely on many different operational technologies and communication standards. This research will overcome these challenges with hardware and software solutions that will integrate these services within an edge device to enable application portability and simplify deployment.

Challenges such as inference on limited computational power and energy devices, and its effects on the accuracy/sensitivity of the predictions will be solved using robust cross-validation techniques, extensive testing, and benchmarking using community standards. The technical product of this research will advance caregiver knowledge and increase understanding of epileptic seizures as well as increase patient well-being.

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 I: Aluminum Oxide Coatings as Fluorine-Free Hydrophobic Barriers for Paper 29754 WILLOW CREEK RD APT 235 10/01/2025 – 09/30/2026 (Estimated) Please report errors in award information by writing to awardsearch@nsf. gov .

The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project focuses on developing an alternative coating to per- and poly-fluoroalkyl substances. These chemicals feature strong carbon-fluorine bonds that are extremely persistent in the environment and have recently been shown to be hazardous to human health.

These per- and polyfluoroalkyl compounds are widely used in industrial and personal use applications, including as water-proof coatings for paper, clothing, and packaging materials, as surfactants, and as flame-retardant and stain-repellent coatings, among many other applications.

This project will provide a replacement coating that is inexpensive, non-hazardous, and can be applied to a wide range of surfaces, such as for textiles or paper products, which are the project? s initial market targets. The technological innovation is based on a fluorine-free, earth-abundant mineral coating, using a class of material that is novel for these types of applications.

This project will lead to barrier coatings that are durable, highly water and oil repellent, and resistant to scratching and corrosion. The final product will serve as a drop-in replacement substitute for these coatings. Phase 1 will focus on providing necessary data on the durability and industrial feasibility of the technology.

This Small Business Technology Transfer (STTR) Phase I project focuses on generating a viable chemical coating that can replace current fluorinated coatings. These fluorinated chemicals are used on a wide array of surfaces as anti-corrosion, anti-oxidation, waterproof, or other types of barrier coatings. This project?

s proprietary mineral coatings are fluorine-free and prepared from earth-abundant, benign materials. This approach enables coating at ambient temperatures and pressures using a process that is not precedented for use on ? soft?

substrates like cotton or paper. This research will start by depositing films using solution processible techniques like spray or dip coating. These films will be investigated for their hydrophobicity by static goniometry and for their homogeneity and chemical compositions using surface analytical techniques such as scanning electron microscopy and x-ray photoelectron spectroscopy.

These films will be investigated for their durability when exposed to environmental factors such as friction or washing, and the precursor? s compatibility with common additives found in competitive coatings will also be studied.

It is expected that this project will result in a coating chemical and procedure that can generate functional, durable barrier films on any target substrate as a drop-in replacement for current per- and poly-fluoroalkyl containing products. 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.

ALTERNATIVE ENERGY MATERIALS, LLC SBIR Phase I: Dry Powder Pressing Additive Manufacturing (DPP-AM) Please report errors in award information by writing to awardsearch@nsf. gov . The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project will be the development of a new additive manufacturing technique for ceramic materials.

Technical ceramics provide unmatched performance in harsh environment applications found throughout the energy, defense, healthcare, and IT sectors. Applications requiring miniaturization or process intensification would benefit from a novel additive ceramic manufacturing that can form internal microfeatures and combine different materials into functional layers for chemical reactions, imaging, or energy transfer.

This proposal will advance from proof-of-concept to a functional prototype of a dry powder pressing additive manufacturing printer. This work will improve our understanding of the fluidization and aerosolization of ultrafine and dense nanopwders that are prone to compaction and static adhesion.

The high-resolution from dry powder pressing additive manufacturing will lower monolith fabrication cost an order of magnitude to accelerate the adoption of emerging ceramic technologies. No existing ceramic production technology can combine multiple functional materials in the same layer or produce internal flow features at the proposed sub-mm scale.

The technology will be leased or sold to advanced ceramic fabricators to enable further technology developments in the ceramics industry. The manufacturing will first be applied to the energy market, but has the potential to impact defense and health imaging technologies as well.

This Small Business Innovation Research (SBIR) Phase I project seeks to scale the throughput capacity of a dry-powder pressing additive manufacturing technique that can fabricate multifunctional ceramic monoliths with internal flow structures.

Five key capabilities distinguish dry-powder pressing additive manufacturing from existing ceramic additive manufacturing methods: i) applicability to materials not amenable to laser sintering, ii) co-deposition of multiple materials with high lateral precision, iii) densification of materials typically incapable of pressureless sintering to full density, iv) a quality control step can reject a layer prior to adhering to prior layers, and v) co-deposition of fugitive material can form internal gas routing that eliminates costly and complex ceramic sealing technology in harsh environment applications.

The proposed work will advance the technology by creating a high-throughput printing system to deposit patterned 50 cm2 layers in a single pass, representing a 100x throughput increase. Automation will also address two precision targets; layer deposition below 7. 5mg/cm2 and lateral resolution less than 0.

5mm. The scope of work will advance the science of dry powder deposition and transfer to refine the processing capability for thinner layers and finer microfeatures while simultaneously engineering a high throughput device representative of pilot-scale manufacturing.

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 I: Commercial applications of CropMAP (Monitoring, Analysis, and Prediction) for oil seed fields 201 DAVID L BOREN BLVD RM 124A Please report errors in award information by writing to awardsearch@nsf. gov .

The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project involves the development and evaluation of the Crop Ecosystem Monitoring, Analysis, and Prediction (CropMAP) tool. This project addresses the critical need to support food security profitability by optimizing resource management and decision-making through advanced monitoring and predictive analytics in crop production.

The significance of this research lies in its potential to enhance agricultural productivity and sustainability across the United States, thereby improving the lives of farmers by increasing yield outputs and reducing losses. Furthermore, the successful commercialization of CropMAP could generate substantial economic benefits, including increased tax revenues and job creation in the agricultural sector. By aligning with NSF?

s mission to advance the progress of science, this project contributes to the scientific understanding of agricultural ecosystems and impacts related fields such as environmental science and economics. This project represents a significant technical innovation in the field of precision agriculture through the development of the CropMAP tool, a high-risk effort with substantial potential for high impact.

CropMAP integrates novel algorithms and models with real-time data feeds for enhanced monitoring and predictive analytics of crop conditions.

The primary innovation involves the application of machine learning techniques to satellite images and climate data to predict crop yields, water usage, and soil health more accurately than current methods allow and the use of artificial intelligence to make actionable insights timely available to technical and non-technical users.

The goals of this project are to validate these models' effectiveness in real-world settings and to establish a scalable framework for its application across various agricultural contexts. The project will employ rigorous methodological approaches, including the use of time-series image analytics and data-driven diagnostic models, to achieve these objectives.

Through its focus on innovation and scalability, the project aims to set a new standard in agricultural practices, ultimately facilitating better resource management and sustainability. 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 I: Ultra-Sensitive and Multiplexed Pathogen Profiling for Neonatal Sepsis Detection Cambridge, MA 02139--3544 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 I project lies in its potential to transform sepsis diagnostics and patient care, particularly for vulnerable newborns.

Globally, sepsis is responsible for up to a third of neonatal deaths, with an increased burden in low- and middle- income countries. Current diagnostic methods rely heavily on blood cultures, which require substantial blood volumes, take 24+ hours to yield results, and frequently produce false negatives.

The proposed DNA-based technology seeks to comprehensively identify and quantify pathogens in a few hours from small volumes of blood, critical capabilities for low-birthweight and immunocompromised newborns. The clinical impacts could include more targeted antibiotic therapy and guided therapy durations that could translate to thousands of lives saved annually, shortened hospital stays, and reduced readmission rates.

This technology's compatibility with existing digital PCR hardware enables a very capital-efficient development path and reduces barriers to adoption in hospitals given the expanding use of digital PCR in clinical diagnostic laboratories. Enabling routine pathogen testing for sepsis in any community hospital represents a major opportunity.

The core proposed technologies can build on a growing installed base of compatible hardware, adding new tests in other diverse applications. This Small Business Innovation Research (SBIR) Phase I project aims to develop a rapid diagnostic test for sepsis-causing pathogens in plasma samples with available digital PCR hardware that can scale to cover all critical pathogens.

Pathogen identification tests must be very sensitive, fast, and able to detect a wide range of organisms. Specific innovations are proposed to make the test suitable for neonates with less than 1 mL of blood per test. Currently, digital PCR-based technologies achieve state-of-the-art sensitivity with results in a few hours but are limited in their panel breadth, typically detecting no more than a dozen analytes simultaneously.

DNA sequencing can achieve comprehensive detection but is complex, costly for on-demand use, and much slower than PCR. The proposed