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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.

Displaying 1 - 10 of 6141 results
  1. DIGITAL ENGINEERING- Integration of Fiber Optics Systems Design, Supportability, and Maintainability

    SBC: Critical Frequency Design, LLC            Topic: N23AT002

    Advancements in optical transport and signal processing for digital and Microwave Photonics (MWP) technology have enabled higher bandwidth, throughput, Dynamic Range (DR), and communication link budgets. Optical transport eliminates heavy shielded twisted pair or coaxial cables, reducing Size, Weight, and Power (SWaP) and provides Electromagnetic Interference (EMI) immunity compared to traditional ...

    STTR Phase I 2023 Department of DefenseNavy
  2. Advanced Physics Modeling for Gas Turbine Particulate Ingestion

    SBC: COMBUSTION RESEARCH & FLOW TECHNOLOGY INC            Topic: N23AT003

    Ingestion of silicate particles into aircraft gas turbine (GT) engines remains a serious hazard to both commercial and military aircraft. Helicopters and low-flying fixed-wing aircraft in desert environments are exposed to airborne sand particulate. Both commercial and military aircraft can be exposed to runway dust as well as high-altitude volcanic ash. These silicate particulates present a hazar ...

    STTR Phase I 2023 Department of DefenseNavy
  3. Microwave Radiator for Curing Polymer Composites (MRCPC)

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT006

    The Navy requires a low-cost, industrial microwave system for curing aerospace composite materials. In this Phase I STTR proposal, Physical Sciences Inc outlines the development of a microwave applicator that uses low-cost RF sources and can be installed in heritage autoclaves for curing large aerospace composite parts. This technology has the potential to improve cured mechanical properties, and ...

    STTR Phase I 2023 Department of DefenseNavy
  4. Large-scale Meta-optic Optimization

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT008

    Physical Sciences Inc. (PSI), in collaboration with Stanford University, will develop an electromagnetic simulation package used for the development and optimization of large-scale meta-optics, and demonstrate the functionality of the package in the long-wave infrared (LWIR). Our team will combine recent progress in physics-augmented deep learning neural networks with rigorous far-field diffractio ...

    STTR Phase I 2023 Department of DefenseNavy
  5. INTELLIPHASE: Software Platform for Fouling Monitoring and Prediction

    SBC: Interphase Materials, Inc.            Topic: N23AT010

    In response to Navy STTR topic N23A-T010, Interphase Materials (IPM) in collaboration with the Virginia Tech Applied Research Corporation, propose the development of INTELLIPHASE, a sensor fusion and data analysis and software package, for the monitoring and prediction of the antifouling lifespan of TBTO in sonar radar domes. Sonar domes are necessary for optimal sonar performance, but require Tri ...

    STTR Phase I 2023 Department of DefenseNavy
  6. Gradient index for reduced integration costs (GRIN-RICH)

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT011

    Physical Sciences Inc. partnered with Alfred University will develop an F/1, 90 degree full field of view MWIR/SWIR gradient index (GRIN) compound lens for reduced size and lens integration cost. The element-by-element achromatization and athermalization of GRIN provide useful performance improvements to GRIN systems. Element count is reduced (= 2), diversity of optical material needed is fixed, a ...

    STTR Phase I 2023 Department of DefenseNavy
  7. Sensor Modality Translation through Contrastive Deep Learning

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT013

    Physical Sciences Inc. (PSI), in collaboration with the University of Rhode Island, proposes to develop an advanced algorithm suite for data translation across sensing modalities to support the development of automated target recognition and classification algorithms for Unmanned Underwater Vehicles. The proposed Deep Diffusion Sensor Translation (DDST) leverages recent advancements in generative ...

    STTR Phase I 2023 Department of DefenseNavy
  8. Scalable JP-10 Production from Sustainable Resources

    SBC: MAINSTREAM ENGINEERING CORP            Topic: N23AT015

    Mainstream Engineering, in cooperation with the University of Massachusetts Lowell (UML), will develop and demonstrate a process to produce renewable JP-10. The process includes several reaction steps that begin with biomass-derived xylose. Our JP-10 process minimizes the number of reactors and will be continuous by integrating all the steps. To date, only one renewable route to JP-10 has been pro ...

    STTR Phase I 2023 Department of DefenseNavy
  9. HTPB Predictive Model Development for Rocket Motors

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT018

    Physical Sciences Inc. and Purdue University propose to develop a chemical model that accurately predicts the performance of hydroxyl terminated polybutadiene (HTPB) polymer commonly used as a propellant binder in rocket motors. The model will utilize chemical and physical data from HTPB feedstock to predict propellant cure kinetics, mechanical properties, and aging performance. The model will inc ...

    STTR Phase I 2023 Department of DefenseNavy
  10. A Chemometric Approach to Categorize HTPB and Reliably Predict Outcomes in Gum stock and Propellant Formulations

    SBC: HELICON CHEMICAL COMPANY LLC            Topic: N23AT018

    This Phase I proposal will utilize multivariate regression, including cluster and principal component analysis to categorize lots of HTPB R-45M based on their physicochemical properties. Artificial intelligence-driven pattern recognition analysis will be performed on a variety of HTPB characterization data and spectra. Categorized data will be assessed against measured outcomes (pot life, mechanic ...

    STTR Phase I 2023 Department of DefenseNavy
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