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The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.

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.

  1. HIGH STRENGTH, WATER-FILLED CERAMIC NANOCOMPOSITE MICROCAPSULES WITH LOW PERMEABILITY FOR SELF-SEALING FUEL BLADDERS

    SBC: NANOSONIC INC.            Topic: N19BT030

    During the proposed Navy STTR program, NanoSonic and Virginia Tech will design and synthesize innovative, high strength ceramic nanocomposite microcapsules filled with > 80 volume percent water that are empirically optimized to function as readily dispersed powdered additives with long-term water retention, durability during air craft bladder production, and rupture during ballistic shock. NanoSon ...

    STTR Phase I 2019 Department of DefenseNavy
  2. Robust, Low Permeability, Water-Filled Microcapsules

    SBC: LUNA INNOVATIONS INCORPORATED            Topic: N19BT030

    The Navy is actively developing a self-sealing, water-activated fuel bladder to mitigate fuel leaks upon mechanical shock (e.g. penetration by a .50 caliber bullet). To circumvent the requirement of an external water source to activate the self-sealing, this system will require water-filled microcapsules that can be incorporated directly into the polymeric matrix of the fuel bladder. Upon mechanic ...

    STTR Phase I 2019 Department of DefenseNavy
  3. Atomic Triaxial Magnetometer

    SBC: VESCENT PHOTONICS LLC            Topic: N19AT006

    Vescent Photonics and MIT Lincoln Labs (MIT-LL) propose to develop a quantum-based vector magnetometer with low size, weight, power, and cost (SWaP+C) for Navy applications. The proposed system will rely on probing magnetically-sensitive, atomic-like transitions of nitrogen-vacancy (NV) centers in diamond to provide stable, high-bandwidth readout of the vector magnetic field with sub-picotesla sen ...

    STTR Phase I 2019 Department of DefenseNavy
  4. Atomic Triaxial Magnetometer

    SBC: SOUTHWEST SCIENCES INC            Topic: N19AT006

    Improved magnetic anomaly detection can counter threats from quieter submarines and mines. This Phase I STTR project will address this goal using improved atomic magnetometry methods based on pumping alignment coherence in rubidium vapor. A means of changing the polarization of the pump light will ensure that atomic coherence can be pumped in any orientation of the magnetic field. Multiple probe b ...

    STTR Phase I 2019 Department of DefenseNavy
  5. Optimized Higher Power Microwave Sources

    SBC: XL SCIENTIFIC LLC            Topic: N19AT001

    Verus Research and the University of New Mexico (UNM) are pleased to respond to the Navy Phase I STTR solicitation N19A-T001 titled “Optimized Higher Power Microwave Sources.” Verus Research, in collaboration with UNM, propose to develop a GW-class, S-band, high power microwave (HPM) source to integrate in vehicle and vessel stopping systems. Our integrated approach ensures the objectives for ...

    STTR Phase I 2019 Department of DefenseNavy
  6. GECCO: Gecko-gripper for EOD with Cavitation Cleaning Operation

    SBC: VALOR ROBOTICS, LLC            Topic: N19AT011

    The objective of the Phase I proposal is to investigate the application of controlled cavitation cleaning technology in conjunction with gecko-inspired mechanical adhesion and soft elastomeric applicators for use in non-intrusive EOD operations. This investigation requires the proof-of-concept testing and validation of a controlled cavitation cleaning mechanism, and a soft robotic gecko-inspired m ...

    STTR Phase I 2019 Department of DefenseNavy
  7. Quench Monitoring and Control System for High-Temperature Superconducting Coils

    SBC: ADVANCED CONDUCTOR TECHNOLOGIES LLC            Topic: N19AT016

    The Navy has been developing superconducting systems, based on high-temperature superconductors (HTS), for future use on Navy ships. One of the challenges associated with superconducting magnets is the possibility of a quench, which is an event where a local hot spot develops within the superconductor that quickly spreads throughout the device, driving it into its normal and dissipative state. Sen ...

    STTR Phase I 2019 Department of DefenseNavy
  8. Novel Development of an Intelligent Quench Detection (QD) Method for HTS Coils

    SBC: TAI-YANG RESEARCH COMPANY            Topic: N19AT016

    Energy to Power Solutions (e2P) has teamed with quench detection (QD) expert Dr. Yuri Lvovsky (retired GE), Dr. Sastry Pamidi of the Center for Advanced Power Systems (FSU-CAPS), and American Superconductor Corporation (AMSC) to design, fabricate, and test a robust, reliable, and low cost QD system. e2P’s proposed system is a vastly different quench avoidance system that will provide multiple le ...

    STTR Phase I 2019 Department of DefenseNavy
  9. Unified Logging Architecture for Performance and Cybersecurity Monitoring

    SBC: INNOVATIVE DEFENSE TECHNOLOGIES, LLC            Topic: N19AT012

    In order to achieve real-time monitoring, analysis, and alerting for complex systems, a unified logging architecture must exist that can support the collection and analysis of big data. Our technical objective is to develop a unified logging architecture that supports collection, aggregation, storage, and analysis of system performance and cybersecurity logs, events, and alerts produced by Naval C ...

    STTR Phase I 2019 Department of DefenseNavy
  10. Predictive Graph Convolutional Networks- 19-008

    SBC: METRON INCORPORATED            Topic: N19AT017

    Metron and Northeastern University propose to design, develop, and validate a proof-of-concept predictive Graph Convolutional Network (GCN) capability using open source Reddit and GDELT data. We propose: (1) to extract and preprocess open-source Reddit and GDELT data, (2) to design a predictive graph convolutional neural network model, (3) to implement and train that model, and (4) to validate the ...

    STTR Phase I 2019 Department of DefenseNavy
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