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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.

  1. AM Functionally Graded Graded Randomes for Hypersonic Vehicles

    SBC: KAI, LLC            Topic: A20008

    The innovation described in the Phase I proposal “Additive Manufacturing and Characterization of Radome Materials” is to achieve the first fiber-reinforced ceramic composite manufactured with selective laser sintering (SLS) Additive manufacturing (AM). The innovation is twofold, (i) development of a novel rapid-cured fiber-reinforced preceramic composite material that can be (ii) additively ma ...

    SBIR Phase I 2020 Department of DefenseArmy
  2. Energy Harvesting Multiferroic Resonating Beams for Unattended Sensors

    SBC: LYNNTECH INC.            Topic: A20018

    The US Army’s Network Command, Control, Communications and Intelligence (NC3I) modernization requires sensing assets capable of intelligent, autonomous and reliable processing and communications. Unattended sensors providing these capabilities can integrate with networks to provide this raw, processed, or fully analyzed sensor data. However, there is a need for powering these elements for long t ...

    SBIR Phase I 2020 Department of DefenseArmy
  3. High brightness mid-wave infrared coherent beam combiner based on QCL-PIC

    SBC: TRANSWAVE PHOTONICS, LLC            Topic: A20021

    TransWave Photonics proposes a photonic integration architecture for efficient coherent beam combining of quantum cascade arrays consisting of more than 10 elements to generate > 10 W continuous-wave output power with 90% combining efficiency at room temperature. The proposed beam combiner will produce diffraction-limited laser beam with at least one order of magnitude higher brightness than the s ...

    SBIR Phase I 2020 Department of DefenseArmy
  4. TiO2 Microflakes for IR Narrowband Transmission with Broadband Attenuation

    SBC: NANOHMICS INC            Topic: A20022

    To ensure the safety of military personnel, equipment, and installations there is a need to maintain a clear IR transmission band for friendly use while blocking a large swath of other IR wavelengths. In recent years, researchers have studied a series of nanomaterials that exhibit this type of transmission/attenuation behavior, which is typically achieved using nanoparticles with multiple resonanc ...

    SBIR Phase I 2020 Department of DefenseArmy
  5. High-Frequency, Surface-Mounted Nanomembrane based Strain Sensors for Non-Intrusive Pressure Measurements

    SBC: NANOSONIC INC.            Topic: A20029

    This DOD Phase I SBIR program would develop high-frequency, surface-mounted nanomembrane based strain sensors for non-intrusive pressure measurements. Such non-intrusive pressure sensors will be implemented using a series of surface-mounted high frequency nanomembrane strain sensors and supporting data acquisition and signal processing electronics, that are applied to existing gun tubes for dynami ...

    SBIR Phase I 2020 Department of DefenseArmy
  6. Reusable, Soft Catch System for Large Projectiles Traveling at High Speeds

    SBC: LUNA INNOVATIONS INCORPORATED            Topic: A20031

    The Army has a need to catch and recover projectiles after launch without damage to assess internal ballistics, design strength, and the function of the critical munition components (e.g. deployable fins and fuzes) . There is currently no system that can catch and recover large munitions traveling at high speeds without damaging the projectile. Current materials used to soft catch projectiles cons ...

    SBIR Phase I 2020 Department of DefenseArmy
  7. Dynamic frame rate CMOS camera

    SBC: ENCRYPTOR INC            Topic: A20037

    During the Phase I effort, we will develop, demonstrate and deliver a working device that meets the requirements of this solicitation. The primary specifications of the deliverable item include the use of a monochrome multi-MegaPixel CMOS Silicon camera sensor with appropriate read-out electronics formatted onto a USB-C interface. The prototype will connect directly to a notebook computer f ...

    SBIR Phase I 2020 Department of DefenseArmy
  8. Cyber Terrain and Electromagnetic Operating Environment (EMOE) Scenario Generation Toolkit (CTAEMOESGT)

    SBC: SILVER BULLET SOLUTIONS, INC.            Topic: A20038

    Cyber Electromagnetic Activities (CEMA) cross-cuts fires, aviation, other operations, and many aspects of Doctrine, Organization, Training, Materiel, Leadership and education, Personnel, and Facilities (DOTMLPF).  Knowledge of the interdependencies is critical to CEMA planning and executing a combined arms effect inside cyberspace.  This integration is a new concept and the transition is still i ...

    SBIR Phase I 2020 Department of DefenseArmy
  9. Air Surveillance Radar Classification Improvement

    SBC: Technology Service Corporation            Topic: A20039

    Increasingly capable Unmanned Aerial Systems (UASs) have made the accurate identification of such threats an important aspect of battlefield situational awareness. Army Counter-fire Target Acquisition (CTA) radars, including the man-portable AN/TPQ-50 and larger long-range AN/TPQ-53, provide the greatest opportunity to incorporate more advanced air target classification techniques because they are ...

    SBIR Phase I 2020 Department of DefenseArmy
  10. Mitigation of GMTI Radar False Alarms

    SBC: Technology Service Corporation            Topic: A20040

    Modern GMTI radar systems can detect the movement of unwanted clutter, including wind-blown foliage, waves, and rotating objects. The resulting false alarms create a distraction for radar operators who can miss critical information or improperly allocate resources. Machine Learning (ML) techniques provide a means of classifying detections and discarding these false alarms. In doing so, radar sensi ...

    SBIR Phase I 2020 Department of DefenseArmy
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