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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.

  1. Activated Reactants to Reduce Fuel Cell Overpotentials

    SBC: JSJ Technologies, LLC            Topic: A10AT011

    The current produced in electrochemical galvanic cells is primarily dependent on the rate of the electrode reactions where the cell's anode is less negative, supplying less energy than thermodynamically predicted, and the cell's cathode is less positive, supplying less energy than thermodynamically predicted. Reduction of electrochemical overpotentials in electrochemical systems has been the prim ...

    STTR Phase I 2010 Department of DefenseArmy
  2. Active Imaging through Fog

    SBC: SA PHOTONICS, LLC            Topic: N18AT021

    Active imaging systems are used to for imaging in degraded visual environments like that found in marine fog and other environments with a high level of attenuation and scattering from obscurants like fog, rain, smoke, and dust.These systems are still limited in range and resolution. SA Photonics is taking advantage of multiple image enhancement techniques, like wavelength tunability, pulse contro ...

    STTR Phase I 2018 Department of DefenseNavy
  3. Adaptive Fleet Synthetic Scenario Research

    SBC: KAB LABORATORIES INC.            Topic: N10AT044

    Synthetic scenario-based training of Navy personnel in the use of Navy SIGINT/IO systems has helped to reduce training costs, and it has enabled the personnel to be trained in an environment that sufficiently approximates real-world situations that could not otherwise be accomplished within the class room. However, scenario development is highly complex and involves a great deal of human effo ...

    STTR Phase I 2010 Department of DefenseNavy
  4. Adaptive Learning for Stall Pre-cursor Identification and General Impending Failure Prediction

    SBC: Frontier Technology Inc.            Topic: N10AT008

    Frontier Technology, Inc. (FTI) and Northeastern University propose to investigate and develop an innovative approach to predict stall events of aircraft engines prior to occurrence and in sufficient time to allow the FADEC controller to adjust engine variables. The team will utilize vector quantization and neural network techniques to develop accurate models of engine behavior that will be used t ...

    STTR Phase I 2010 Department of DefenseNavy
  5. Adaptive Markov Inference Game Optimization (AMIGO) for Rapid Discovery of Evasive Satellite Behaviors

    SBC: INTELLIGENT FUSION TECHNOLOGY, INC.            Topic: AF17CT02

    Space superiority requires space protection and space situational awareness (SSA), which rely on rapid and accurate space object behavioral and operational intent discovery. The focus of this project is to develop a stochastic approach for rapid discovery of evasive satellite behaviors. Designing the innovative decision support tool has numerous challenges: (i) partial observable actions; (ii) eva ...

    STTR Phase I 2018 Department of DefenseAir Force
  6. Additive Manufacturing for Naval Aviation Battery Applications

    SBC: TEXAS RESEARCH INSTITUTE , AUSTIN, INC.            Topic: N18AT008

    Texas Research Austin (TRI-Austin) will partner with the University of Texas, Austin, and will use diverse printing technologies to fabricate the components of selected battery chemistries (Li-ion, Zn-air, Zn-Ag). In the Phase I base period, each battery component will be printed with a technology that has been previously used to deposit the required material (i.e. ADM for metals, SLS for polymers ...

    STTR Phase I 2018 Department of DefenseNavy
  7. Additive Manufacturing of Metallic Materials for High Strain Rate Applications

    SBC: MRL MATERIALS RESOURCES LLC            Topic: MDA17T001

    Metallic additive manufacturing (AM) is an attractive technology for the production of lethality test articles due to the potential for significantly reduced lead time and manufacturing cost.However, in order to be effective in providing accurate lethality data, the properties of the AM material have to match closely the properties of conventionally manufactured alloys found in real threat targets ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  8. Additive Manufacturing of Multifunctional Nanocomposites

    SBC: Sciperio, Inc.            Topic: A13AT010

    Sciperio with team members Georgia Institute of Technology and Centecorp have teamed up to develop an Additive Manufacturing Composite using nano and micro fillers. The team will develop multi-scale models that are supported by experimental characterization for advanced 3D Printable materials. Inelastic response of high strength hierarchical structures composed of engineered materials and specif ...

    STTR Phase I 2013 Department of DefenseArmy
  9. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: ARCTOS Technology Solutions, LLC            Topic: DLA18A001

    Universal Technology Corporation (UTC) has teamed with the University of Dayton Research Institute (UDRI), Stratonics, and Macy Consulting to demonstrate not only the transitionability into commercial systems, but also to develop the data analytics and monitoring and control requirements to extract the full value fromseveral sensors, including the Stratonics ThermaViz, acoustic and profilometry se ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  10. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: X-Wave Innovations, Inc.            Topic: DLA18A001

    Additive Manufacturing (AM) is a modern and increasingly popular manufacturing process for metallic components, but suffers from well known problems of inconsistent quality of the finished product. Process monitoring and feedback control are therefore crucial research areas with a goal of solving this problem. To address this concern, X-wave Innovations, Inc. (XII) and the University of Dayton Res ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
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