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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. Formable Preform for Advanced Ceramic Matrix Composite Structures

    SBC: PEPIN ASSOCIATES INC            Topic: DLA23A003

    Pepin Associates, Inc. has developed a unique, aligned discontinuous textile reinforcement for composite structures.  This reinforcement architecture is composed of short, overlapped tow segments.  The architecture allows the textile to stretch in its reinforcement direction.  This ability permits rapid fabrication of complex shaped thermal protection system structures from simple low cost sh ...

    STTR Phase I 2023 Department of DefenseDefense Logistics Agency
  2. 3D Printed Ablative Re-Entry Vehicle Heat Shields

    SBC: MANTIS COMPOSITES INC            Topic: DLA23A003

    Re-Entry Vehicles (RVs) are a critical component of the strategic weapon arsenal. While physics packages themselves have a substantial deterrence value, the ability to deliver those weapons quickly and with high survivability unlocks the ability to maintain a truly global deterrence. However, these thermal protection systems are encountering two developing challenges: Industrial Base Attrition: Th ...

    STTR Phase I 2023 Department of DefenseDefense Logistics Agency
  3. Improved surface interface of OPF and PAN carbon fibers for Carbon-Carbon processing

    SBC: ALKEMIX CORPORATION            Topic: DLA23A003

    Specialized materials such as Carbon-Carbon (C-C) are required as primary structural and thermal protection elements to sustain the severe temperatures on the surface of Hypersonic Vehicles during high-speed flight. The C-C material currently qualified at Northrop in Hypersonic Glide-Bodies or “Aeroshells” consist of phenolic resin and a low fired, stretch broken polyacrylonitrile fiber (LFSP ...

    STTR Phase I 2023 Department of DefenseDefense Logistics Agency
  4. Low Cost MLMI as a Hypersonic Aerostructure with TPS

    SBC: Peregrine Falcon Corporation            Topic: DLA23A003

    As Hypersonic weapons enter the US arsenal there is a need for cost control and high rate of production which is currently not being met by the current state of the art material systems, like Carbon-Carbon. What is needed is a Thermal Protection System (TPS) for aero structures that can survive the Hypersonic environments of high heat in oxygenated environments. A means to do this is using Peregr ...

    STTR Phase I 2023 Department of DefenseDefense Logistics Agency
  5. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  6. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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