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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. A Generalized Model for Assessing Manufacturability of 2D and 3D Reinforced High-Temperature Composites

    SBC: MATERIALS RESEARCH & DESIGN INC            Topic: DLA23A003

    Materials Research & Design, Inc. (MR&D) and their program partners propose a research and development (R&D) effort to identify, develop, and demonstrate a class of high-temperature resistant TPS materials and production processes which seek to increase domestic production capacity, affordability, and supply chain resiliency. Under the effort proposed herein, MR&D will work closely with the Nation ...

    STTR Phase I 2023 Department of DefenseDefense Logistics Agency
  2. 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
  3. 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
  4. 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
  5. Characterize Automated Fine Unit Cell Preforms for Future Nose Tip TPS

    SBC: Bally Ribbon Mills            Topic: DLA23A003

    BRM will weave and supply material to Sandia National Laboratory for characterization. Sandia National Laboratory has experience with nose tip applications, and they are currently working to redesign sounding rocket leading edges. BRM and Sandia National Laboratory shall characterize the continuously woven preforms for the sounding rocket leading edge, but this work will also feed into the charact ...

    STTR Phase I 2023 Department of DefenseDefense Logistics Agency
  6. 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
  7. AI/ML Aided Aviation Sensors for Cognitive and Decision Optimization

    SBC: KRTKL INC.            Topic: SOCOM23B001

    krtkl (“critical”) will conduct a Phase I Feasibility Study to identify the best approach for reducing aviator cognitive load by optimizing information delivery and decision-making based on a thorough analysis of existing platforms, sensors, data sources, and onboard compute resources. This information will be used to identify Artificial Intelligence and Machine Learning based algorithms for p ...

    STTR Phase I 2023 Department of DefenseSpecial Operations Command
  8. AI/ML Aided Aviation Sensors for Cognitive and Decision Optimization

    SBC: PARRY LABS, LLC            Topic: SOCOM23B001

    Existing airborne defense systems integrate a wide variety of sensors necessary to provide operators with situational awareness across the visual, thermal, signals, and electromagnetic spectrums. To date, individual sensor systems have been largely stove-piped, as have Artificial Intelligence/Machine Learning (AI/ML) and advanced, Size, Weight, and Power (SWaP)-optimized data processing systems. T ...

    STTR Phase I 2023 Department of DefenseSpecial Operations Command
  9. Self-Supervised Training in Geospatial Applications with a Robust Hierarchical Vision Transformer (STAR)

    SBC: UNIVERSITY TECHNICAL SERVICES, INC.            Topic: OSD22A001

    Satellite Imagery in Geospatial Intelligence (GEOINT), in conjunction with imagery intelligence (IMINT), geospatial information, and other means of gaining intelligence, has greatly improved the potential of the warfighter and decision makers enabling them to gain a more comprehensive perspective, an in-depth understanding, and a cross-functional awareness of the operational environment. The Artif ...

    STTR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency
  10. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Convolutional Neural Networks (DCNNs) have become ubiquitous in the analysis of large datasets with geometric symmetries. These datasets are common in medicine, science, intelligence, autonomous driving and industry. While analysis based on DCNNs have proven powerful, uncertainty estimation for such analyses has required sophisticated empirical studies. This has negatively impacted the effect ...

    STTR Phase II 2022 Department of DefenseNational Geospatial-Intelligence Agency
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