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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. Marburg Virus Prophylactic Medical Countermeasure

    SBC: Flow Pharma, Inc.            Topic: CBD18A002

    Through this STTR contract, we propose to evaluate the efficacy of our vaccine, FlowVax Marburg, in nonhuman primates (NHPs). This will be achieved through four Tasks. In Task 1, we will manufacture the vaccine in a quantity sufficient for the animal studies. In Task 2, we will perform MHC genotyping on a representative population of NHPs and, based on results, select a set of MHC-matched NHPs for ...

    STTR Phase II 2020 Department of DefenseOffice for Chemical and Biological Defense
  2. Marburg Virus Prophylactic Medical Countermeasure

    SBC: MAPP BIOPHARMACEUTICAL, INC.            Topic: CBD18A002

    There are currently no vaccines or therapeutics available for Marburg Virus Disease (MVD). Given the specter of weaponization and the terrible morbidity and high mortality rate of MVD, this represents a critical threat to the operational readiness of the Warfighter. While traditional vaccines have contributed greatly to public health, they have some limitations especially in the context of operati ...

    STTR Phase II 2020 Department of DefenseOffice for Chemical and Biological Defense
  3. Analysis and Modeling of Erosion in Gas-Turbine Grade Ceramic Matrix Composites (CMCs)

    SBC: ALPHASTAR TECHNOLOGY SOLUTIONS LLC            Topic: N19BT033

    A significant barrier to the insertion of ceramic matrix composite (CMC) materials into advanced aircraft engines is their inherent lack of toughness under erosion and post erosion. Our team will develop and demonstrate a physics-based model for erosion/post erosion of CMC’s at room and elevated temperatures (RT/ET). The ICME (Integrated Computational Material Engineering) Physics based Multi Sc ...

    STTR Phase I 2020 Department of DefenseNavy
  4. Three Dimensional Field of Light Display

    SBC: TRITON SYSTEMS, INC.            Topic: N19BT036

    As the Navy continues to reduce manpower requirements associated with operating ever-increasing technologically complex systems, new methods that enable natural and intuitive interaction with 3D data are required to reduce overall operator workload and to enhance situational awareness. Operators who cannot quickly access and interpret data are prone to errors ranging from missing critical data dur ...

    STTR Phase I 2020 Department of DefenseNavy
  5. Fully Automated Quantum Cascade Laser Design Aided by Machine Learning

    SBC: PENDAR TECHNOLOGIES LLC            Topic: N20AT003

    Pendar Technologies proposes to develop a QCL simulation tools that leverage machine learning to dramatically improve the speed of QCL device design. The innovative QCL design suite proposed will benefit from recent advances made by Pendar in bandstructure engineering, laser cavity design and thermal management at the chip and the package level.

    STTR Phase I 2020 Department of DefenseNavy
  6. Hexahedral Dominant Auto-Mesh Generator

    SBC: HYPERCOMP INC            Topic: N20AT004

    The objective of our proposed STTR phase-I work is to transition the latest advancements within the academic community to the design of a robust, user-friendly, and application-oriented tool for automatic hex-dominant meshing. Our software will fully couple CAD models to the discretized domain required by finite element software in structural analysis and other simulation and modeling applications ...

    STTR Phase I 2020 Department of DefenseNavy
  7. Hexahedral Dominant Auto-Mesh Generator

    SBC: M4 ENGINEERING, INC.            Topic: N20AT004

    Advances in both software and computer hardware have made the finite element method the preeminent choice for analyzing highly complex systems that are of great value to the Department of Defense.   The US Defense industry, however, continues to spend enormous time and resources in mesh generation, a key step in finite element analysis, despite progress that has been made in automated mesh gener ...

    STTR Phase I 2020 Department of DefenseNavy
  8. Ambient Quantum Processor compatible with an All-photonic Repeater Architecture

    SBC: CATALYTE, LLC            Topic: N20AT005

    The significance of the problem is to deploy combined quantum communication-and-processing near to Navy applications.   Our approach, when successful, would enable small, ambient operating QPUs to be connected at a distance by quantum-secure communication.  Unlike bulky optical components and in-contrast to cryogenic qubits, our system, using in situ generated photons, offers a practical s ...

    STTR Phase I 2020 Department of DefenseNavy
  9. High Efficiency Propeller for Small Unmanned X Systems using Advanced Composite Materials

    SBC: CATTO PROPELLERS            Topic: N20AT006

    In the proposed STTR study, Catto Propellers, Inc. (Catto) and the University of North Dakota (UND) will create an efficient new propeller design utilizing advanced composite materials for use on small unmanned x systems.  During Phase I, a comprehensive study will be conducted to develop a new propeller design in order to increase propeller efficiency, reduce aerodynamic noise and utilize innova ...

    STTR Phase I 2020 Department of DefenseNavy
  10. PARTEL: Periscope video Analysis using Reinforcement and TransfEr Learning

    SBC: MAYACHITRA, INC.            Topic: N20AT007

    We propose a suite of video processing algorithms utilizing the machine learning (ML) techniques of artificial intelligence (AI) reinforcement learning, deep learning, and transfer learning to process submarine imagery obtained by means of periscope cameras. Machine learning (ML) can help in addressing the challenge of human failure of assessing the data of periscope imagery. Though pre-tuned blac ...

    STTR Phase I 2020 Department of DefenseNavy
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