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The Award database is continually updated throughout the year. As a result, data for FY21 is not expected to be complete until September, 2022.

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.

  1. Spray-based visual indicator of opioids for the rapid and effective decontamination of large areas

    SBC: Triton Systems, Inc.            Topic: CBD20AT001

    Triton Systems, Inc. will collaborate with The University of Massachusetts Lowell to develop a spray-based technology for the rapid, visual identification of opioids, turning contaminated areas from nondescript white powders to a brightly colored spots for easy recognition with the naked eye. The technology leverages molecular recognition units that bind strongly and specifically to a variety of o ...

    STTR Phase I 2021 Department of DefenseOffice for Chemical and Biological Defense
  2. Opioid Indicator Spray for Complex Surfaces

    SBC: Clear Scientific, LLC            Topic: CBD20AT001

    Opioids, especially synthetic opioids, are a constant threat to the safety of civilian first responders; there are now serious concerns about their use in modern chemical warfare. Fentanyl and its many analogues (carfentanil, remifentanil, lofentanil, mefentanil, sufentanil, etc.) are extremely hazardous—if ingested or inhaled, many synthetic opioids cause lethal intoxication with only a few gra ...

    STTR Phase I 2021 Department of DefenseOffice for Chemical and Biological Defense
  3. Eye-readable Solution-based Dye Displacement Probe for Large-area Detection of Opioids

    SBC: Intelligent Optical Systems, Inc.            Topic: CBD20AT001

    Intelligent Optical Systems, Inc., in collaboration with Bowling Green State University, proposes to develop a field-rugged, eye-readable indicating spray solution that can immediately detect synthetic opioids over a large area of contamination (i.e., military vehicles, individual protective equipment, clandestine labs, etc.). The proposed chemosensor in a spray solution format will detect multipl ...

    STTR Phase I 2021 Department of DefenseOffice for Chemical and Biological Defense
  4. 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
  5. 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 terriblemorbidity and high mortality rate of MVD, this represents a critical threat to the operational readiness of the Warfighter. While traditionalvaccines have proven to be a huge contribution to public health, they do have some limitations especially in the cont ...

    STTR Phase I 2018 Department of DefenseOffice for Chemical and Biological Defense
  6. Host-Targeted Antiviral Drugs Providing Rapid, Scalable, Stable, Broad-Spectrum MedicalCountermeasure Against Marburg Virus

    SBC: Evrys Bio, LLC            Topic: CBD18A002

    In a pandemic or exposure to weaponized Marburg virus the morbidity, mortality and social unrest that will ensue before vaccines orMilitary operating on foreign soil as well as to the population of the US Homeland. Practically all threat agents of biological origin (except forcertain non-proteinaceous toxins, typically produced by marine organisms and fungi) have their structure and vital function ...

    STTR Phase I 2018 Department of DefenseOffice for Chemical and Biological Defense
  7. Marburg Virus Prophylactic Medical Countermeasure

    SBC: Flow Pharma, Inc.            Topic: CBD18A002

    Flow Pharma, Inc. is a biotechnology company in the San Francisco Bay Area developing fully synthetic cytotoxic T lymphocyte (CTL)stimulating peptide vaccines for Marburg virus. The FlowVax vaccine platform allows us to create dry powder formulations of biodegradablemicrospheres and TLR adjuvants incorporating class I and class II T cell epitopes. FlowVax vaccines can be designed for delivery by i ...

    STTR Phase I 2018 Department of DefenseOffice for Chemical and Biological Defense
  8. Circulating Diagnostic Markers of Infectious Disease

    SBC: PATHOVACS INCORPORATED            Topic: CBD18A001

    The focus of this STTR phase I component is on proof-of-concept studies demonstrating applicability of technical approaches for identificationof circulatory diagnostic markers for infectious disease. Therefore, the primary objective of this project is to determine feasibility of one suchtechnical approach called Proteomics-based Expression Library Screening (PELS), for identification of pathogen-d ...

    STTR Phase I 2018 Department of DefenseOffice for Chemical and Biological Defense
  9. 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
  10. 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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