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Award Data
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.
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Portable Phage Preparation Technology for Field Application
SBC: CFD RESEARCH CORPORATION Topic: A20BT023Bacteriophages (phages) are becoming important therapeutic candidates against multidrug resistant (MDR) bacterial infections. There is a need to isolate and concentrate phages collected from the field to enhance specimen storage stability for long-distance transport to specialized laboratories for subsequent analysis. Environmental phage preparation requires expensive, bulky instrumentation and in ...
STTR Phase II 2023 Department of DefenseDefense Health Agency -
Flexible Microfluidic Process Technology for Biopharmaceutical Purification of Bacteriophages
SBC: CFD RESEARCH CORPORATION Topic: DHA21C002Multidrug resistant (MDR) bacterial wound infections remain a persistent challenge for front-line military medical providers in prolonged care treatment. Bacteriophage (phage) therapeutics have demonstrated preclinical and clinical efficacy against ESKAPEE infections. Phage production however remains a challenge to remove common pyrogen contaminants from phage products, including endotoxins (lipop ...
STTR Phase II 2023 Department of DefenseDefense Health Agency -
Generative Modeling of Multispectral Satellite Imagery
SBC: NOVATEUR RESEARCH SOLUTIONS LLC Topic: DTRA22D001This STTR Phase I project proposes novel deep learning models for generating realistic multi-spectral remote sensing imagery, specifically in the infrared (IR) and near-infrared (NIR) bands. The proposed system enables synthesis of semantically realistic imagery and provides parametric control of synthesizing objects-of-interest, type of terrain and land cover, time or season, weather, cloud cover ...
STTR Phase I 2023 Department of DefenseDefense Threat Reduction Agency -
Generative Modeling of Multispectral Satellite Imagery
SBC: Applied Research In Acoustics LLC Topic: DTRA22D001To address the challenge DTRA faces in identifying rare objects of interest to defeat improvised threat networks using multispectral imagery, small business ARiA and research institution Michigan Technological University (MTU) will develop and demonstrate the feasibility of the Generative Augmentation Process (GAP). The Phase I effort will (1) conduct a proof-of-concept study for GAP by developing ...
STTR Phase I 2023 Department of DefenseDefense Threat Reduction Agency -
Flexible Microfluidic Process Technology for Biopharmaceutical Purification of Bacteriophages
SBC: CFD RESEARCH CORPORATION Topic: DHA21C002Multidrug resistant (MDR) bacterial wound infections remain a persistent challenge for front-line military medical providers in prolonged care treatment. Phage therapeutics have demonstrated preclinical and clinical efficacy against ESKAPEE infections. Phage production however remains a challenge to remove bacterial remnants including LPS endotoxins and other pyrogens. To meet regulatory requireme ...
STTR Phase I 2022 Department of DefenseDefense Health Agency -
Develop and demonstrate a technology for isolation of bacteriophages with enhanced antibiofilm activity
SBC: CFD RESEARCH CORPORATION Topic: DHA20B003The twenty-first century has seen a global rise in bacterial infections exhibiting antimicrobial-resistance (AMR). More than ninety percent of chronic wounds contain microbial biofilms that exhibit AMR, and the bacteria responsible for several of these recalcitrant infections are called ESKAPEE pathogens. Eradicating ESKAPEE pathogenic infections is challenging, but bacteriophage (phage) therapy i ...
STTR Phase II 2022 Department of DefenseDefense Health Agency -
Compact Laser Drivers for Photoconductive Semiconductor Switches- STTR Phase II Sequential
SBC: SCIENTIFIC APPLICATIONS & RESEARCH ASSOCIATES, INC. Topic: DTRA16A004For effective protection against radiated threats, produced by high altitude electromagnetic pulse (HEMP) caused by nuclear detonations and high-power microwave (HPM) Directed Energy (DE) weapons it is important to understand not only the physics of the threats, but also to quantify the effects on mission-critical electrical systems. EMP/HMP simulators enable threat level testing of MCS and provid ...
STTR Phase II 2022 Department of DefenseDefense Threat Reduction Agency -
Wide Area Distributed Algorithms for Cooperative Source Identification, Characterization, and Localization
SBC: THE PROBITAS PROJECT, INC. Topic: DTRA21B003Current radiation detection algorithms are based on the concept that each detector operates independently. The Probitas Project, Inc. (Probitas) and the Lawrence Berkeley National Laboratory (LBNL) propose to show the benefits of data fusion to improve the identification, localization, and characterization of a radioactive source in a complex scene as compared to a singular detector algorithm. We ...
STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency -
SAR AI Training dataset generated using Reification
SBC: Arete Associates Topic: DTRA21B001The Synthetic Aperture Radar (SAR) Image Generation Data Augmentation (SIGDA) system is achieved using SAR simulators and the Arete’s Reification approach. Large, realistic datasets will be generated using the Arete Reification capability. These large Reified datasets are then used to train machine learning or Artificial Intelligence (AI), Automatic Target Recognition (ATR) classification algori ...
STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency -
Numerics-Informed Neural Networks (NINNs)
SBC: KARAGOZIAN & CASE, INC. Topic: DTRA21B002The overall goal is to develop numerics-informed neural networks (NINNs) and DeepOnets for chemical reactions and for PDEs with spatial derivatives improve the computational efficiency of the chemical kinetics models for chemical weapon agents and simulants. Based on the first NINN developed by the Karniadakis’s group in 2018, which blends the multi-step time-stepping with deep neural networks, ...
STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency