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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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Environmentally Robust, Fluorine-Free, Elastomeric Barrier Composite for CBRN Protective Ensembles
SBC: NANOSONIC INC. Topic: CBD222001Through the proposed SBIR program, NanoSonic shall design, fabricate, test, and validate an environmentally robust, fluorine-free elastomeric composite barrier material for class 1 NFPA 1994 certified chemical, biological, radioactive, and nuclear (CBRN) protective ensembles. The material shall be chemically resistant against the liquid and vapor challenge chemicals listed in NFPA 1994 and shall m ...
SBIR Phase I 2023 Department of DefenseOffice for Chemical and Biological Defense -
Chemical-Imaging Registration and Multi-modal Analysis
SBC: INTELLISENSE SYSTEMS INC Topic: CBD212001To address the Chemical and Biological Defense (CBD) agency’s need for a new deep learning (DL)-based threat detection solution that fuses imaging sensors and chemical/biological sensors to detect and locate concealed chemical threats, Intellisense Systems, Inc. (Intellisense) proposes development of a new Chemical-Imaging Registration and Multimodal Analysis (CIGMA) solution. CIGMA will compris ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
Learning-based Threat Classification and Localization Using Infrared Imaging and Ion Mobility Spectrometry
SBC: KEF ROBOTICS INC Topic: CBD212001Concealed chemical threats represent a significant hazard to field operators. Even brief exposure to chemicals can cause lasting physical, physiological, and mental damage. Field operators require tools and techniques to more quickly, safely, and reliably locate and identify chemical threats. Chemical detectors typically excel at either threat classification or threat localization, but seldom both ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
Multi-Modal Detection of Chemical Threats Using Deep Learning
SBC: NOVATEUR RESEARCH SOLUTIONS LLC Topic: CBD212001This SBIR Phase I project proposes development of deep learning based chemical threat detection and localization framework that exploits multiple sensor data streams including imaging and chemical sensors. The proposed technologies combine deep hypernetworks and reinforcement learning techniques to fuse sensor data streams in an online fashion. Novateur team will perform training of deep networks ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
CBRNE Augmented Reality Display and Device Integration Network
SBC: INTELLISENSE SYSTEMS INC Topic: CBD212002To address the CBD’s need for an augmented reality (AR) display that shows chemical, biological, radiological, nuclear (CBRN), and electro-optical/infrared (CBRNE) threats, Intellisense Systems, Inc. (Intellisense) proposes to develop a new CBRNE Augmented Reality Display and Device Integration Network (CARDDIN), based on a modular network that incorporates CBRNE and geolocation information from ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
Augmented reality and data fusion for CBRN threats
SBC: Gamma Reality Inc. Topic: CBD212002One of the hallmarks of our modern world is the increasing ubiquity of sensors and platforms deployed in complex environments. On the battlefield, there are a wide variety of sensors integrated with platforms ranging from large vehicles, such as the NBCRV, to drones, ground robots, light tactical off road vehicles, and on the Warfighters themselves. Collecting, correlating, analyzing, and communic ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
Aerosol Ionization Mass Spectrometer (AIMS)
SBC: HEDGEFOG RESEARCH INC. Topic: CBD212003There is an urgent need to rapidly test warfighters and their surrounding environment for disease-causing pathogens such as SARS-CoV-2. Existing methods for identifying pathogens in people usually require significant time and sample preparation. In addition, detecting pathogens such as SARS-CoV-2 in the air presents an even greater challenge. To meet the DoD need for field portable bioaerosol iden ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
Multi Injection Vial Anti-contamination cap (MIVA-Cap)
SBC: CHROMOLOGIC LLC Topic: CBD212004To meet CBD’s need for a multi-dose formulation of scopolamine hydrobromide trihydrite ChromoLogic proposes to develop a vial add-on containing a self-decontamination cap with a self-healing material to eliminate evaporation and contamination risks through needle punctures in the septum. CL will also perform forced degradation and stability tests with a panel of preservatives to select optimal p ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
Succor Combat Foam for Treatment of Dermal Injuries Caused by Sulfur Mustard
SBC: CRITICAL INNOVATIONS LLC Topic: CBD212005The Joint Project Manager for Chemical, Biological, Radiological, and Nuclear Medical (JPM CBRN Medical) seeks to develop dermal dressing technologies that provide multiple advantages over current wound dressings for treating dermal injuries caused by sulfur mustard. Critical Innovations, Battelle's Biomedical Research Center, and NDA Partners, with consultants Captain David Tanen, MD, FAAEM, FACM ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
DOCTRINe-based AIded target REcognition (DOCTRINAIRE) for the IC
SBC: COVAR, LLC Topic: NGA203005CoVar’s DOCTRINAIRE is a new approach to computer aided object annotation that is modeled after the way expert end-users leverage generic, robust background information (e.g., what wheels look like) and known doctrine (the size and shape of components on a pickup truck) to perform reliable, explainable object detection and annotation. Our approach solves the robustness problem by training a reli ...
SBIR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency