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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. Additive Manufacturing of Metallic Materials for High Strain Rate Applications

    SBC: MRL MATERIALS RESOURCES LLC            Topic: MDA17T001

    Metallic additive manufacturing (AM) is an attractive technology for the production of lethality test articles due to the potential for significantly reduced lead time and manufacturing cost.However, in order to be effective in providing accurate lethality data, the properties of the AM material have to match closely the properties of conventionally manufactured alloys found in real threat targets ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  2. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: ARCTOS Technology Solutions, LLC            Topic: DLA18A001

    Universal Technology Corporation (UTC) has teamed with the University of Dayton Research Institute (UDRI), Stratonics, and Macy Consulting to demonstrate not only the transitionability into commercial systems, but also to develop the data analytics and monitoring and control requirements to extract the full value fromseveral sensors, including the Stratonics ThermaViz, acoustic and profilometry se ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  3. 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
  4. Advanced Rocket Trajectory Propagation Techniques

    SBC: NANOHMICS INC            Topic: MDA17T002

    High-fidelity trajectory propagators are fundamental to the simulation and analysis of launch vehicles, missiles, and satellites. Applications in fields ranging from missile threat analysis to flightpath optimization seek fast and accurate solutions to large numbers of trajectories in federated simulation environments. Due to their robustness, well-known properties, and straightforward implementat ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  5. AIMSMART: Adaptive, Interactive MDA Semantic Model-based Articulation

    SBC: CYCORP, INC.            Topic: MDA11T003

    We will develop automatic ontology-aligning software, leveraging the enormous existing Cyc AI system to drive the formulation and ranking (pro/con argumentation) of hypotheses about term-term relationships, especially where the relationship is not simple 1-to-1 correspondence. Multiple Cyc micro-theories (contexts) will be created, to hold incommensurate alternative mappings, and logical conseque ...

    STTR Phase II 2013 Department of DefenseMissile Defense Agency
  6. Brain Targeting Nanoparticle for Drug Delivery in Traumatic Brain Injury

    SBC: WEINBERG MEDICAL PHYSICS, INC.            Topic: DHA18A001

    There is an urgent need to bypass or cross the blood brain barrier (BBB) to deliver drugs for TBI treatment. This project aims to develop targted intranasally delivered, magnetically propulsed, and targted nanoparticles for traumatic brain injury (TBI). In Phase I, we will demonstrate the design, fabrication, and in vitro characterization of proposed drug laden magnetic nanoparticles as well as co ...

    STTR Phase I 2018 Department of DefenseDefense Health Agency
  7. CCA-SAFE: A Model-Assisted NDE Tool for Failure Analysis of Gold Contaminated Solder Joints

    SBC: Intelligent Automation, Inc.            Topic: MDA15T005

    Gold contamination in Circuit Card Assembly (CCA) solder joints leads to brittle intermetallic compounds (IMCs), which is one of the major factors in solder joint failure. The conventional rule of thumb considers 3 wt% of gold (Au) as a safety threshold, which is not always reliable due to varieties of package platforms, solder types, reflux settings, operational and environmental conditions, etc. ...

    STTR Phase I 2016 Department of DefenseMissile Defense Agency
  8. Combined RF/IR Data Correlation

    SBC: Technology Service Corporation            Topic: MDA12T001

    Aegis BMD 5.0 CU will expand and update the Baseline 9 MRBM and IRBM threat set, while Aegis BMD 5.1 will have capabilities against more sophisticated short to intermediate range ballistic missiles. An ability to discriminate this wider set of increasingly sophisticated threats is essential. Technology Service Corporation (TSC) and the Michigan Tech Research Institute (MTRI) propose to identify fe ...

    STTR Phase I 2013 Department of DefenseMissile Defense Agency
  9. Contamination-free, Ultra-rapid Reactive Chemical Mechanical Polishing (RCMP) of GaN substrates

    SBC: Sinmat Inc            Topic: MDA09T001

    Gallium Nitride (GaN) substrates are ideal materials for fabrication of high-power and high-frequency devices based on III-V materials. The current state-of-the-art Chemical Mechanical Polishing (CMP) methods are plagued by several challenges, including, surface charge affects due to surface contamination, and sub-surface damages, which can limit the quality of III-V devices. Furthermore, there is ...

    STTR Phase I 2010 Department of DefenseMissile Defense Agency
  10. Deep Machine learning for risk Analysis and Prediction (D-MAP) in supply chains

    SBC: Intelligent Automation, Inc.            Topic: MDA16T002

    Globalization and digitization have been driving the recent economic growth at the expense of raising the risk level in the supply chain related to fraud, security, and safety, while current practice of supply chain management and risk assessment is lagging far behind. Therefore, commercial industries and government agencies are seeking advanced supply chain risk assessment solutions, which can ef ...

    STTR Phase II 2018 Department of DefenseMissile Defense Agency
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