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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. A Cost-effective, Large Scale and a Novel Synthetic Route for Quasi-2D “MXene� Phase

    SBC: Texas Biochemicals Incorporated            Topic: AF18BT017

    MAX phases are an important class of layered machinable ternary carbides and nitrides that exhibit a unique combination of ceramic and metal properties. In 2011, a new family of two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides – called MXenes – was discovered via selective etching of the ‘A’ element from MAX phases. Texas Biochemicals Inc., has ...

    STTR Phase I 2019 Department of DefenseAir Force
  2. Activated Reactants to Reduce Fuel Cell Overpotentials

    SBC: JSJ Technologies, LLC            Topic: A10AT011

    The current produced in electrochemical galvanic cells is primarily dependent on the rate of the electrode reactions where the cell's anode is less negative, supplying less energy than thermodynamically predicted, and the cell's cathode is less positive, supplying less energy than thermodynamically predicted. Reduction of electrochemical overpotentials in electrochemical systems has been the prim ...

    STTR Phase I 2010 Department of DefenseArmy
  3. Adaptive multi-sensor wide area situational awareness system- MP 85-12

    SBC: Metron, Incorporated            Topic: AF12BT14

    ABSTRACT: Existing machine learning algorithms have difficulty using all available data about a problem. This STTR will develop a new algorithm that can make full use of all available data, whether that data is labeled or not, and even when some data types or data resolutions are not available during operation. BENEFIT: This STTR will develop a novel machine learning algorithm for reasoning abo ...

    STTR Phase I 2013 Department of DefenseAir Force
  4. Adaptive Turbine Engine Control for Stall Threat Identification and Avoidance

    SBC: AURORA FLIGHT SCIENCES CORPORATION            Topic: N10AT008

    Aurora Flight Sciences and MIT propose to develop a model-based adaptive health estimation and real-time proactive control to identify gas turbine engine stability risks and avoid them through control action. In this concept, the engine control system actively monitors sensors and actuators, compares them against physical models, and infers which components may be performing poorly and may need to ...

    STTR Phase I 2010 Department of DefenseNavy
  5. Additive Manufacturing for Naval Aviation Battery Applications

    SBC: Texas Research Institute, Austin, Inc.            Topic: N18AT008

    Texas Research Austin (TRI-Austin) will partner with the University of Texas, Austin, and will use diverse printing technologies to fabricate the components of selected battery chemistries (Li-ion, Zn-air, Zn-Ag). In the Phase I base period, each battery component will be printed with a technology that has been previously used to deposit the required material (i.e. ADM for metals, SLS for polymers ...

    STTR Phase I 2018 Department of DefenseNavy
  6. 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
  7. Additive Manufacturing of Multifunctional Nanocomposites

    SBC: Sciperio, Inc.            Topic: A13AT010

    Sciperio with team members Georgia Institute of Technology and Centecorp have teamed up to develop an Additive Manufacturing Composite using nano and micro fillers. The team will develop multi-scale models that are supported by experimental characterization for advanced 3D Printable materials. Inelastic response of high strength hierarchical structures composed of engineered materials and specif ...

    STTR Phase I 2013 Department of DefenseArmy
  8. Additive Manufacturing of 17-4 PH Stainless Steel Metal Matrix Composites using Nickel functionalized Carbon Nanotubes

    SBC: Shepra, Inc.            Topic: N16AT007

    Additive Manufacturing (AM) has a potential to significantly reduce the cost and lead time associated with the maintenance and sustainment issues faced by the US Navy. However, current materials such as 17-4 PH Stainless Steel typically achieve half the required mechanical properties when additively manufactured, thus limiting the use of AM in critical parts. Recent advancements in carbon nanotube ...

    STTR Phase I 2016 Department of DefenseNavy
  9. 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
  10. ADP: Autonomous Deep Perception

    SBC: Lynntech Inc.            Topic: N13AT016

    Autonomous systems acquire massive amounts of sensor and communications data over the course of their potentially lengthy missions. Ideally, such systems would incorporate current and historical data into their decision making processes to generalize from experience and avoid repetitive errors. However, the sheer quantity of data gathered can make storage and processing of an unfiltered data strea ...

    STTR Phase I 2013 Department of DefenseNavy
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