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The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.

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. Operational Sand and Particulate Sensor System for Aircraft Gas Turbine Engines

    SBC: HAL Technology, LLC            Topic: N18AT023

    Gas turbine engines with prolonged exposure to sand and dust are susceptible to component and performance degradation and ultimately engine failure. Our proposed sensor will use an innovative hybrid and complimentary discrimination approach to incorporate material identification along with capability of size, size distributions, and concentration while maintaining the same form factor of the curre ...

    STTR Phase II 2021 Department of DefenseNavy
  2. Integrated Computational Materials Engineering (ICME) Modeling Tool for Optimum Gas Flow in Metal Additive Manufacturing Processes

    SBC: TECHNICAL DATA ANALYSIS, INC.            Topic: N21BT022

    Users of additive manufacturing machines expect the highest quality when it comes to the mechanical properties of parts, the usability of the machines and associated processes, and overall machine design. The main contributor to the quality of the part is the involvement of process-related by-products originating from the melting process. To handle these by-products in additive manufacturing - in ...

    STTR Phase I 2021 Department of DefenseNavy
  3. Quantum Optical Semiconductor Chip and Its Application to Quantum Communication

    SBC: MAXXEN GROUP LLC            Topic: N20AT005

    Current Superconducting Quantum Interference Device (SQUID) technology is capable of quantum computing, however, its application is limited due to its large size, low-temperature refrigeration requirement, and high cost.  Quantum optical semiconductor-scale chip technology is promising but not commercially available yet due to the multiple challenges to overcome. Using quantum photonic technology ...

    STTR Phase II 2021 Department of DefenseNavy
  4. Tunable interferometer in SiN

    SBC: NEXUS PHOTONICS INC            Topic: N21BT019

    We propose to develop tunable, high-power handling, low-loss phase-to-amplitude conversion photonic-integrated circuit (PIC) based device on heterogeneous SiN platform. The use of SiN for waveguide is the key to meet the stringent requirements, while additional heterogeneously integrated components are the key to enable tuning and locking of the phase-to-amplitude device. Nexus Photonics, realizi ...

    STTR Phase I 2021 Department of DefenseNavy
  5. Tunable wideband optical phase to amplitude conversion in a monolithically assembled network

    SBC: FIBERTEK, INC.            Topic: N21BT019

    We propose a serially concatenated switchable asymmetric Mach Zehnder switching network on a silicon nitride photonic integrated circuit platform to provide flexible and switchable demodulation of signals from less than 1 GHz to greater than 45 GHz. The silicon nitride platform provides a low size weight and power solution while reducing the stabilization requirements and providing long path lengt ...

    STTR Phase I 2021 Department of DefenseNavy
  6. Hemp-based Fabric for Military Uniforms

    SBC: TDA RESEARCH, INC.            Topic: N21AT001

    Hemp fibers have been used in textile applications for thousands of years, but have been limited to textiles for home furnishing, rope, sacks, and other non-clothing textile applications. However, new treatment methods for hemp fibers to improve their hand, as well hemp’s general softening properties with time and wear, make these fibers attractive for use in performance clothing due to hemp’s ...

    STTR Phase I 2021 Department of DefenseNavy
  7. Hatchable Solid State Transformer Rectifier Design through System Co-Optimization

    SBC: HEPBURN AND SONS LLC            Topic: N21BT020

    Hepburn and Sons LLC teamed with North Carolina State University (NCSU) proposes an analysis of alternatives (AoA) to design and optimize a megawatt class, solid state transformer rectifier (SSTR) supporting maintainability of U.S. Navy shipboard power infrastructure. The design will convert 13.8kV AC to +-850V DC with modular, line-replaceable units (LRUs) that are compact and lightweight such th ...

    STTR Phase I 2021 Department of DefenseNavy
  8. Ground Fault Detection and Localization for Low Voltage Alternating Current (LVAC) Shipboard Distribution Systems

    SBC: HEPBURN AND SONS LLC            Topic: N21AT005

    Hepburn and Sons LLC proposes to design a ground fault detection system for 440VAC three phase radial Navy shipboard power distribution, while teaming with their partner Florida State University, Center for Advanced Power Systems (FSU CAPS). Existing Navy ground fault detection systems can detect and locate single line to ground faults on a given phase, but a second ground fault on a separate phas ...

    STTR Phase I 2021 Department of DefenseNavy
  9. Analysis and Modeling of Erosion in Gas-Turbine Grade Ceramic Matrix Composites (CMCs)

    SBC: ALPHASTAR TECHNOLOGY SOLUTIONS LLC            Topic: N19BT033

    A significant barrier to the insertion of ceramic matrix composite (CMC) materials into advanced aircraft engines is their inherent degradation under erosion and post erosion. Our team will develop and demonstrate a physics-based model for erosion/post erosion of CMC’s at room and elevated temperatures (RT/ET). The ICME (Integrated Computational Material Engineering) Physics based Multi Scale Mo ...

    STTR Phase II 2021 Department of DefenseNavy
  10. Timing-Harmonic Adaptive Waveform Estimation using Deep learning (THAWED)

    SBC: EXPEDITION TECHNOLOGY, INC.            Topic: N20AT025

    The physics generating timing spurs in a high-speed, low-bit depth analog-to-digital converter will be modeled in a machine learning framework to enable both the prediction of spurs and adaptive removal using a linearization neural network.  High-speed implementation of the spur estimation and removal networks will be accomplished.

    STTR Phase II 2021 Department of DefenseNavy
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