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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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An Effective and Durable Icephobic Coating for Turbomachinery Inlet Components
SBC: CREARE LLC Topic: N182115Creare proposes to develop novel icephobic coatings for F-35 turbomachinery inlet components. Our baseline approach explores the use of two easily applied silicone-based coating technologies specifically engineered for challenging lift fan and engine compressor geometries. Our integration leverages Creare’s decades of advances in turbomachinery development with the advanced aerospace coating exp ...
SBIR Phase I 2019 Department of DefenseNavy -
EROSION RESISTANT HYBRIDSIL® ICEPHOBIC COATINGS FOR AIRCRAFT PROPULSION TURBOMACHINERY INLET COMPONENTS
SBC: NANOSONIC INC. Topic: N182115Through the proposed Navy SBIR program, NanoSonic will molecularly engineer and empirically optimize its HybridSil Icephobic coating technology for imparting long-term passive ice protection to aircraft propulsion turbomachinery inlet components. Unlike competing icephobic coating technologies that rely on unreacted, surface enriching / lubricating oils that impart temporary ice protection, NanoSo ...
SBIR Phase I 2019 Department of DefenseNavy -
Minimization of In-Band Interferers on Airborne Anti-Submarine System Performance
SBC: IN-DEPTH ENGINEERING CORPORATION Topic: N182116Coordinated Anti-Submarine Warfare (ASW) tactical operations frequently hinge upon the ability of our airborne assets to detect, classify, localize and prosecute enemy submarines who possess formidable offensive and defensive capabilities. This is becoming increasingly difficult as enemy assets get quieter and the acoustic environment becomes increasingly crowded. The Navy’s airborne ASW passive ...
SBIR Phase I 2019 Department of DefenseNavy -
Multifunction-Multimodal Airborne Radar in Maritime/Littoral Environments
SBC: AZURE SUMMIT TECHNOLOGY, INC. Topic: N182120Airborne surveillance radars continue to advance towards more flexible architectures that enable them to address evolving adversary tactics facilitated by global proliferation of RF and High Performance Embedded Computing (HPEC) technologies. Aided by these technologies, the adversary can use multiple electronic counter measures that can confuse traditional monostatic radars and obscure the true o ...
SBIR Phase I 2019 Department of DefenseNavy -
Fleet Material Locator Information System (FMLIS)
SBC: Premier Solutions HI, LLC Topic: N182122In order to support operational objectives for readiness across the vast PACOM area of responsibility, Commander Pacific Fleet (COMPACFLT) requires improved visibility into the availability of parts and other material. While a wide range of logistics systems from multiple DoD agencies provide data on material from supplier to on-hand inventory, critical gaps exist in tracking material while in tra ...
SBIR Phase I 2019 Department of DefenseNavy -
Clearinghouse for Subsistence Ordering & Receipt (CSOR)
SBC: Premier Solutions HI, LLC Topic: N182123The Navy’s food logistics operations rival the largest commercial enterprises in scale and complexity, but the Navy has not been able to take advantage modern food service management systems. Current Navy processes are plagued by out-of-date vendor catalogs, manual/duplicate data entry, man-in-the-loop delays, manual data transfers, and non-compliant processes and systems. Business-to-Business ( ...
SBIR Phase I 2019 Department of DefenseNavy -
Seamless Knitting for Military Protective Clothing
SBC: PROTECTIVE TECHNOLOGIES SERVICES, INC Topic: N182124Recent developments in machinery and flame-resistant (FR) yarn blends provide an opportunity to improve Sailor comfort, reduce heat stress, and maintain protection while reducing manufacturing lead-times, costs, and waste by utilizing novel seamless knitting technologies. The proposed effort will evaluate the feasibility of using 3D knitting with FR yarns and analyze the strengths and weaknesses o ...
SBIR Phase I 2019 Department of DefenseNavy -
Effect of Surface Finish and Post-Processing on the Fatigue Life of Additively Manufacturing Parts
SBC: TECHNICAL DATA ANALYSIS, INC. Topic: N182126Additive manufacturing technology is becoming more popular for the fabrication of 3D metal products as it offers rapid prototyping and large design freedom. However, with more complex geometric features due to topology optimization, it becomes infeasible to carry out traditional surface machining process to improve surface roughness for fatigue performance. In this effort, we will develop a novel ...
SBIR Phase I 2019 Department of DefenseNavy -
Fooling Computer Vision Classifiers with Adversarial Examples
SBC: LYNNTECH INC. Topic: N182127The Lynntech team proposes to develop a Computer Vision FoolKit system that integrates cutting-edge approaches to systematically evaluate physically realizable adversarial attacks against several leading computer vision classifiers. It has been noted that most deep neural networks are demonstrably vulnerable to adversarial examples, even in the form of small-magnitude changes in intensities of the ...
SBIR Phase I 2019 Department of DefenseNavy -
Computer Learning Obfuscating Adversarial Kit (CLOAK)
SBC: DECISIVE ANALYTICS CORPORATION Topic: N182127In 2013, Szegedy et al. identified a series of “intriguing properties” of neural networks. One property introduced in the paper, “adversarial examples”, describes the possibility of instability in neural network classification when a small perturbation is added to input. Specifically, the authors found that a small strategic perturbation to an input image could cause a classifier to miscla ...
SBIR Phase I 2019 Department of DefenseNavy