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The Award database is continually updated throughout the year. As a result, data for FY19 is not expected to be complete until April, 2020.

  1. Phytoextractionand Recycling of Arsenic From CCA-Contaminated Soils

    SBC: Edenspace Systems Corporation            Topic: N/A

    More than 70 percent of U.S. arsenic consumption¿representing approximately 37 million pounds of arsenic per year¿is used to produce chromated copper arsenate (CCA), a wood preservative. Weathered lumber in decks, docks, playground equipment, and garden construction can leach significant amounts of arsenic into soil and water, where it poses health risks to humans and animals. Effective Decemb ...

    SBIR Phase I 2003 Environmental Protection Agency
  2. Nanocomposite-Based Filter for Arsenic Removal in Drinking Water

    SBC: Materials Modifications Inc.            Topic: N/A

    To comply with the U.S. Environmental Protection Agency¿s (EPA) directive of 10 ppb levels for arsenic in drinking water, there is an urgent need to develop new, simple, and effective treatment systems that can be qualified as best available technologies (BAT) to remove this toxic substance from water. Towards this goal, Materials Modification, Inc. (MMI) proposes to develop a novel arsenic-remo ...

    SBIR Phase I 2003 Environmental Protection Agency
  3. Heterdyne Interferometer for Triggering Gas Puff PRSs

    SBC: HY-Tech Research Corp.            Topic: N/A

    Large X-ray generators using gas fill loads are a key element in the DTRA simulator program. Consistent relative timing between the injection of the gas shell and the generator current pulse is crucial to producing consistent, high-yield x-ray pulses.To that end HY-Tech proposes replacing existing high voltage trigger pin technology, with a sensitive, all fiber-optic heterodyne interferometer ba ...

    SBIR Phase I 2003 Department of DefenseDefense Threat Reduction Agency
  4. Automated pattern recognition methods to identify nuclear explosions

    SBC: Acorn Science & Innovation, Inc.            Topic: DTRA182005

    Reliable automated pattern recognition in the current system is limited by excessive noise and clutter combined with insufficient and/or ambiguous features. Our team consisting of AcornSI and Leidos thus propose a multi-step approach based on the combined effects of improved detection, feature extraction, phase identification, and global association.A key innovation here is creating a new classifi ...

    SBIR Phase I 2019 Department of DefenseDefense Threat Reduction Agency

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