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Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
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spectral filters and superconducting detectors on a single chip will enable dramatic new functionality and scalability. We are developing these systems utilizing silicon photonic devices with superconducting
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prediction. In collaboration with NASA, NOAA, and the USGS, NIST develops technology to advance the calibration and characterization of ground- and space-based infrared, optical, and temperature sensors
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thin films. Complementary techniques based on scanned probes, such as electrostatic force microscopy for surface potential measurements and photovoltage measurements, are also being developed. key words
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.B1995 Gaithersburg, MD NIST only participates in the February and August reviews. Advisers name email phone Hui Wu huiwu@nist.gov 301 975 2387 Description We develop and study new materials for carbon
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Obrzut jan.obrzut@nist.gov 301.975.6845 Christopher L. Soles christopher.soles@nist.gov 301.975.8087 Description This research is focused on the development and use of cellulose nanocrystals (CNC) in
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that emit charged particles upon neutron capture. Research topics include method development, focusing on improved specificity, accuracy, sensitivity, and spatial resolution through detailed studies
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to this information offers unique possibilities for the development and validation of the next generation of models of molecular interface formation on multiple scales, ranging from molecular dynamics simulations
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on gas storage and separation in these materials, but also help us to rationally develop the next generation of flexible materials. References H. Yang, et al. "Visualizing structural transformation and
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@nist.gov 843.442.2188 Description The Chemical Sciences Division has a developing program to improve the quality of analytical chemical measurements in the field of marine debris research. Research