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the construction process and correlates to the durability and service life of the composites. The goal is to understand the interplay between structure-properties-performance within these systems
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designs. In addition to fabrication and characterization of these measurement tools, we also develop new readout schemes, signal and data processing, control systems, and biomimetic surfaces to improve
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Description Membrane proteins participate in virtually all interactions between cells and the surrounding media and play vital roles in all biological processes involving transport across cell membranes, charge
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lyle.levine@nist.gov 301.975.6032 Mark R. Stoudt mark.stoudt@nist.gov 301.975.6025 Description The extreme processing conditions of metal additive manufacturing create inhomogeneous materials that can include
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guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
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process control measurements, and utilizing appropriate statistical analyses to understand the assay results and their uncertainties. This process should lead to improvements in the comparability and
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infrastructure, and experimental automation to materials characterization (metrology) methods across all portions of the structure-processing-properties-performance relationship. Specific group research focus
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NIST only participates in the February and August reviews. Many industrial processes generate carbon dioxide as a by-product, which is released to the atmosphere and contributes to global warming
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complex chemical matrices and natural systems. This requires the complementary use of advanced inorganic and organic mass spectrometries (MS), implementing multi-use sample processing streams and developing
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, interfacial phenomena, and associated transduction processes, often at the nanoscale that allow detection. A wide variety of sensing principles (chemiresistive, capacitive, electrochemical, FET, mass loading