176 structures-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" positions at NIST in United States
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the structural changes of materials during indentation deformation. Recently, this methodology has been employed in in-situ studies on the phase transformation of crystalline and amorphous silicon thin films and
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temperature and velocity data to provide a complete picture of the physics and chemical structure of the environment within the fire enclosure. Measurements will be completed for a variety of fuel types and
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measure structural changes as the agents go from their biologically active to their biologically inactive forms. As analytical methods become available, studies of the physical and chemical processes
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-to illuminate the structural transformations that occur across phases. The optical characterization of biological molecules using vibrational spectroscopy supplies critical, detailed structural information
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models. The current application domain is the relationship between the macroscopic deformation behavior of structural and mechanical materials and the corresponding microstructural deformation, internal
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structure-property relationships for polymers has been largely limited due to the inability to systematically control polymer sequence especially under real-world conditions where process history
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on determination of structural, thermophysical, and transport properties of systems and materials. We also aim to enable materials design from the function-structure relationship standpoint, as applied to metal
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-angle x-ray and neutron scattering, and fluorescence correlation spectroscopy to understand the relationships between CART molecular structure (block ratio, charge fraction, degradability), polyanion, and
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are a fascinating subfamily of porous crystalline materials. The unique structural flexibility related to pore opening/closing makes them promising candidates for many gas storage and separation
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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