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involves the development of modeling tools for microstructure-sensitive materials characterization, including finite-element tools and crystal plasticity modeling, extensible to new classes of constitutive
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NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form
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to develop tools to enable more routine high level production of labeled proteins using these different expression platforms. key words Protein labeling; Protein expression; Stable isotopes; Eligibility
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developing new approaches and approximations to improve the predictive power of such calculations. This project will be carried out in close collaboration with experimentalists, providing close feedback and
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at few hPa. We are developing an alternative truly atmospheric pressure approach that can be applied to a broad class of samples and be implemented in conventional laboratory-based instruments, as
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Description A new capability has been developed where sheet metal can be multi-axially strained while stresses in various directions within the plane of the sheet are measured using x-ray diffraction
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function; (2) explore available algorithms for image post-processing to recover original information about the visual environment; (3) explore scientific applications of new imaging methods; and (4) develop
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sample preparation equipment (including cross-sections) and also an ability to fabricate prototype devices using electron lithography. The current topics of interest include the process development and
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. This research aims to develop state-of-the-art imaging techniques and determine cell states and phenotypes based on quantitative and SI-traceable chemical measurements, which will boost the measurement assurance
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the world. With this production system, we are looking to augment our ability to rapidly answer science questions using the aggregated data volume. Additionally, we seek to develop and deploy new autonomous