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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Machine Learning Methods for the Prediction and Correlation of Thermophysical Properties
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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Scientific machine learning methods for trustable accelerated materials characterization and
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been applied to cells, tissues, and biomaterials. Our interest in further development is two-fold: (1) Numerical processing and analysis methods for extraction of actionable information from the rich
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between methods. NIST has world-class facilities in neutron, magnetic, X-ray, and electron techniques to investigate these methods; compare between different methods; and provide improved techniques
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and traceable activity that can be used for calibration in advanced imaging methods. At NIST, advances in “drop-on-demand” inkjet technology are poised to meet this need. Advanced gravimetric techniques
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biomedical, communications, sensing devices, and quantum computing. Their dimensions range from a few to several hundred nanometers. There is special interest in color centers in nanodiamonds, which give
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and adhesion including nanoindentation, atomic force microscopy, high-throughput methods, in addition to a range of more traditional techniques to measure the mechanical properties and adhesion
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specific inter- and intra-moleculuar configurations and compositions to ensure clinical efficacy, targeted delivery, and patient safety. Because conventional analytical methods face nontrivial limitations
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ultraviolet photoemission, scanning probe microscopies, mass spectrometry, fluorescence and other optical methods, and local electronic transport measurements are used to monitor these surface and
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from plots, analysis of paper content and extraction of metadata (substances, description of their samples, experimental methods, uncertainties, etc.). This processing system can also be expanded to