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NIST only participates in the February and August reviews. This suite of projects seeks to advance the microbial metabolomics infrastructure through the development of new analytical methods
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Laboratory. doi: https://doi.org/10.6028/NIST.TN.2178 . building control; intelligent agents; optimization; data analytics; machine learning; data-driven models; HVAC
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developing the measurement infrastructure to acquire fundamental property data related to the capture and release of difficult to detect drugs or drug metabolites. We will then design, develop, and
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identification of spectral features by computer vision and machine learning. Our computational methods development has three primary goals. The first goal is continued support of expert-driven biomolecular
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Leveraging Large Language Models, Recommendation Systems, and Interpretable Deep Learning for Firefighter Safety and Decision Support NIST only participates in the February and August reviews
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ionization MS, and/or related approaches) and complementary methods for the characterization of micro- and nanoplastics (MNPs). Analytical approaches for the measurement of MNPs from complex matrices will take
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chemical imaging of pharmaceutical solid dosage forms with broadband CARS microscopy.” Analytical Chemistry 85: 8102-8111, 2013 Raman spectroscopy; CARS; Spectroscopy; Chemical imaging; Bioimaging
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alloys, carbon-based composites, and solid-state-biomolecule hybrid structures. Our data-driven development uses cheminformatics methodologies combined with machine learning methods to produce predictive
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synthesis and the development of complex fluids is important to a number of industrial applications. Many diagnostic assays use the temperature dependence of different analytes as a diagnostic tool. For