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, which require specific 3D conformations to function properly. Any abnormalities in their structures can result in serious health-related problems or drug efficacy losses. However, all current methods have
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NIST only participates in the February and August reviews. This project’s focus is to develop light-scattering nanoscopy methods for rapid, multi-attribute characterization of nanoparticles
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on developing methods, algorithms, data, and tools, to support autonomous experimentation as well as prediction of industry- and/or community-relevant material properties. Keywords Machine Learning; Artificial
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mass spectrometry; and advanced chemometric tools for the analysis, interpretation, and comparison of complex metabolomic data sets. https://www.nist.gov/programs-projects/metabolomics-quality-assurance
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. [1] D. S. La Mantia et al, Phys. Rev. Applied 23, 044037 (2025) https://doi.org/10.1103/PhysRevApplied.23.044037 Thermometry; Radiometry; Blackbody radiation; Thermal radiation; Quantum Metrology
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to meet these demands. NIST also researches methods to improve physical environmental measurements made to complement or validate space-based measurements. The NIST effort is aided by specialized facilities
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Description Research opportunities are available to develop and advance measurement methods required for current and future semiconductor manufacturing processes. Areas of particular interest include
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on a collaboration with experts across multiple Laboratories at NIST involving detector-response modelling, next-generation TES sensor design, and quantitative sample-preparation methods. key words
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spectrum. A theoretical approach must include automatic discovery of reactions and their rates. Some tandem methods (CID, IRMPD) may be modeled as occurring on the ground electronic state. Electron
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processing of tables, extraction of property data from plots, analysis of paper content and extraction of metadata (substances, description of their samples, experimental methods, uncertainties, etc