83 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at NIST
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to effectively convert interactions occurring at gas-solid or liquid-solid interfaces, or between molecules, into measurable signals that provide information on the nature and concentration of chemical and
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and are transforming biosciences and biotechnology. Yet, the translation of technologies requires greater measurement confidence underpinned by SRMs. Data produced by the NIST-led Genome in a Bottle
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in popularity of density functional theory (DFT) has created an opportunity for critical evaluation of the performance of DFT methods versus accurate quantum and experimental data. Quantities such as
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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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their enterprises. We are interested in projects that focus on the development and application of performance metrics, information models, test methods, and protocols to assess and assure the key attributes
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of the uncertainties encountered in the physical experiments. These computational methods have been applied to nanorheology in photopolymerization 3D printing [1], human breath research for forensic and clinical data
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NIST only participates in the February and August reviews. Demand for mobile data, the implementation of new wireless devices, and an explosion of mobile users has stressed our telecommunications
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of fit for purpose high-quality reference materials that can be used to help normalize and benchmark data from the various EV isolation and characterization methods is a key bottle-neck inhibiting
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of protein therapeutics, because NMR spectra are sensitive to molecular shape and intermolecular interactions as well as chemical structure, and NMR can reproducibly probe this information at atomic resolution
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activity of individual cells over time. We have shown that such data can provide information about fluctuations in promoter activity and can be used to predict rates of state change in cell populations