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critically on the availability of cryogenics that are user-friendly, compact, and power efficient. As part of the cryogenic detector development effort, NIST also develops the requisite cryogenic systems
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machine learning systems needed for accelerated science. This research opportunity focuses on developing, evaluating, and applying computational methods for materials characterization and/or
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Systems Division opportunity location 50.77.51.C0802 Gaithersburg, MD NIST only participates in the February and August reviews. Advisers name email phone Peter Bajcsy [email protected]
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complicated systems such as post-consumer resins, property changes from physical or chemical processing, the addition of other measurement modalities and/or using more sophisticated machine learning techniques
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approaches involving surfactant-based systems exploiting the spontaneously self assembling properties of lipids have recently attracted considerable attention due to some promising initial results using the
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characterization of advanced drug products, specifically targeting therapeutic proteins , nucleic acids ( RNA and DNA ), and complex gene delivery systems . These next-generation therapeutics require highly
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equilibrium, fugacity and activity coefficients, virial coefficients to describe vapor nonideality, transport properties, dielectric constant, and refractive index. Systems of interest include water
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type. Various optical methods exist to record these vibrational spectra, enabling access to the rich chemical landscape within materials, cells, cell systems, and tissues—without the addition of
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feedback for the design of entirely new materials. We currently apply these broadband measurement techiques to study thin-film material systems such as dielectrics, ferroelectrics, multiferroics
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experimental and theoretical studies on model sample systems consisting of crystalline samples, ordered ultrathin films, or appropriately assembled biomolecules or nanostructures (including aptamers