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NIST only participates in the February and August reviews. Manufacturing optimized devices that incorporate newly-emerging materials requires predictable performance throughout device lifetimes
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aptamer conformation changes. Method development efforts should focus on the incorporation of a robust and optimized experimental design aimed at assessing the sources of variability, repeatability, and
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. Developing advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data and phase transformation kinetics assessment. Applying the insights gained to optimize AM
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Description Mathematical modeling forms the basis for understanding, simulating, optimizing and controlling numerous scientific phenomenon and associated measurements. Mathematical models frequently take the
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impediments to meeting the desired manufacturing and performance standards. Digital twins (DT) are being adopted in the AM industry to optimize the entire manufacturing process and enable products with high
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to be used by every machine tool to enable optimized production of assets within manufacturing facilities. Augmented intelligence, which is the augmentation of traditional scientific intelligence with
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our understanding of the fundamental limitations of detectors and sources; development of new ways to package detectors, sources, and components optimized for few photon operation; and developing new
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proposals to develop, optimize and deploy a headspace collection method to measure partition coefficients at physiological temperatures. We are especially interested in methods that target molecules
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of novel optical methods for nanoscale dimensional measurements using the NIST 193 nm Microscope: a newly upgraded, custom-built, world-class high-magnification optical imaging platform optimized
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and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and