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RAP opportunity at National Institute of Standards and Technology NIST Machine Learning Driven Autonomous Metrology System Location Physical Measurement Laboratory, Sensor Science Division
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RAP opportunity at National Institute of Standards and Technology NIST Machine Learning for Autonomous Genetic Engineering of Microbial Systems Location Material Measurement Laboratory
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, and c) predicting new phenomena and discovering improved materials for applications. My efforts in this area use a variety of modeling approaches to answer questions on materials systems of interest
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, sensitivity, and temperature range of CoBRAS thermometers. A key goal is to compare a CoBRAS to a temperature transfer standard and demonstrate the ability to self-calibrate the sensor by sequentially exciting
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systems as well. Unique capabilities in our group include the ability to heat single biomolecules with IR laser light to study T dependent kinetics and thermodynamics relevant to evolution of thermophilic
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advance our ability to accurately predict increasingly complex burning scenarios (e.g., varied sample/product configuration and scale). Further details of the project are available online: https
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of linking atomic interactions to larger scale dynamic materials properties. A primary challenge with these investigations is that the predicted results are often extremely sensitive to the choice
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NIST only participates in the February and August reviews. Project Description:NIST is developing a novel neutron interferometric phase imaging method using a grating-based, far-field interferometer
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NIST only participates in the February and August reviews. Additive manufacturing (AM) is a rapidly growing technology, but its commercial adaptation to ceramic-based materials lags behind
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jason.widegren@nist.gov 303.497.5207 Description https://www.nist.gov/programs-projects/electric-acoustic-spectroscopy-intermolecular-interactions-solution#OnChip NIST’s Material Measurement Laboratory (MML) and