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Pragnay Nevatia on Muck Rack

Pragnay Nevatia

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Articles

Accurate SEM-EDS quantification, automation, and machine learning enable high-throughput compositional characterization of powders

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By Yuxing Fei, Bernardus Rendy, Nathan Szymanski, Gerbrand Ceder, Andrea Giunto, Pragnay Nevatia
| Nature Verified
Abstract Compositional characterization is essential for understanding and optimizing material performance. For powder-based materials underpinning many modern technologies, however, accurately and rapidly resolving the composition of individual constituent phases remains an unsolved challenge, slowing materials research and limiting autonomous laboratory platforms.
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