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DOE OSTI · 2523648

Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning

Gleason, Samuel P.·Carbone, Matthew R.·Lu, Deyu·Ciston, Jim
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Abstract

Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about distributions and locations of atoms, their coordination numbers and oxidation states, and the bonding characteristics [1]. However, analysis of XAS/EELS data often relies on matching the spectra of an unknown experimental sample to a series of simulated or experimental spectra of standard samples. Here, this limits analysis throughput and the ability to extract quantitative information from a sample.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRYCuEELSMachine LearningSpectral AnalysisXAS

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Gleason, Samuel P., Carbone, Matthew R., Lu, Deyu, Ciston, Jim. 2024-07-24. Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning. https://doi.org/10.1093/mam%2Fozae044.1014

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