1. Probing Sulfur Chemical and Electronic Structure with Experimental Observation and Quantitative Theoretical Prediction of Kα and Valence-to-Core Kβ X-ray Emission Spectroscopy
- Author
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Gerald T. Seidler, Evan P. Jahrman, William M. Holden, and Niranjan Govind
- Subjects
Valence (chemistry) ,010304 chemical physics ,Chemistry ,chemistry.chemical_element ,Electronic structure ,010402 general chemistry ,01 natural sciences ,Sulfur ,0104 chemical sciences ,0103 physical sciences ,Emission spectrum ,Physical and Theoretical Chemistry ,Atomic physics ,X Ray Emission Spectroscopy - Abstract
An extensive experimental and theoretical study of the Kα and Kβ high-resolution X-ray emission spectroscopy (XES) of sulfur-bearing systems is presented. This study encompasses a wide range of organic and inorganic compounds, including numerous experimental spectra from both prior published work and new measurements. Employing a linear-response time-dependent density functional theory (LR-TDDFT) approach, strong quantitative agreement is found in the calculation of energy shifts of the core-to-core Kα as well as the full range of spectral features in the valence-to-core Kβ spectrum. The ability to accurately calculate the sulfur Kα energy shift supports the use of sulfur Kα XES as a bulk-sensitive tool for assessing sulfur speciation. The fine structure of the sulfur Kβ spectrum, in conjunction with the theoretical results, is shown to be sensitive to the local electronic structure including effects of symmetry, ligand type and number, and, in the case of organosulfur compounds, to the nature of the bonded organic moiety. This agreement between theory and experiment, augmented by the potential for high-access XES measurements with the latest generation of laboratory-based spectrometers, demonstrates the possibility of broad analytical use of XES for sulfur and nearby third-row elements. The effective solution of the forward problem, i.e., successful prediction of detailed spectra from known molecular structure, also suggests future use of supervised machine learning approaches to experimental inference, as has seen recent interest for interpretation of X-ray absorption near-edge structure (XANES).
- Published
- 2020
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