Sulfur Kβ X-ray emission spectroscopy: comparison with sulfur K-edge X-ray absorption spectroscopy for speciation of organosulfur compounds

Abstract

Sulfur Kβ non-resonant X-ray emission spectroscopy complements sulfur K-edge X-ray absorption spectroscopy in providing information on chemical speciation and electronic structure.

Combined first-principles statistical mechanics approach to sulfur structure in organic cathode hosts for polymer based lithium–sulfur (Li–S) batteries

“…An interesting aspect to investigate in future studies would be the comparison of the experimental Raman spectra to the calculated frequencies of the most representative structure. 72,73 Furthermore, this study is the first step into a multi-scale approach bridging classical and ab initio molecular dynamics simulations that will focus on larger solvated polymer structures 74,75 and spectroscopy calculations, [76][77][78] respectively.…”

Unsupervised Machine Learning for Unbiased Chemical Classification in X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy

“…Then a linear increase in the FWHM broadening was applied, starting from the whiteline at 0.6 eV and increasing to 4.0 eV FWHM at 15 eV past the whiteline. This scheme accurately depicts features from experimental data 59,83 . Finally, the spectra were individually normalized by dividing their total Kα intensities and an energy shift of -53.3 eV was applied to all XANES transitions to align the theoretically calculated transitions with experiment.…”

“…al 22 , which we believe is the first application of unsupervised ML in XAS, we fully examine clustering in this reduced dimensional space for unbiased discovery of chemical classes and thus the extent of encoded information in spectra. As a secondary consequence of our choice to investigate both XANES and VtC-XES, we are also able to test the common qualitative assertion that the methods are "complementary" because of their respective sensitivity to unoccupied and occupied electronic states 57 , here quantitatively addressing whether the chemically relevant information in XANES and VtC-XES is indeed complementary or is instead highly coincident [58][59][60] .…”

“…(1) their rich diversity of bonding environments; (2) the considerable evidence for sensitivity of both XANES and VtC-XES of the S K-edge to chemical bonding in this family 59,64,65 ; and (3) the prior demonstration of good agreement between experiment and time-dependent density functional theory (TD-DFT) 65 calculation of XANES 66 and VtC-XES 65,[67][68][69] .…”

Unsupervised Machine Learning for Unbiased Chemical Classification in X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy

“…Another observation in the t-SNE VtC-XES that was not present in PCA or VAE results is that the blue Type 2 group by the yellow Type 5 cluster consists of isothiocynates, which are distinct from the other Type 2 thioketones. Some sensitivity to aromaticity could have been expected (although whether it would be seen in just a two-dimensional representation was definitely uncertain), given the prior work by Yasuda and Kakiyama 64 on VtC-XES and by Qureshi et al 59 on XANES. Here, because t-SNE is unbiased, we can explore clustering in more detail to look for unexpected chemical classifications, an issue that we explore in Fig.…”