2022
DOI: 10.1093/mtomcs/mfac078
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Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems

Abstract: Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based x-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multi-dimensional (4D+) structure of spectromicroscopy data poses visualization and data reduction chal… Show more

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Cited by 5 publications
(4 citation statements)
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References 39 publications
(48 reference statements)
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“…1, where background spectra are filtered out such that only the sample region is examined. This sampleand thus dataset is the same as the one found in Pattammattel, et al 17 Each "pixel" (150 nm wide) represents a processed XANES spectrum. Each pixel is 150 nm.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…1, where background spectra are filtered out such that only the sample region is examined. This sampleand thus dataset is the same as the one found in Pattammattel, et al 17 Each "pixel" (150 nm wide) represents a processed XANES spectrum. Each pixel is 150 nm.…”
Section: Resultsmentioning
confidence: 99%
“…12 We measured Fe K-edge XANES using nano-XANES imaging, where our data consist of a 3D image with 155 x 160 spatial pixels and just over 70 photon energies between 7.08 to 7.20 keV, or approximately 25,000 spectra. Further processing of the stack was performed via the XMIDAS program 17 . The energy stack was first spatially aligned using the image registration tool in XMIDAS that uses the PyStackReg package 18 .…”
Section: Experimental Methodsmentioning
confidence: 99%
“…The sample and experimental data is the same as it appeared in A. Pattammattel, et al 25 and S. Tetef, et al 26 See those works for the experimental details. Briey, the sample was composed of stainless steel (SS), lithium iron phosphate (LFP), pyrite (Pyr), and hematite (Hem) nanoparticles.…”
Section: Methodsmentioning
confidence: 99%
“…Inspired by the work in the literature, we show how uncertainty-aware neural network classifiers can be used to predict 41 different chemical motifs at very high accuracy using multiple simulated XANES signals as input. Multimodal data should, in principle, be of more value than the sum of its parts to ML models and are already being employed to great effect in many domains, including the study of XANES spectra. The ML classifiers we develop can have important applications in environmental chemistry, paleontology, organic geochemistry, and space science . We also evaluate the effectiveness of the uncertainty quantification (UQ) algorithms and demonstrate a “multimodal advantage”: the accuracy of the classifiers is significantly higher when using multiple XANES signals from different absorbing elements when compared to that when using only single modalities independently.…”
Section: Introductionmentioning
confidence: 99%