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1 August 2021 Hard x-ray nano-XANES and implementation deep learning tools for multi-modal chemical imaging
Ajith Pattammattel, Ryan Tappero, Yong S. Chu, Mingyuan Ge, Dmitri Gavrilov, Xiaojing Huang, Hanfei Yan
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Proceedings Volume 11839, X-Ray Nanoimaging: Instruments and Methods V; 118390J (2021) https://doi.org/10.1117/12.2599526
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States
Abstract
Spectromicroscopy techniques allow the study of local chemical states along with morphology information. At the hard X-ray nanoprobe (HXN) beamline at NSLS-II, we developed nanoscale chemical imaging with high chemical state sensitivity and micron-scale penetration depth. In addition to the chemical images, XRF and phase-contrast images collected simultaneously offer multi-modal, correlative image analysis. We also developed a highly interactive, python-based graphical user interface (NSLS-II MIDAS) that allows multi-modal analysis of nano-XANES and XRF images. Advanced supervised and unsupervised learning algorithms enable users to explore the traditional XANES analysis along with standard machine-learning tools
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Ajith Pattammattel, Ryan Tappero, Yong S. Chu, Mingyuan Ge, Dmitri Gavrilov, Xiaojing Huang, and Hanfei Yan "Hard x-ray nano-XANES and implementation deep learning tools for multi-modal chemical imaging", Proc. SPIE 11839, X-Ray Nanoimaging: Instruments and Methods V, 118390J (1 August 2021); https://doi.org/10.1117/12.2599526
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KEYWORDS
Hard x-rays
Imaging spectroscopy
Absorption
Data modeling
Spectroscopy
Systems modeling
X-rays
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