Highly sensitive 2D X-ray absorption spectroscopy via physics informed machine learning

Simple item page
dc.citation.articleNumber128en_US
dc.citation.journalTitlenpj Computational Materialsen_US
dc.citation.volumeNumber10en_US
dc.contributor.authorLi, Zeyuanen_US
dc.contributor.authorFlynn, Thomasen_US
dc.contributor.authorLiu, Tongchaoen_US
dc.contributor.authorLiu, Sizhanen_US
dc.contributor.authorLee, Wah-Keaten_US
dc.contributor.authorTang, Mingen_US
dc.contributor.authorGe, Mingyuanen_US
dc.date.accessioned2024-08-09T16:25:26Zen_US
dc.date.available2024-08-09T16:25:26Zen_US
dc.date.issued2024en_US
dc.description.abstractImproving the spatial and spectral resolution of 2D X-ray near-edge absorption structure (XANES) has been a decade-long pursuit to probe local chemical reactions at the nanoscale. However, the poor signal-to-noise ratio in the measured images poses significant challenges in quantitative analysis, especially when the element of interest is at a low concentration. In this work, we developed a post-imaging processing method using deep neural network to reliably improve the signal-to-noise ratio in the XANES images. The proposed neural network model could be trained to adapt to new datasets by incorporating the physical features inherent in the latent space of the XANES images and self-supervised to detect new features in the images and achieve self-consistency. Two examples are presented in this work to illustrate the model’s robustness in determining the valence states of Ni and Co in the LiNixMnyCo1-x-yO2 systems with high confidence.en_US
dc.identifier.citationLi, Z., Flynn, T., Liu, T., Liu, S., Lee, W.-K., Tang, M., & Ge, M. (2024). Highly sensitive 2D X-ray absorption spectroscopy via physics informed machine learning. Npj Computational Materials, 10(1), 1–9. https://doi.org/10.1038/s41524-024-01313-7en_US
dc.identifier.digitals41524-024-01313-7en_US
dc.identifier.doihttps://doi.org/10.1038/s41524-024-01313-7en_US
dc.identifier.urihttps://hdl.handle.net/1911/117643en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleHighly sensitive 2D X-ray absorption spectroscopy via physics informed machine learningen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
s41524-024-01313-7.pdf
Size:
2.4 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications
Materials Science and NanoEngineering Publications