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Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials

The origins of performance degradation in batteries can be traced to atomistic phenomena, accumulated at mesoscale dimensions, and compounded up to the level of electrode architectures. Hyperspectral X-ray spectromicroscopy techniques allow for the mapping of compositional variations, and phase sepa...

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Autores principales: Santos, David A., Andrews, Justin L., Lin, Binbin, De Jesus, Luis R., Luo, Yuting, Pas, Savannah, Gross, Michelle A., Carillo, Luis, Stein, Peter, Ding, Yu, Xu, Bai-Xiang, Banerjee, Sarbajit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768684/
https://www.ncbi.nlm.nih.gov/pubmed/36569543
http://dx.doi.org/10.1016/j.patter.2022.100634
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author Santos, David A.
Andrews, Justin L.
Lin, Binbin
De Jesus, Luis R.
Luo, Yuting
Pas, Savannah
Gross, Michelle A.
Carillo, Luis
Stein, Peter
Ding, Yu
Xu, Bai-Xiang
Banerjee, Sarbajit
author_facet Santos, David A.
Andrews, Justin L.
Lin, Binbin
De Jesus, Luis R.
Luo, Yuting
Pas, Savannah
Gross, Michelle A.
Carillo, Luis
Stein, Peter
Ding, Yu
Xu, Bai-Xiang
Banerjee, Sarbajit
author_sort Santos, David A.
collection PubMed
description The origins of performance degradation in batteries can be traced to atomistic phenomena, accumulated at mesoscale dimensions, and compounded up to the level of electrode architectures. Hyperspectral X-ray spectromicroscopy techniques allow for the mapping of compositional variations, and phase separation across length scales with high spatial and energy resolution. We demonstrate the design of workflows combining singular value decomposition, principal-component analysis, k-means clustering, and linear combination fitting, in conjunction with a curated spectral database, to develop high-accuracy quantitative compositional maps of the effective depth of discharge across individual positive electrode particles and ensembles of particles. Using curated reference spectra, accurate and quantitative mapping of inter- and intraparticle compositional heterogeneities, phase separation, and stress gradients is achieved for a canonical phase-transforming positive electrode material, α-V(2)O(5). Phase maps from single-particle measurements are used to reconstruct directional stress profiles showcasing the distinctive insights accessible from a standards-informed application of high-dimensional chemical imaging.
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spelling pubmed-97686842022-12-22 Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials Santos, David A. Andrews, Justin L. Lin, Binbin De Jesus, Luis R. Luo, Yuting Pas, Savannah Gross, Michelle A. Carillo, Luis Stein, Peter Ding, Yu Xu, Bai-Xiang Banerjee, Sarbajit Patterns (N Y) Article The origins of performance degradation in batteries can be traced to atomistic phenomena, accumulated at mesoscale dimensions, and compounded up to the level of electrode architectures. Hyperspectral X-ray spectromicroscopy techniques allow for the mapping of compositional variations, and phase separation across length scales with high spatial and energy resolution. We demonstrate the design of workflows combining singular value decomposition, principal-component analysis, k-means clustering, and linear combination fitting, in conjunction with a curated spectral database, to develop high-accuracy quantitative compositional maps of the effective depth of discharge across individual positive electrode particles and ensembles of particles. Using curated reference spectra, accurate and quantitative mapping of inter- and intraparticle compositional heterogeneities, phase separation, and stress gradients is achieved for a canonical phase-transforming positive electrode material, α-V(2)O(5). Phase maps from single-particle measurements are used to reconstruct directional stress profiles showcasing the distinctive insights accessible from a standards-informed application of high-dimensional chemical imaging. Elsevier 2022-11-17 /pmc/articles/PMC9768684/ /pubmed/36569543 http://dx.doi.org/10.1016/j.patter.2022.100634 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Santos, David A.
Andrews, Justin L.
Lin, Binbin
De Jesus, Luis R.
Luo, Yuting
Pas, Savannah
Gross, Michelle A.
Carillo, Luis
Stein, Peter
Ding, Yu
Xu, Bai-Xiang
Banerjee, Sarbajit
Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials
title Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials
title_full Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials
title_fullStr Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials
title_full_unstemmed Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials
title_short Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials
title_sort multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768684/
https://www.ncbi.nlm.nih.gov/pubmed/36569543
http://dx.doi.org/10.1016/j.patter.2022.100634
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