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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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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. |
format | Online Article Text |
id | pubmed-9768684 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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