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

• Published in: Patterns (New York, N.Y.) Vol. 3; no. 12; p. 100634
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 09.12.2022; Elsevier
• Subjects: battery materials; cathodes; chemo-mechanics; hyperspectral imaging; image analytics; multivariate data analytics; singular value decomposition; vanadium oxide; database; DSML3: Development/pre-production: Data science output has been rolled out/validated across multiple domains/problems; vanadium oxide; chemo-mechanics; multivariate data analytics; cathodes; image analytics; hyperspectral imaging; battery materials; singular value decomposition
• ISSN: 2666-3899; 2666-3899
• DOI: 10.1016/j.patter.2022.100634

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-V2O5. 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. [Display omitted] •Phase inhomogeneities and stress gradients govern electrode performance in batteries•Hyperspectral imaging allows for the mapping of compositional variations•Spectral databases are critical to investigating compositional heterogeneities•Chemistry-geometry-mechanics coupling is crucial to lithiation phenomena In battery electrode materials, phase inhomogeneities and stress gradients substantially influence battery performance and longevity. In this work, multivariate hyperspectral X-ray spectromicroscopy measurements are coupled with data dimensionality reduction and clustering techniques in conjunction with a spectral database and finite element analysis to probe compositional, phase, and strain heterogeneities in a canonical electrode material. The resulting maps are based on physically interpretable spectral standards and allow a detailed view of Li-intercalation-induced changes in the crystal lattice and electronic structure across decades of length scales. Direct observation of these changes across length scales provides a foundational understanding of lithiation processes and informs the design of next-generation electrode materials. In battery electrode materials, compositional heterogeneities give rise to stress gradients that ultimately result in degenerative failure. Progress in hyperspectral imaging has enabled an unparalleled view of multiphysics processes. Data science methods hold promise for deciphering mechanistic understanding from high-dimensional data. In this work, the utility of X-ray spectromicroscopy coupled with multivariate data analytics is demonstrated through multi-scale investigation of intercalation phenomena in a canonical cathode material aided by curation of a spectral standards database.