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

David A Santos; Justin L Andrews; Binbin Lin; Luis R De Jesus; Yuting Luo; Savannah Pas; Michelle A Gross; Luis Carillo; Peter Stein; Yu Ding; Bai-Xiang Xu; Sarbajit Banerjee

doi:10.1016/j.patter.2022.100634

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

Patterns (N Y). 2022 Nov 17;3(12):100634. doi: 10.1016/j.patter.2022.100634. eCollection 2022 Dec 9.

Authors

David A Santos^{1

2}, Justin L Andrews^{1

2}, Binbin Lin³, Luis R De Jesus^{1

2}, Yuting Luo^{1

2}, Savannah Pas^{1

2}, Michelle A Gross^{1

2}, Luis Carillo^{1

2}, Peter Stein³, Yu Ding⁴, Bai-Xiang Xu³, Sarbajit Banerjee^{1

2}

Affiliations

¹ Department of Chemistry, Texas A&M University, College Station, TX 77843-3255, USA.
² Department of Materials Science and Engineering, Texas A&M University, College Station, TX 77843-3255, USA.
³ Institute of Materials Science, Mechanics of Functional Materials, Technische Universität Darmstadt, Otto-Berndt-Str. 3, 64287 Darmstadt, Germany.
⁴ Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843-3255, USA.

Abstract

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.

Keywords: battery materials; cathodes; chemo-mechanics; database; hyperspectral imaging; image analytics; multivariate data analytics; singular value decomposition; vanadium oxide.