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Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain
David A Santos1,2, Justin L Andrews1,2, Binbin Lin3
1Department of Chemistry, Texas A&M University, College Station, TX 77843-3255, USA.
Patterns (New York, N.Y.)
|December 26, 2022
Summary
Battery performance degradation stems from atomistic changes. New hyperspectral X-ray spectromicroscopy workflows quantify these changes across electrode particles, revealing insights into battery aging and stress.
Area of Science:
- Materials Science
- Electrochemistry
- Chemical Imaging
Background:
- Battery performance degradation originates from complex atomistic phenomena across multiple length scales.
- Understanding these phenomena is crucial for designing more durable and efficient energy storage systems.
- Hyperspectral X-ray spectromicroscopy offers high spatial and energy resolution for mapping material variations.
Purpose of the Study:
- To develop quantitative compositional mapping workflows for battery electrode materials.
- To analyze compositional heterogeneities and phase separation within electrode particles.
- To correlate these variations with stress gradients and battery performance.
Main Methods:
- Utilized hyperspectral X-ray spectromicroscopy for chemical imaging.
- Implemented singular value decomposition, principal-component analysis, k-means clustering, and linear combination fitting.
- Developed workflows integrating these methods with a curated spectral database for quantitative analysis.
Main Results:
- Achieved high-accuracy quantitative compositional maps of the effective depth of discharge.
- Successfully mapped inter- and intraparticle compositional heterogeneities and phase separation in α-V2O5.
- Reconstructed directional stress profiles from single-particle phase maps.
Conclusions:
- Demonstrated a standards-informed approach for high-dimensional chemical imaging in battery materials.
- Provided distinctive insights into the origins of battery performance degradation at the particle level.
- Enabled accurate characterization of compositional variations and stress gradients for improved battery design.
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