{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T08:45:31Z","timestamp":1791794731971,"version":"4.3.4"},"reference-count":58,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Small Methods"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Electrode\u2010electrolyte interphases (EEIs) govern battery performance, yet the interplay between their chemical composition and spatial organization remains challenging to resolve. Time\u2010of\u2010flight secondary ion mass spectrometry (TOF\u2010SIMS) provides rich 3D chemical depth profiles of these interfaces, yet conventional analyses simplify the complexity by collapsing the data into 1D depth profiles or 2D maps, obscuring critical spatial features. Here, we apply a machine\u2010learning\u2010based approach directly to 3D TOF\u2010SIMS depth profiles, leveraging their full spatial and chemical richness to investigate the interphase structure and chemistry more comprehensively than conventional analyses. Principal component analysis (PCA) identifies fragments that dominate chemical heterogeneity across the EEI, reveals correlated and anticorrelated spatial distributions, and enables direct comparison of average interphase chemistry and chemical variability. Building on this, t\u2010distributed stochastic neighbor embedding (t\u2010SNE) and uniform manifold approximation and projection (UMAP) resolve stratified structures and fine\u2010scale spatial interphase variations. Finally, applying PCA to fragment\u2010specific thickness maps enables comparison of interphase thickness heterogeneity and identifies the chemical species responsible for local thickness variations. Together, the approach creates a generalized framework for extracting chemically and spatially meaningful information from complex TOF\u2010SIMS datasets, linking interphase chemistry to electrochemical behavior and guiding the design of uniform interphases for advanced battery systems.<\/jats:p>","DOI":"10.1002\/smtd.71113","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T07:58:27Z","timestamp":1791791907000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unveiling Hidden Patterns in Electrode\u2010Electrolyte Interphase Chemistry and Architecture Through Machine Learning\u2010Assisted Mass Spectrometry Analysis"],"prefix":"10.1002","author":[{"given":"Tara","family":"Ingebrand","sequence":"first","affiliation":[{"name":"Materials Science and Engineering Program and Walker Department of Mechanical Engineering The University of Texas at Austin  Austin Texas 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