Forecasting Battery Electrode Performance via Electrochemical Fluorescence Microscopy and Machine-Learning.

ABSTRACT: Predicting lithium-ion battery performance is hindered by microscale electrode heterogeneities invisible to conventional diagnostics. Here, we combine electrochemical fluorescence microscopy (EFM), which maps electronic connectivity by visualizing an electrofluorophore reaction distribution, with a multitask ElasticNet regression to forecast discharge capacity from spatial heterogeneity. Analyzing 196 images from six pilot-scale LiNi_0.5Mn_0.3Co_0.2O₂ cathodes with varying carbon loadings, we extract 62 descriptors that capture morphology and texture. A compact five-feature model predicts capacity across eight discharge rates, achieving a per-target R² of up to 0.63 and an overall R² of 0.92, with a mean absolute

SUBMITTER: Negrete K 

PROVIDER: S-EPMC12723639 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature