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Journal article
Forecasting Battery Electrode Performance via Electrochemical Fluorescence Microscopy and Machine-Learning
ACS applied materials & interfaces, v 17(50), pp 67906-67913
03 Dec 2025
PMID: 41332278
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
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 LiNi0.5Mn0.3Co0.2O2 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 percentage error of less than 2%. This performance rivals impedance-based approaches while avoiding their reliance on postformation data and incomplete electronic network information. Our facile and rapid, image-driven method may enable electrode quality control upstream of costly cell assembly to offer a transformative tool for data-driven battery research and manufacturing.
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Details
- Title
- Forecasting Battery Electrode Performance via Electrochemical Fluorescence Microscopy and Machine-Learning
- Creators
- Karla Negrete - Drexel University, Mechanical Engineering and MechanicsMarco-Tulio Fonseca Rodrigues - Argonne National LaboratoryDaniel P. Abraham - Argonne National LaboratoryMaureen H Tang (Corresponding Author) - Drexel University, Chemical and Biological Engineering
- Publication Details
- ACS applied materials & interfaces, v 17(50), pp 67906-67913
- Publisher
- ACS Publications
- Number of pages
- 8
- Grant note
- U.S. Department of Energy: DE-AC02-06CH11357 U.S. Department of Education: P200A190036
K.N. was supported by the US Department of Education GAANN program, fund #P200A190036. The submitted manuscript has been created in part by UChicago Argonne, LLC, Operator of Argonne National Laboratory ("Argonne"). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:001641460900001
- Scopus ID
- 2-s2.0-105025242549
- Other Identifier
- 991022135141704721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Materials Science, Multidisciplinary
- Nanoscience & Nanotechnology