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Dissertation
Electrochemical fluorescence microscopy to predict Li-ion battery performance
Doctor of Philosophy (Ph.D.), Drexel University
Sep 2025
DOI:
https://doi.org/10.17918/00011154
Abstract
As electric vehicle adoption accelerates, lithium-ion battery (LIB) production is scaling rapidly to meet global energy targets. To sustain this growth, manufacturers must produce thousands of cells per minute, leaving minimal tolerance for defects. Minor inconsistencies during electrode fabrication can cause early capacity fade, performance loss, or safety risks. One of the most critical and undercharacterized sources of cell variability is the strength of the electronic network within the electrode, which governs electron access to active material. Despite its importance, researchers have struggled to quantify this internal network at production scale. This dissertation introduces electrochemical fluorescence microscopy (EFM), a new in-situ imaging technique that visualizes the electronic network in composite LIB electrodes. The method combines a redox-sensitive fluorophore with a custom-designed electrochemical cell to map current-carrying pathways in real time. We developed an image processing pipeline that extracts spatial descriptors from EFM images, providing quantitative measures of electronic connectivity across the electrode surface. We used these image-derived metrics to train a multi-output regression model that predicts electrode discharge capacity across a range of C-rates. The results demonstrate that an EFM-machine learning framework can enable upstream quality control by forecasting electrode performance from image data before electrochemical testing. We further illustrate the utility of EFM through two case studies involving overcharged NMC cathodes and cycled graphite anodes, which reveal structural degradation and connectivity loss. Together, these contributions establish EFM as both a scalable diagnostic tool for LIB manufacturing and a research platform for investigating how electrode structure influences battery performance.
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Details
- Title
- Electrochemical fluorescence microscopy to predict Li-ion battery performance
- Creators
- Karla Negrete
- Contributors
- Maureen Han-Mei Tang (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Philosophy (Ph.D.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- xiv, 99 pages
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- College of Engineering (1970-2026); Mechanical Engineering (and Mechanics) [Historical]; Drexel University
- Other Identifier
- 991022085454604721