Electrochemical Fluorescence Microscopy to Predict Li-Ion Battery Performance
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| Publicado en: | ProQuest Dissertations and Theses (2025) |
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| 001 | 3248471982 | ||
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| 020 | |a 9798293826629 | ||
| 035 | |a 3248471982 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 66569 |2 nlm | ||
| 100 | 1 | |a Negrete, Karla | |
| 245 | 1 | |a Electrochemical Fluorescence Microscopy to Predict Li-Ion Battery Performance | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a 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 introduced during electrode fabrication can result in early capacity fade, performance loss, or safety risks. Among 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 significance, this internal network has remained difficult to quantify at the production scale. This dissertation presents electrochemical fluorescence microscopy (EFM), a new in-situ imaging technique for visualizing the electronic network in composite LIB electrodes. The method combines a redox-sensitive fluorophore with a custom-designed electrochemical cell to enable real-time optical mapping of current-carrying pathways. An image processing pipeline was developed to extract spatial descriptors from EFM images, providing quantitative measures of electronic connectivity across the electrode surface. These image-derived metrics were used to train a multi-output regression model capable of predicting electrode discharge capacity across a range of C-rates. The results demonstrate the potential of an EFM–machine learning framework for upstream quality control, in which electrode performance can be forecasted from image data before electrochemical testing. The broader utility of EFM is further illustrated through two case studies involving over-charged NMC cathodes and cycled graphite anodes, which highlight the method’s ability to detect 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. | |
| 653 | |a Energy | ||
| 653 | |a Engineering | ||
| 653 | |a Computer science | ||
| 653 | |a Electrical engineering | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3248471982/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3248471982/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |