Quantifying Inhomogeneity of Lithium Ion Battery Electrodes and Its Influence on Electrochemical Performance
Bibliographic record
Abstract
This paper presents an approach to quantify microstructural inhomegeneity in lithium ion battery electrodes over multiple length scales and examines the impact of this microstructural inhomogeneity on electrochemical performance. Commerical graphite anodes are investigated because graphite remains the anode material of choice due to its low cost, mechanical robustness, and suitable electrochemical properties. At the same time, the graphite anode often plays a role in cell degradation and failure, as lithium plating can occur on the graphite anode during charge, when unfavorable microstructure in the graphite electrode leads to a large overpotential. Here, three-dimensional representations of four different commercial anodes obtained with X-ray tomographic microscopy are statistically analyzed to quantify the microstructural inhomogeneity that is commonly present in lithium ion battery electrodes. Electrochemical simulations on the digitalized microstructures are performed to isolate and understand the influence of different types of microstructural inhomogeneity on battery performance. By understanding how distributions in particle size and shape or slurry and electrode processing cause microstructural inhomogeneity and impact performance, it is possible to determine the extent to which homogeneity should be prioritized for specific applications and how homogeneity could be achieved through smart material selection and processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".