Homogenization Study of the Effects of Cycling on the Electronic Conductivity of Commercial Lithium-Ion Battery Cathodes
Bibliographic record
Abstract
State-of-the-art image acquisition, image analysis, and modern homogenization theory are used to study the effects of cycling on commercial lithium-ion battery cathodes’ ability to conduct electronic current. This framework allows for a rigorous computation of an effective, or macroscale, electronic conductivity given an arbitrarily complicated three-dimensional microstructure comprised of three different material phases, i.e., active material, binder (polymer mixed with conductive carbon black), and electrolyte. The approach explicitly takes into account the geometry and is thus a vast improvement over the commonly used Bruggeman approximation. We apply our framework to two different types of lithium-ion battery cathodes before and after cycling. This leads us to predict an appreciable decrease in the effective electronic conductivity as a direct result of cycling. In addition, we present an ad-hoc “neighbor counting” methodology which meaningfully quantifies the effect of binder detaching from the surface of the active material due to the internal mechanical stresses experienced under operating conditions, thereby supporting the results of the homogenization calculations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".