Using Lithium-ion Differential Thermal Analysis to Probe Tortuosity of Negative Electrodes in Lithium-Ion Cells
Bibliographic record
Abstract
If the positive electrode of a lithium-ion cell faces a surface with no opposing negative electrode, Li + ions can plate on the nearest edge of the negative electrode current collector. This poses considerable danger to the battery, and so modern Li-ion cells have a negative electrode that is both wider and longer than the positive electrode. In this work, we present evidence using Li-ion cell differential thermal analysis that this overhang causes the formation of long lived electrolyte concentration gradients after discharge or charge due to the long times needed for the lithium content in the overhang region of the negative electrode to equilibrate with the lithium content in the bulk of the negative electrode. Several cases are shown, as well as a comparison to a commercial cell, and an estimation of the type and magnitude of the electrolyte concentration gradient is given. Finally, it is shown that this phenomenon can be applied to easily distinguish between graphite electrodes with high and low tortuosity using differential thermal analysis.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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".