Transition Metal Dissolution, Ion Migration, Electrocatalytic Reduction and Capacity Loss in Lithium-Ion Full Cells
Bibliographic record
Abstract
Continuous operation of full cells with layered transition metal (TM) oxide positive electrodes (NCM523) leads to dissolution of TM ions and their migration and incorporation into the solid electrolyte interphase (SEI) of the graphite-based negative electrode. These processes correlate with cell capacity fade and accelerate markedly as the upper cutoff voltage (UCV) exceeds 4.30 V. At voltages ≥4.4 V there is enhanced fracture of the oxide during cycling that creates new surfaces and causes increased solvent oxidation and TM dissolution. Despite this deterioration, cell capacity fade still mainly results from lithium loss in the negative electrode SEI. Among TMs, Mn content in the SEI shows a better correlation with cell capacity loss than Co and Ni contents. As Mn ions become incorporated into the SEI, the kinetics of lithium trapping change from power to linear at the higher UCVs, indicating a large effect of these ions on SEI growth and implicating (electro)catalytic reactions. We estimate that each Mn II ion deposited in the SEI causes trapping of ∼10 2 additional Li + ions thereby hastening the depletion of cyclable lithium ions. Using these results, we sketch a mechanism for cell capacity fade, emphasizing the conceptual picture over the chemical detail.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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".