Editors' Choice—Hindering Rollover Failure of Li[Ni<sub>0.5</sub>Mn<sub>0.3</sub>Co<sub>0.2</sub>]O<sub>2</sub>/Graphite Pouch Cells during Long-Term Cycling
Bibliographic record
Abstract
Eventual rapid capacity loss or "rollover" failure of lithium-ion cells during long-term cycling (>3000 cycles in many cases) at room temperature was studied with Li[Ni 0.5 Mn 0.3 Co 0.2 ]O 2 /graphite pouch cells. The effects of positive electrode material coating, electrolyte additives, upper cutoff voltage, LiPF 6 concentration, cell rest periods, electrode thickness, graphite type and electrolyte solvent formulation were probed. Cells were tested under 1C charge and discharge with "rate maps" (discharges at C/20, C/2, 1C, 2C, 3C) applied every 100 cycles. The loss of high rate capability (3C) is shown to be an early warning of impending rollover failure. Electrochemical impedance spectroscopy (EIS) and studies of symmetric cells made using electrodes from disassembled cells demonstrate that impedance growth at the positive electrode and associated DC resistance growth is responsible for rollover failure in these cells. Ultra-high precision coulometry (UHPC) shows that cells that were charged to higher voltages, which increase the rate of electrolyte oxidation, or show higher rates of electrolyte oxidation at the same cutoff potential due to changes in electrolyte formulation, normally are more prone to eventual rollover failure. In order to avoid rollover and extend the cycling life of Li-ion cells, it is important to choose optimal cell chemistries, some of which are enumerated in this report.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".