Accelerated Failure in Li[Ni<sub>0.5</sub>Mn<sub>0.3</sub>Co<sub>0.2</sub>]O<sub>2</sub>/Graphite Pouch Cells Due to Low LiPF<sub>6</sub> Concentration and Extended Time at High Voltage
Bibliographic record
Abstract
Li[Ni 0.5 Mn 0.3 Co 0.2 ]O 2 /graphite pouch cells were cycled using protocols that included 24 h spent at high voltage (≥ 4.3 V) under constant voltage or open circuit conditions to accelerate failure. Compared to traditional cycling, failure was reached up to 3.5 times faster. When this protocol was applied to cells containing low LiPF 6 concentrations (≤ 0.4 M) failure was achieved up to 17.5 times faster than traditional cycling with normal LiPF 6 concentrations. This represents a time improvement on the order of years and therefore can be used as a high-throughput screening method. Failure mechanisms for cells containing a range of LiPF 6 concentrations undergoing these aggressive protocols were investigated using charge-discharge cycling, impedance spectroscopy (including symmetric cell analysis) and isothermal microcalorimetry. Long times at high voltage rapidly increase positive electrode impedance but do not seem to consume lithium inventory. The use of lower LiPF 6 concentrations does not seem to introduce new failure mechanisms but makes cells less tolerant to positive electrode impedance growth. The utility of this method is demonstrated by screening cells with a variety of electrolyte additive combinations. Fewer than 3 months were required to distinguish cells containing 1% lithium difluorophospate as superior to cells with other additive combinations.
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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 | 0.001 | 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".