Corrosion of Lithium-ion Battery Cylindrical Cell Hardware: Understanding the Mechanisms and Exploring Effective Solutions
Bibliographic record
Abstract
We present a detailed examination of Ni corrosion in lithium-ion battery Ni-coated steel cylindrical cell hardware, focusing on LiPF 6 -based electrolytes contaminated with water. The corrosion potential of the cell hardware is predominantly controlled by the iron component of the cylindrical can which cathodically protects the Ni coating. Despite the presence of cathodic protection, the Ni coating still experiences significant crevice corrosion, as confirmed through chemical aging tests. Mechanistic investigations on pure Ni metal reveal two distinct corrosion pathways depending on the presence or absence of oxygen in the electrolyte. The pathway involving oxygen proves to be more detrimental, as it oxidizes Ni in conjunction with acid, leading to the generation of water and the regeneration of corrosive species. This pathway exhibits corrosion rates two orders of magnitude higher than the alternative pathway. The dissolved Ni species predominantly exist in the +2 oxidation state and forms highly soluble F-rich compounds, comprising a mixture of associated species denoted by the formula Ni(P x O y F z ) w . Finally, several suggestions for effectively mitigating Ni corrosion have been proposed, with alloying with chromium being the most effective.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".