Corrosion of Ni-Coated Can Hardware of Li-Ion Batteries in Organic-Based LiPF<sub>6</sub> Electrolytes
Bibliographic record
Abstract
Water contamination of Lithium Ion Battery (LIB) electrolytes leads to hydrofluoric (HF) acid formation1, which impacts the formation2 and degradation3 of the SEI layer as well as causing corrosion of the positive electrode oxide materials and inactive components of the cell4. One of the inactive components that corrodes is the Ni-coated steel can used in the commercial 18650 cells. This paper presents for the first time a detailed report of Ni corrosion in water-contaminated organic-based LiPF6 electrolytes. The study reveals the chemical and electrochemical nature of Ni corrosion. Cyclic voltammetry, chronoamperometry and Tafel analysis coupled with electrochemical quartz microbalance (EQCM) technique were used to investigate the mechanism of the Ni dissolution at OCV and near OCV potentials (2.5 V – 3.5 V vs. Li/Li+). Chronoamperometry at a potential close to OCV gives a current and mass plateau for the first few hours (4 – 6 h) followed by a sudden oxidation current and mass loss due to Ni dissolution (Figure 1) indicating the generation of electrolyte-soluble corrosive species, which was confirmed by testing a pristine Ni electrode in the same used electrolyte (Figure 1). Speciation of dissolved Ni and generated corrosive species is of the great importance for revealing the mechanism and is in the focus of the ongoing research. SEM reveals pitting corrosion of Ni in LiPF6 based electrolytes. References M. Stich, M. Göttlinger, M. Kurniawan, U. Schmidt, and A. Bund, J. Phys. Chem. C, 122, 8836–8842 (2018). D. Strmcnik et al., Nat. Catal., 1, 255–262 (2018). J. G. Han et al., J. Power Sources, 446, 227366 (2020). L. Yang, M. Takahashi, and B. Wang, Electrochim. Acta, 51, 3228–3234 (2006). Figure 1. Three-electrode EQCM: Ni electrode mass change in 0.25 % water spiked 1 M LiPF6/EC/DEC (1:1) electrolyte during chronoamperometry at 3.35 V vs. Li/Li+. Figure 1
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".