(Digital Presentation) Evaluating the Corrosivity of Liquid LiPF<sub>6</sub> Electrolytes with Nickel-Coated Mild Steel Used in the Manufacturing of Li-Ion Cells for Energy Storage
Bibliographic record
Abstract
Effects of electrolyte degradation on cell performance and corrosion of cell components, e.g. current collectors, are topics that have been reported in the literature.1,2 Recently, it has been reported that water contamination of LiPF6 liquid electrolytes can lead to salt degradation, generating difluorophosphoric acid (DFPH) and hydrofluoric acid (HF).3 With strict quality control of the moisture content in individual components and the use of dryroom conditions for cell assembly the risk of moisture contamination can be practically eliminated. When moisture is allowed to enter the cell in controlled research samples, dark stains indicative of pitting type corrosion have been observed on the inside of mild steel cans that are used to house cylindrically wound electrodes (see Figure 1 inset). Motivation to understand the underlying corrosion mechanism and the influential factors is important to help lower the risk of occurrence in manufacturing further. The use of a simple corrosion plate cell to study the susceptibility of a mild steel sample immersed in a LiPF6/EC/DMC (15/25/60 wt%) electrolyte will be discussed, with a focus on measured corrosion potentials, E corr, and current densities, j corr, extracted from the Tafel region of a potentiodynamic scan. Observations on the influence of acid degradation products in the electrolyte on the corrosion susceptibility of the mild steel will be discussed and applied to study select electrolyte additives previously reported in literature. Early results have shown that additives that scavenge HF and/or water directly can effectively suppress j corr, as shown in Figure 1, whereas additives that increased DFPH generation had no apparent effect. References: [1] Myung, Hitoshi, and Sun. J. Mater. Chem., 2011, 21, 9891-9911. [2] Ma et al. J. Phys. Chem. Lett. 2017, 8, 5, 1072–1077 [3] Wiemers-Meyer, Winter, and Nowak. Phys. Chem. Chem. Phys., 2016, 18, 26595-26601. Figure 1: Bar graph of corrosion current, j corr, measured for a LiPF6/EC/DMC (15/25/60 wt%) control electrolyte with 2500 ppm of water contamination, as well as with ~2 wt% of an additive that formed more DFPH than the control, an additive that formed more DFPH and HF than the control, and an additive that scavenged HF. Inset: Optical image of the inside of a mild steel can disassembled after 14 days of 60°C storage at top of charge illustrating dark regions indicative of steel corrosion. 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.175 | 0.024 |
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".