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Record W4285399101 · doi:10.1149/ma2022-012247mtgabs

(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

2022· article· en· W4285399101 on OpenAlexaff
Scott R. Smith, Preet Sahota, Bryan D. Wood, Brian Way

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsE-One Moli Energy (Canada)
Fundersnot available
KeywordsCorrosionElectrolyteDegradation (telecommunications)Materials scienceMoistureHydrofluoric acidTafel equationMetallurgyChemistryElectrochemistryElectrodeComposite material

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.175
Threshold uncertainty score0.587

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.1750.024

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.

Opus teacher head0.021
GPT teacher head0.264
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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