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Record W2920916182 · doi:10.1149/2.0801904jes

Editors' Choice—Hindering Rollover Failure of Li[Ni<sub>0.5</sub>Mn<sub>0.3</sub>Co<sub>0.2</sub>]O<sub>2</sub>/Graphite Pouch Cells during Long-Term Cycling

2019· article· en· W2920916182 on OpenAlexaff
Xiaowei Ma, Jessie Harlow, Jing Li, Lin Ma, David S. Hall, Samuel Buteau, Matthew Genovese, Marc M. E. Cormier, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteRollover (web design)Dielectric spectroscopyMaterials scienceElectrodeElectrochemistryLithium (medication)ChemistryAnalytical Chemistry (journal)Chromatography

Abstract

fetched live from OpenAlex

Eventual rapid capacity loss or "rollover" failure of lithium-ion cells during long-term cycling (>3000 cycles in many cases) at room temperature was studied with Li[Ni0.5Mn0.3Co0.2]O2/graphite pouch cells. The effects of positive electrode material coating, electrolyte additives, upper cutoff voltage, LiPF6 concentration, cell rest periods, electrode thickness, graphite type and electrolyte solvent formulation were probed. Cells were tested under 1C charge and discharge with "rate maps" (discharges at C/20, C/2, 1C, 2C, 3C) applied every 100 cycles. The loss of high rate capability (3C) is shown to be an early warning of impending rollover failure. Electrochemical impedance spectroscopy (EIS) and studies of symmetric cells made using electrodes from disassembled cells demonstrate that impedance growth at the positive electrode and associated DC resistance growth is responsible for rollover failure in these cells. Ultra-high precision coulometry (UHPC) shows that cells that were charged to higher voltages, which increase the rate of electrolyte oxidation, or show higher rates of electrolyte oxidation at the same cutoff potential due to changes in electrolyte formulation, normally are more prone to eventual rollover failure. In order to avoid rollover and extend the cycling life of Li-ion cells, it is important to choose optimal cell chemistries, some of which are enumerated in this report.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.206
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

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

Citations112
Published2019
Admission routes1
Has abstractyes

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