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Record W2770843729 · doi:10.1149/2.0191714jes

Quantifying, Understanding and Evaluating the Effects of Gas Consumption in Lithium-Ion Cells

2017· article· en· W2770843729 on OpenAlexafffund
L. D. Ellis, Jennifer P. Allen, Lauren Thompson, Jessie Harlow, W J D Stone, Ian G. Hill, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaShanghai Ocean UniversityUniversities Space Research Association
KeywordsLithium (medication)ElectrolyteElectrodeChemistryIonInterphaseAnalytical Chemistry (journal)Gas chromatographyGas consumptionEnvironmental chemistryChromatographyProcess engineeringOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Lithium-ion cells produce a considerable amount of gas in their first cycle. If the gases are not removed in a degassing step, most are consumed by the cell over time. This phenomenon has never been investigated explicitly in the literature. In this paper, the evolution and subsequent consumption of gas in typical lithium-ion cells are measured by Archimedes' principle and gas chromatography. It is found that all evolved gases are subsequently consumed to some degree, except for saturated hydrocarbons. The consumption of gas occurs predominantly at the negative electrode, where the gases are reduced to form part of the solid-electrolyte interphase (SEI). Changes to the negative electrode SEI upon gas consumption are investigated using X-ray photoelectron spectroscopy. The effect of gas consumption on cell performance is studied with ultra-high precision charging and high voltage storage experiments. It is found that gas consumption does not result in measurable adverse effects to cell performance.

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 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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.050
GPT teacher head0.321
Teacher spread0.271 · 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 teacher head, 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

Citations133
Published2017
Admission routes2
Has abstractyes

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