MétaCan
Menu
Back to cohort
Record W2549970521 · doi:10.1149/2.1031614jes

Rapid Impedance Growth and Gas Production at the Li-Ion Cell Positive Electrode in the Absence of a Negative Electrode

2016· article· en· W2549970521 on OpenAlexafffund
D. J. Xiong, L. D. Ellis, K. J. Nelson, Toren Hynes, R. Petibon, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaShanghai Ocean University
KeywordsElectrolyteElectrodeGraphitePouchMaterials scienceChemistryAnalytical Chemistry (journal)Chemical engineeringChromatographyComposite materialSurgeryMedicine

Abstract

fetched live from OpenAlex

The effects of electrolyte additives on gas evolution, gas consumption and impedance growth at elevated temperature have been studied using Li[Ni 0.42 Mn 0.42 Co 0.16 ]O 2 (NMC442)/graphite pouch cells and pouch bags containing delithiated NMC442 or lithiated graphite electrodes plus electrolyte. It was found that there was much more gas, mostly CO 2 , generated in pouch bags containing charged positive electrodes than pouch cells. It was found that the impedance of the charged positive electrodes stored in pouch bags increased dramatically, while those stored in pouch cells did not. The two observations show that there are interactions between positive and negative electrodes that limit gas evolution and reduce impedance growth in Li-ion cells. To verify this, CO 2 intentionally added to pouch bags containing lithiated graphite and electrolyte was consumed. The use of several electrolyte additives, known to affect gassing and high voltage cycling did not substantially alter these conclusions. XPS studies were used to eliminate some possible mechanisms responsible for these phenomena.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 spread0.202 · 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

Citations59
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicAdvancements in Battery MaterialsFrench-language works237,207