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Record W2757672473 · doi:10.1149/2.0431713jes

Electrolyte Reactivity on Graphite and Copper as Measured in Lithium Double Half Cells

2017· article· en· W2757672473 on OpenAlexafffund
Zilai Yan, M. N. Obrovac

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 Canada
KeywordsElectrolyteReactivity (psychology)GraphiteLithium (medication)CopperChemistryElectrodeElectrochemistryPropylene carbonateInorganic chemistryDecompositionChemical engineeringMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The study of electrolyte reactivity on electrode materials is fundamental to understand the service life of lithium ion batteries (LIBs). In this paper, a new type of symmetric cells, termed double half-cells (DHC), has been designed to evaluate electrolyte reactivity precisely and accurately while only using a standard charger. The construction of DHCs avoids the disassembly/assembly process and electrode alignment issues associated with conventional symmetric cells, resulting in improved performance and reliability. Using DHCs, electrolyte reactivity on graphite and copper were investigated. While electrolyte reactivity on Cu and graphite were found to be low at room temperature, elevated temperatures resulted in significant electrolyte reactivity on both surfaces. The addition of vinylene carbonate to the electrolyte was found to have little effect on graphite electrodes, but results in severe electrolyte decomposition on copper. These results show the utility of DHCs and also that inactive materials, such as current collectors, should not be ignored when considering electrolyte decomposition reactions in Li-ion cells.

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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Electrochemical Society→Same topicAdvancements in Battery Materials→French-language works237,207→