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Record W2794348180 · doi:10.1149/2.0971803jes

Development of Electrolytes for Single Crystal NMC532/Artificial Graphite Cells with Long Lifetime

2018· article· en· W2794348180 on OpenAlexafffund
Jing Li, Hongyang Li, Will Stone, Stephen Glazier, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolyteGraphiteEthylene carbonateCoulometryMaterials scienceIsothermal processLithium (medication)Plating (geology)Crystal (programming language)Single crystalChemical engineeringElectrochemistryInorganic chemistryElectrodeChemistryMetallurgyCrystallographyThermodynamics

Abstract

fetched live from OpenAlex

NMC532/artificial graphite cells using single crystal NMC532 active material can have excellent long term lifetime at 4.4 V and elevated temperature if appropriate electrolytes are used. However, electrolytes developed earlier for these cells and reported in the literature cannot support even C/2 rates during charging without unwanted lithium plating at room temperature. This work is thus focused on the development of new electrolytes for single crystal NMC532/artificial graphite cells that can yield long lifetime and support higher charging rates. Ex-situ and in-situ gas measurements, ultra-high precision coulometry, isothermal microcalorimetry, lithium plating tests and long term cycling tests were used for the screening of electrolytes. Electrolytes with 2% vinylene carbonate (VC) + 1% ethylene sulfate (DTD) additives or 2% fluoroethylene carbonate (FEC) + 1% DTD additives yield single-crystal NMC532/graphite cells with long lifetime that can support C-rate charging at 20°C.

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 score0.003

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 communication0.0010.001
Open science0.0010.000
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations84
Published2018
Admission routes2
Has abstractyes

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