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Record W2739077066 · doi:10.1149/2.1381709jes

Dramatic Effects of Low Salt Concentrations on Li-Ion Cells Containing EC-Free Electrolytes

2017· article· en· W2739077066 on OpenAlexafffund
D. J. Xiong, Toren Hynes, 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 Canada
KeywordsElectrolyteEthylene carbonateConductivityChemistryElectrochemistryIonic conductivitySalt (chemistry)ElectrodeLithium (medication)IonInorganic chemistryMaterials scienceOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Different concentrations of LiPF 6 (0.3 M–2 M) in ethyl methyl carbonate (EMC) electrolyte and ethylene carbonate (EC)-based electrolyte were studied in LiNi 0.4 Mn 0.4 Co 0.2 O 2 (NMC442)/graphite pouch cells. Fresh cells containing 0.3 M LiPF 6 in EMC electrolyte showed extremely large charge transfer resistance while those with 0.3 M LiPF 6 in EC/EMC electrolyte did not. Impedance spectra taken on symmetric cells and ionic conductivity measurements suggest this difference is due to difficulty in dissociating and desolvating Li + ions from the EMC-based electrolyte to intercalate into both the electrodes. After elevated temperature storage experiments at 4.5 V, cells with 0.3 M LiPF 6 in EC/EMC showed a large increase in positive electrode charge transfer impedance, presumably caused by electrolyte oxidation. With salt concentrations greater than 1 M, charge transfer resistance was much smaller in EMC-based electrolytes and was stable during storage for both electrolyte types. Conductivity and cycle testing measurements suggest that 1.5 M LiPF 6 should be used in EC-free EMC-based electrolytes to optimize 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 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)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.005
GPT teacher head0.225
Teacher spread0.220 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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