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Record W2782146100 · doi:10.1149/2.0511802jes

Some Physical Properties of Ethylene Carbonate-Free Electrolytes

2018· article· en· W2782146100 on OpenAlexafffund
D. J. Xiong, Michael Bauer, L. D. Ellis, Toren Hynes, S. R. Hyatt, David S. Hall, 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
KeywordsElectrolyteConductivityEthylene carbonateDimethyl carbonateMelting pointChemistryFourier transform infrared spectroscopyAnalytical Chemistry (journal)Inorganic chemistryDifferential scanning calorimetryCarbonateMaterials scienceChemical engineeringPhysical chemistryThermodynamicsOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Physical properties of LiPF 6 in ethyl methyl carbonate (EMC) and dimethyl carbonate (DMC) electrolytes were studied by conductivity measurements, Fourier transform infrared spectroscopy (FT-IR) and differential thermal analysis (DTA). Conductivity measurements show that the addition of additive levels of FEC to EMC electrolyte can dramatically increase the conductivity of EC-free EMC electrolytes at low salt concentrations below 0.4 M. FT-IR results show that the added FEC hinders ion pair formation by competing with EMC to dissociate LiPF 6 resulting in increased conductivity in EMC electrolytes. Conductivity measurements show that the conductivity of DMC electrolytes decreases significantly below 0°C due to the high melting point of DMC. Differential thermal analysis was used to determine the LiPF 6 -DMC phase diagram which then can be used to explain the conductivity results. The results presented here identify avenues by which EC-free electrolytes can be improved for use in practical 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 score0.002
Threshold uncertainty score0.008

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.217
Teacher spread0.209 · 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

Citations62
Published2018
Admission routes2
Has abstractyes

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