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Record W2096249813 · doi:10.1039/c3cp50560j

Electrochemical characterisation of a lithium-ion battery electrolyte based on mixtures of carbonates with a ferrocene-functionalised imidazolium electroactive ionic liquid

2013· article· en· W2096249813 on OpenAlexafffund
John C. Forgie, Soumia El Khakani, Dean D. MacNeil, Dominic Rochefort

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2013
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsNational Research Council CanadaUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologies
KeywordsIonic liquidElectrochemistryElectrolyteInorganic chemistryChemistryCyclic voltammetryEthylene carbonateLithium (medication)AnodeDimethyl carbonateRedoxBattery (electricity)FerroceneElectrodeOrganic chemistryPhysical chemistryCatalysis

Abstract

fetched live from OpenAlex

Electrolytic solutions of lithium-ion batteries can be modified with additives to improve their stability and safety. Electroactive molecules can be used as such additives to act as an electron (redox) shuttle between the two electrodes to prevent overcharging. The electroactive ionic liquid, 1-ferrocenylmethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)amide (TFSI), was synthesised and its electrochemical properties were investigated when diluted with ethylene carbonate-diethyl carbonate solvent at various concentrations. Cyclic voltammetry data were gathered to determine the redox potential, diffusion coefficient and heterogeneous rate constants of the electroactive imidazolium TFSI ionic liquid in the carbonate solution. The properties of this molecule as an additive in lithium battery electrolytes were studied in standard coin cells with a metallic Li anode and a Li4Ti5O12 cathode.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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 teacher head, not a consensus.

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

Citations54
Published2013
Admission routes2
Has abstractyes

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