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Record W4318612540 · doi:10.1016/j.cocis.2023.101677

Describing the unsuspected advantage of redox ionic liquids applied to electrochemical energy storage

2023· article· en· W4318612540 on OpenAlexaff
Charlotte Bodin, Bruno Gélinas, Jie Deng, Kulika Pithaksinsakul, Yachao Zhu, Dominic Rochefort, Olivier Fontaine

Bibliographic record

VenueCurrent Opinion in Colloid & Interface Science · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsIonic liquidRedoxElectrochemistryElectrolyteLithium (medication)Ionic bondingSupercapacitorIonInorganic chemistryFerroceneMaterials scienceChemistryElectrochemical windowChemical engineeringIonic conductivityOrganic chemistryElectrodePhysical chemistryCatalysis

Abstract

fetched live from OpenAlex

Ionic liquids are a class of solvents widely studied in the literature for various applications. As a subclass of ionic liquids, redox ionic liquids can endow charge exchange properties (electrons transfer) to these electrolytes for electrochemical energy storage . In this review article, we propose to study this family of ionic liquids and suggest a chronological classification. We introduce five generations of redox ionic liquids with different basic compounds such as polyethylene glycol , ferrocene , different linker lengths, TFSI anion, and biredox ionic liquids. The versatility of the redox ionic liquids synthesis will be discussed as well as the fundamental and applied aspects of their use as electrolytes, which have high charge densities. The impact of the redox ionic liquids on the electrochemical mechanisms will be described. We also present how the redox shuttle effect , detrimental to supercapacitors , can be prevented while it can be used to improve lithium-ion batteries.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.051
GPT teacher head0.317
Teacher spread0.266 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

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