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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.010
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.051
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