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Record W2045786027 · doi:10.1021/jp8084149

Influence of the Conductivity and Viscosity of Protic Ionic Liquids Electrolytes on the Pseudocapacitance of RuO<sub>2</sub> Electrodes

2009· article· en· W2045786027 on OpenAlexaff
Laurence Mayrand-Provencher, Dominic Rochefort

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPseudocapacitanceIonic liquidElectrolyteChemistryInorganic chemistryIonic conductivityConductivityViscosityTrifluoroacetic acidElectrochemistrySupercapacitorElectrodeMaterials scienceOrganic chemistryPhysical chemistryCatalysis

Abstract

fetched live from OpenAlex

Several new protic ionic liquids (PILs) were prepared by mixing trifluoroacetic acid (TFA) with various heterocyclic amines using different base:acid ratios (1:1 and 1:2). Their specific conductivities have been measured by electrochemical impedance spectroscopy and were found ranging from 0.71 to 9.07 mS·cm −1 at 27.0 °C. In all cases, the conductivity of a given ionic liquid increased with a higher proportion of acid (1:2 ratio) which is explained mainly a smaller viscosity obtained in these conditions. Indeed, in most PILs, the dynamic viscosity (10.3−484 cp at 27.0 °C) is decreased by a higher acid proportion. Both parameters were used to construct a Walden plot in order to evaluate the quality of these protic ionic liquids (PILs) and compare them with common room temperature (RT) molten salts. The physicochemical properties of the PILs were then compared with the specific capacitance values obtained with a thermally prepared RuO 2 electrode in these ionic liquid electrolytes. This work brings evidence that pseudocapacitance is involved in the energy storage mechanism of the PILs studied here and shows that PILs could eventually be used as electrolytes in metal oxide-based supercapacitors.

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)0.001
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 score0.011
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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.

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

Citations59
Published2009
Admission routes1
Has abstractyes

Explore more

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