MétaCan
Menu
Back to cohort
Record W4220932634 · doi:10.1002/elsa.202100148

Electrochemical reduction of quinones in ethaline chosen as an example of deep eutectic solvent

2022· article· en· W4220932634 on OpenAlexfundno aff
Fangchen Zhen, Philippe Hapiot

Bibliographic record

VenueElectrochemical Science Advances · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsnot available
FundersInstitute of Circulatory and Respiratory HealthCentre National de la Recherche ScientifiqueChina Scholarship Council
KeywordsChemistryElectrochemistryDeep eutectic solventSolventAcetonitrileElectron transferSubstituentEutectic systemIonic bondingKineticsHydrogen bondPhotochemistryPhysical chemistryElectrodeCrystallographyMoleculeStereochemistryIonOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The electrochemical reduction of a series of substituted benzoquinone have been examined in ethaline chosen as an example of ionic deep eutectic solvent. Experiments show the importance of hydrogen‐bonding interactions between the quinones or its intermediates and the solvent. The effects are notably visible on the values of reduction potentials that are much more positive in ethaline than in a molecular solvent like acetonitrile and by the small difference between the first and second reduction potentials. The amplitude of the stabilization increases with the donor character of the substituent. Concerning the second reduction, the peak currents are considerably smaller than those of the first reduction and almost disappear at high scan rates (above 50 V s −1 ). This behavior could be explained considering a chemical step prior to the electron transfer that becomes the limiting step (CE mechanism). As a remarkable feature, the electron transfer kinetics remain fast despite the hydrogen‐bonding interactions ( k s = 0.12–0.14 cm s −1 ).

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 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 score0.005
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.268
Teacher spread0.256 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueElectrochemical Science AdvancesSame topicIonic liquids properties and applicationsFrench-language works237,207