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Record W2057429110 · doi:10.1002/celc.201402065

The Role of Surface Hydrogen Atoms in the Electrochemical Reduction of Pyridine and CO<sub>2</sub> in Aqueous Electrolyte

2014· article· en· W2057429110 on OpenAlexaff
Estelle Lebègue, Julia Agullo, Mario Morin, Daniel Bélanger

Bibliographic record

VenueChemElectroChem · 2014
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPyridiniumElectrochemistryGlassy carbonInorganic chemistryPyridineChemistryElectrodeReversible hydrogen electrodeElectrolyteAdsorptionAqueous solutionHydrogenCyclic voltammetryStandard hydrogen electrodeWorking electrodePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The present study aims to get more insight into the role of pyridinium ions, surface H atoms and the nature of the electrode surface for the electrochemical reduction of CO2. The electrochemical activity of pyridinium ions in the absence and presence of CO2 is investigated on Ir, Pt, Au and glassy carbon electrodes. Glassy carbon and Au electrodes show irreversible reduction of pyridinium characterized by a cathodic peak potential. In the further presence of CO2, an increase of the current is noticed and the overall reduction process remains irreversible. In contrast, cyclic voltammograms recorded on an Ir electrode in a pyridine solution under nitrogen and CO2 are quasi‐reversible and consistent with the participation of H atoms adsorbed onto the electrode surface. Cyclic voltammograms for Ir and Pt electrodes are similar, as expected for metals with a strong affinity for hydrogen. Our results suggest that adsorbed H atoms may play a key role in the electrochemical reduction of CO2.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.003
GPT teacher head0.207
Teacher spread0.203 · 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

Citations43
Published2014
Admission routes1
Has abstractyes

Explore more

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