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Record W4293221857 · doi:10.1149/1945-7111/ac5544

Effect of Lithium Sulfate on the Catalytic Activity of Pt for Hydrogen Oxidation Reaction

2022· article· en· W4293221857 on OpenAlexafffund
Manon Faral, Nicolas Sacré, Régis Chenitz, Asmae Mokrini, Thomas Bibienne, Nicolas Laroche, Mickaël Dollé

Bibliographic record

VenueJournal of The Electrochemical Society · 2022
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsNemaska Lithium (Canada)National Research Council CanadaUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesMitacs
KeywordsTafel equationChemistryLithium (medication)Inorganic chemistryElectrocatalystElectrochemistryLimiting currentCatalysisDesorptionCyclic voltammetryElectrolyteAdsorptionSulfateRotating disk electrodeHydrogenReaction mechanismElectrodePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of Lithium sulfate on the hydrogen oxidation reaction (HOR) in 0.1 M H2SO4 electrolyte was investigated on flat Pt electrode. The Li+ concentration solutions of 0, 10, 25 and 32 g l−1 were studied using cyclic voltammetry and rotating disk electrode (RDE) techniques. The obtained results demonstrate a good repeatability and confidence in analysis method, to understand the influence of lithium sulfate on HOR for an electrocatalysis system. The electrochemical surface area, limiting current and kinetic parameters were measured and analysed using Koutecky-Levich and Tafel representations to investigates the different types of lithium sulfate interactions on the catalytic properties of Pt. In presence of Li2SO4, the H+ adsorption/desorption process, species mass-transport and kinetic current density are reduced. Furthermore, the Tafel’s slope analyse show a change of the rate-determining steps for HOR mechanism. More detailed results of the kinetic analysis and lithium impact on the studied systems are discussed in this work.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

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