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Record W2346718461 · doi:10.14447/jnmes.v15i3.69

A Kinetic-Mechanistic Study of The Hydrogen Evolution Reaction in Sulfuric Acid Solutions with Different Electrode Materials

2012· article· en· W2346718461 on OpenAlexvenueno aff
J.C. Fuentes-Aceituno, G.T. Lapidus

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2012
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTafel equationCopperSulfuric acidInorganic chemistryOverpotentialChemistryHydrogenElectrocatalystElectrochemistryCharge transfer coefficientCatalysisPhysical chemistryElectrodeCyclic voltammetryOrganic chemistry

Abstract

fetched live from OpenAlex

The hydrogen evolution reaction (HER) has been studied extensively due to the potential application of molecular hydrogen H2as a green fuel. Recently this particular reaction attracted the attention of several electrometallurgical researchers because of another promising application, in which the monoatomic hydrogen species H· (intermediate product of HER) is employed as a reducing agent for copper-sulfide minerals. Consequently, knowledge about the kinetics, mechanisms and rate determining step (rds) of HER in sulfuric acid solutions employing aluminum, copper, Inconel® or glassy carbon(GC) cathodes is necessary to determine the under and overpotential domains where H2 and H· can be generated in a selective manner. Analyses of Tafel plots and the charge transfer coefficients, revealed two electrical potential zones for copper, Inconel and GC, where the monoatomic hydrogen can be recombined chemically or electrochemically to H2 as the rds. On the other hand, with aluminum, only the electrochemical recombination to H2 occurs as the rds. A catalytic effect on the hydrogen recombination reaction was also found when ferrous ion is contained in the solution. Finally, it was determined that aluminum is the most efficient electrocatalyst for producing H· and H2, followed by inconel, copper and GC.

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 score0.001
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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