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Record W4306408252 · doi:10.1134/s1023193522100111

On the Thermodynamics of Hydrogen Adsorption at Pt Electrodes

2022· article· en· W4306408252 on OpenAlexaffabout
V. A. Safonov, Jacek Lipkowski

Bibliographic record

VenueRussian Journal of Electrochemistry · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHydroniumAdsorptionElectrolyteChemistryHydrogenGibbs free energyElectrodePlatinumGibbs isothermStandard hydrogen electrodeElectrochemistryElectrode potentialIonInorganic chemistryReversible hydrogen electrodeThermodynamicsPhysical chemistryReference electrodeCatalysis

Abstract

fetched live from OpenAlex

Abstract In the mid 1960th–early 1970th, A. Frumkin and O. Petrii investigated the adsorption of hydrogen at a platinized platinum electrode surface performing measurement of a change in the hydronium ion concentration when the large surface area electrode was immersed into an electrolyte solution at the fixed potentials in the hydrogen adsorption region under equilibrium conditions. The depletion (or increase) of the hydronium ions in the electrolyte bulk corresponded to the amount of hydrogen ions adsorbed at the electrode surface. They derived an equation relating the amounts of adsorbed hydrogen (atoms and ions) to the change of surface energy. A several decades later Guelph–Alicante team employed the Gibbs–Duhem equation to determine the amount of hydrogen atoms adsorbed at a small Pt single crystal electrode from the measurement of the total charge at the Pt surface. This paper compares the thermodynamic basis of the two types of measurements and demonstrates that despite differences between the nature of the two experiments their thermodynamic foundations are equivalent.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.195
Teacher spread0.190 · 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 designTheoretical or conceptual
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

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