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Record W2727660189 · doi:10.1149/2.0741709jes

Electrochemical Oxidation of Smooth and Nanoscale Rough Pt(111): An In Situ Surface X-ray Scattering Study

2017· article· en· W2727660189 on OpenAlexafffund
Martin Ruge, Jakub Drnec, Björn Rahn, Finn Reikowski, David A. Harrington, Francesco Carlà, Roberto Felici, Jochim Stettner, Olaf M. Magnussen

Bibliographic record

VenueJournal of The Electrochemical Society · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Synchrotron Radiation FacilityDeutsche Forschungsgemeinschaft
KeywordsElectrochemistryScatteringOxideIn situElectrodeMaterials scienceNanoscopic scaleChemical physicsCrystal (programming language)CrystallographyLayer (electronics)Analytical Chemistry (journal)ChemistryNanotechnologyOpticsPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

The potential dependent surface structure of Pt(111) electrodes in electrochemical environment was studied by in situ crystal truncation rod measurements and in operando grazing incidence small angle X-ray scattering. Determination of the interface structure in the oxidation region reveal a continuous increase of the coverage of place exchanged atoms toward more positive potentials. The resulting oxide consists of an oxygen rich outer and a Pt-rich inner atomic layer. With increasing potential the oxide approaches a more uniform stoichometry. While place exchange on atomically smooth terraces only occurs above 1.05 V, structural changes of 3D Pt islands, grown by repetitive oxidation/reduction cycles, already commence at 0.8 V, which is assigned to oxidation at Pt steps. Evolution of the 3D island distribution occurs in all stages of the cycles: Whereas Pt oxidation leads to narrowing of the lateral size distribution, vertical island growth occurs during the subsequent reduction.

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)0.001
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 score0.003
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.245
Teacher spread0.235 · 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

Citations41
Published2017
Admission routes2
Has abstractyes

Explore more

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