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Record W4382560907 · doi:10.1016/s1452-3981(23)13443-2

Influence of Pressure on the Structural and Electrocatalytic Properties of Pt Nanoparticles Grown by Pulsed Laser Ablation onto Carbon Paper Substrate

2012· article· en· W4382560907 on OpenAlexafffund
Zéhira Hamoudi, My Alı El Khakani, Mohamed Mohamedi

Bibliographic record

VenueInternational Journal of Electrochemical Science · 2012
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesCentre québécois sur les matériaux fonctionnelsMinistère du Développement Économique, de l’Innovation et de l’Exportation
KeywordsPulsed laser depositionMaterials scienceSubstrate (aquarium)Surface roughnessLaser ablationTorrElectrochemistryDeposition (geology)Carbon fibersChemical engineeringNanoparticleThin filmSurface finishNanotechnologyLaserElectrodeComposite materialChemistryOpticsPhysical chemistry

Abstract

fetched live from OpenAlex

The pulsed laser deposition has been used to synthesize Pt nanostructured films onto carbon paper substrate. In summary, we have examined the change occurring in structural characteristics of Pt thin films grown in a He background gas and in vacuum. The electrochemical studies showed that Pt deposited under 5 Torr of He background pressure displayed the highest electroactive surface area, and the highest current mass activity of methanol electro-oxidation. The reason for such enhanced electrocatalytic activity is ascribed to the high roughness of Pt5T surface, which offers more active sites. From an application point of view, this study demonstrates that using similar amount of Pt, the enhancement of the electrocatalytic activity can be achieved by tuning the surface roughness of the Pt rather than increasing its loading.

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 score0.335

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 integrity0.0000.000
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 head0.007
GPT teacher head0.215
Teacher spread0.209 · 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

Citations10
Published2012
Admission routes2
Has abstractyes

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