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Record W2783158233 · doi:10.1021/acscatal.7b02942

Tuning Pt–Ir Interactions for NH<sub>3</sub> Electrocatalysis

2018· article· en· W2783158233 on OpenAlexafffund
Nicolas Sacré, Matteo Duca, Sébastien Garbarino, Régis Imbeault, Andrew Z. Wang, Azza Hadj Youssef, Jules Galipaud, Gregor Hufnagel, Andreas Ruëdiger, Lionel Roué, Daniel Guay

Bibliographic record

VenueACS Catalysis · 2018
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsElectrocatalystX-ray photoelectron spectroscopyCyclic voltammetryMaterials sciencePulsed laser depositionEpitaxyAnalytical Chemistry (journal)Deposition (geology)Thin filmMiscibilityDiffractionCrystallographyElectrodeElectrochemistryChemistryPhysical chemistryNanotechnologyChemical engineeringPolymerOptics

Abstract

fetched live from OpenAlex

Pt x Ir 100– x thin films (8 nm thick) were deposited on MgO(100) substrates by pulsed laser deposition at 600 °C. As shown by X-ray diffraction and X-ray photoelectron spectroscopy analyses, the formation of fcc bulk and surface PtIr solid solution was observed over the whole compositional range despite the large miscibility gap exhibited by these elements below ca. 1000 °C. Moreover, pole figures indicate that all films exhibit a Pt(001)[010]//[010](001)MgO cube-on-cube epitaxial relationship, which is consistent with AFM images. Cyclic voltammetry measurements performed in alkaline solutions confirmed both the presence of Ir atoms at the surface and the (100) surface orientation of all Pt x Ir 100– x surfaces. The highest electrocatalytic activity for the electrooxidation of NH 3 was observed for the Pt 74 Ir 26 (100) surface with a current density of ca. 1.0 mA cm –2 at 0.71 V vs RHE, which is 25% higher than that on Pt(100). The reasons underlying this behavior are discussed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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.

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

Citations71
Published2018
Admission routes2
Has abstractyes

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