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Record W2033402727 · doi:10.1149/2.014401jes

Beneficial Effects of Adsorbate-Induced Surface Segregation of Pt in Nanoporous Metals Fabricated by Dealloying of Ag-Au-Pt Alloys

2013· article· en· W2033402727 on OpenAlexafffund
Adrián A. Vega

Bibliographic record

VenueJournal of The Electrochemical Society · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNanoporous metals and alloys
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoporousMaterials scienceX-ray photoelectron spectroscopyTransmission electron microscopyChemical engineeringUnderpotential depositionScanning electron microscopeCyclic voltammetryInert gasElectrochemistryHydrogenNanotechnologyChemistryComposite materialElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Surface segregation of Pt is investigated in nanoporous metals formed by dealloying of Ag-Au-Pt alloys. By exposing freshly-dealloyed nanostructures to surprisingly low temperatures in the presence of laboratory air, the Pt segregates to the surface of the ligaments thanks to its preferential interaction with oxygen; in contrast, in an inert atmosphere (Ar-H2), Pt mostly remains in the bulk of the ligaments. Moreover, the co-segregation of Pt and O hinders the thermal coarsening of the ligaments. The averaged size of the ligaments, the resulting roughness factor (Rf) and the fraction of Pt atoms on the surface of the ligaments were investigated. Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy and low energy ion scattering were used to characterize the resulting nanoporous structure, in addition to electrochemical methods such as underpotential deposition of hydrogen and cyclic voltammetry.

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 score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.219
Teacher spread0.212 · 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

Citations25
Published2013
Admission routes2
Has abstractyes

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