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Record W2035591567 · doi:10.1021/cs500147p

Ethanol Electro-Oxidation on Ternary Platinum–Rhodium–Tin Nanocatalysts: Insights in the Atomic 3D Structure of the Active Catalytic Phase

2014· article· en· W2035591567 on OpenAlexaff
Nina Erini, Rameshwori Loukrakpam, Valeri Petkov, Elena A. Baranova, Ruizhi Yang, Detre Teschner, Yunhui Huang, Stanko R. Brankovic, Peter Strasser

Bibliographic record

VenueACS Catalysis · 2014
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNanomaterial-based catalystCatalysisX-ray photoelectron spectroscopyOxidation stateRhodiumChemistryTernary operationTinPlatinumFourier transform infrared spectroscopyChemical statePhase (matter)Inorganic chemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Novel insights in the synthesis–structure–catalytic activity relationships of nanostructured trimetallic Pt–Rh–Sn electrocatalysts for the electrocatalytic oxidation of ethanol are reported. In particular, we identify a novel single-phase Rh-doped Pt–Sn Niggliite mineral phase as the source of catalytically active sites for ethanol oxidation; we discuss its morphology, composition, chemical surface state, and the detailed 3D atomic arrangement using high-energy (HE-XRD), atomic pair distribution function (PDF) analysis, and X-ray photoelectron spectroscopy (XPS). The intrinsic ethanol oxidation activity of the active Niggliite phase exceeded those of earlier reports, lending support to the notion that the atomic-scale neighborhood of Pt, Rh, and Sn is conducive to the emergence of active surface catalytic sites under reaction conditions. In situ mechanistic Fourier transform infrared (in situ FTIR) analysis confirms an active 12 electron oxidation reaction channel to CO 2 at electrode potentials as low as 450 mV/RHE, demonstrating the favorable efficiency of the PtRhSn Niggliite phase for C–C bond splitting.

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)
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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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

Citations109
Published2014
Admission routes1
Has abstractyes

Explore more

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