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Record W3006480339 · doi:10.1002/cctc.201902187

Isotopic Oxygen Exchange Study to Unravel Noble Metal Oxide/Support Interactions: The Case of RuO<sub>2</sub> and IrO<sub>2</sub> Nanoparticles Supported on CeO<sub>2</sub>, TiO<sub>2</sub> and YSZ

2020· article· en· W3006480339 on OpenAlexaff
Yasmine M. Hajar, A. Boréave, A. Caravaca, P. Vernoux, Elena A. Baranova

Bibliographic record

VenueChemCatChem · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCatalysisOxideNoble metalCerium oxideYttria-stabilized zirconiaOxygenInorganic chemistryMaterials scienceMetalCubic zirconiaNanoparticleMixed oxideCeriumChemical engineeringChemistryNanotechnologyMetallurgyCeramic

Abstract

fetched live from OpenAlex

Abstract The aim of this study is to unravel the mechanism of (noble metal oxide)/(active support) interactions for catalytic purposes. Hence, isotopic oxygen exchange (IOE) tests were performed on Iridium‐ and ruthenium‐based oxides supported on cerium oxide (CeO 2 ), titanium oxide (TiO 2 ), and yttria‐stabilized zirconia (YSZ). IOE tests demonstrated the metal oxide support involvement in the propane oxidation reaction, with YSZ‐based catalysts showing the highest exchange rate of oxygen, while CeO 2 and TiO 2 ‐based catalysts had a less diffusion of lattice oxygen in that order. This is related to the presence of extrinsic oxygen vacancies in the YSZ‐based catalysts and the reduction ability of the CeO 2 and TiO 2 supports. Despite the limitations on oxygen exchange in some of the noble/metal oxide catalysts, their catalytic performance was comparable to the ones that showed a high oxygen exchange. Therefore, active supports results in a higher engagement of oxygen from the support but not in a linear correlation with the catalytic performance of the metal/support.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.025
GPT teacher head0.263
Teacher spread0.238 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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