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Record W4395037408 · doi:10.1021/acscatal.4c00886

Remote Activation of H–H Bonds by Platinum in Dilute Alloy Catalysts

2024· article· en· W4395037408 on OpenAlexafffund
Tongxin Han, Yuanyuan Li, Tao Wu, Debora Meira, Shuting Xiang, Yueqiang Cao, Ilkeun Lee, Xinggui Zhou, De‐en Jiang, Anatoly I. Frenkel, Francisco Zaera

Bibliographic record

VenueACS Catalysis · 2024
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsCanadian Light Source (Canada)
FundersChemical Sciences, Geosciences, and Biosciences DivisionArgonne National LaboratoryBasic Energy SciencesU.S. Department of EnergyDivision of ChemistryFundamental Research Funds for the Central UniversitiesOffice of ScienceCanadian Light SourceNational Science Foundation
KeywordsCatalysisAlloyPlatinumChemistryMaterials scienceInorganic chemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide With heterogeneous catalysts, chemical promotion takes place at their surfaces. Even in the case of single-atom alloys, where small quantities of a reactive metal are dispersed within the main host, it is assumed that both elements are exposed and available to bond with the reactants. Here, we show, on the basis of in situ X-ray absorption spectroscopy data, that in alloy catalysts made from Pt highly diluted in Cu the Pt atoms are located at the inner interface between the metal nanoparticles and the silica support instead. Kinetic experiments indicated that these catalysts still display better selectivity for the hydrogenation of unsaturated aldehydes to unsaturated alcohols than the pure metals. Density functional theory calculations corroborated the stability of Pt at the metal–support interface and explained the catalytic performance as being due to a remote lowering of the activation barrier for the dissociation of H 2 at Cu sites by the internal Pt atoms.

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 score0.002
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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