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Record W2343839223 · doi:10.1039/c6cp01100d

CO oxidation by MoS<sub>2</sub>-supported Au<sub>19</sub> nanoparticles: effects of vacancy formation and tensile strain

2016· article· en· W2343839223 on OpenAlexaff
Soonho Kwon, Kihyun Shin, Kihoon Bang, Hyun You Kim, Hyuck Mo Lee

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation of Korea
KeywordsVacancy defectNanoparticleDensity functional theoryStrain (injury)Materials scienceCatalysisTensile strainPhysical chemistryUltimate tensile strengthChemical physicsNanotechnologyChemical engineeringComputational chemistryCrystallographyChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The mechanism of the catalytic oxidation of CO activated by MoS2-supported Au19 nanoparticles (NPs) was studied using density functional theory calculations. Of particular interest were the effects of the physical/chemical modification of a MoS2 support on the CO oxidation pathway and the activation of specific reactive centers, i.e., the Au atoms of Au19 or the Au-MoS2 perimeter sites. We systematically modified MoS2 by introducing an S vacancy or 5% tensile strain and studied the shift of each reaction step and the overall change in the reaction pathway and activity. Despite the lack of direct involvement of the Au-MoS2 perimeter in the reaction, the combination of an S vacancy and the tensile strain in the MoS2 support was found to improve the stability and catalytic activity of Au NPs for CO oxidation. The results show that support modification can provide information for new pathways for the rational design of Au-based catalysts.

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)0.001
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.227
Teacher spread0.221 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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