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Record W4396892249 · doi:10.1002/ange.202407025

Tuning Adsorbate‐Mediated Strong Metal‐Support Interaction by Oxygen Vacancy: A Case Study in Ru/TiO<sub>2</sub>

2024· article· en· W4396892249 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAngewandte Chemie · 2024
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsWestern University
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsOxygenMetalChemistryVacancy defectChemical physicsMaterials scienceCrystallographyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The adsorbate‐mediated strong metal‐support interaction (A‐SMSI) offers a reversible means of altering the selectivity of supported metal catalysts, thereby providing a powerful tool for facile modulation of catalytic performance. However, the fundamental understanding of A‐SMSI remains inadequate and methods for tuning A‐SMSI are still in their nascent stages, impeding its stabilization under reaction conditions. Here, we report that the initial concentration of oxygen vacancy in oxide supports plays a key role in tuning the A‐SMSI between Ru nanoparticles and defected titania (TiO 2‐x ). Based on this new understanding, we demonstrate the in situ formation of A‐SMSI under reaction conditions, obviating the typically required CO 2 ‐rich pretreatment. The as‐formed A‐SMSI layer exhibits remarkable stability at various temperatures, enabling excellent activity, selectivity and long‐term stability in catalyzing the reverse water gas‐shift reaction. This study deepens the understanding of the A‐SMSI and the ability to stabilize A‐SMSI under reaction conditions represents a key step for practical catalytic applications.

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.

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 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 score0.008
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.274
Teacher spread0.253 · 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