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Record W4293724036 · doi:10.1002/anie.202207524

Selectively Coupling Ru Single Atoms to PtNi Concavities for High‐Performance Methanol Oxidation via <i>d</i>‐Band Center Regulation

2022· article· en· W4293724036 on OpenAlexafffund
Fanpeng Kong, Xiaozhi Liu, Yajie Song, Zhengyi Qian, Junjie Li, Lei Zhang, Geping Yin, Jiajun Wang, Dong Su, Xueliang Sun

Bibliographic record

VenueAngewandte Chemie International Edition · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsWestern University
FundersFundamental Research Funds for the Central UniversitiesWestern UniversityChina Postdoctoral Science FoundationNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsCatalysisAtom (system on chip)NanoparticleMethanolFourier transform infrared spectroscopyAtomic layer depositionMetalChemistryMaterials scienceCoupling (piping)DiffusionNanotechnologyChemical engineeringLayer (electronics)PhysicsComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Single atom tailored metal nanoparticles represent a new type of catalysts. Herein, we demonstrate a single atom‐cavity coupling strategy to regulate performance of single atom tailored nano‐catalysts. Selective atomic layer deposition (ALD) was conducted to deposit Ru single atoms on the surface concavities of PtNi nanoparticles (Ru‐ca‐PtNi). Ru‐ca‐PtNi exhibits a record‐high activity for methanol oxidation reaction (MOR) with 2.01 A mg −1 Pt . Also, Ru‐ca‐PtNi showcases a significant durability with only 16 % activity loss. Operando electrochemical Fourier transform infrared spectroscopy (FTIR) and theoretical calculations demonstrate Ru single atoms coupled to cavities accelerate the CO removal by regulating d ‐band center position. Further, the high diffusion barrier of Ru single atoms in concavities accounts for excellent stability. The developed Ru‐ca‐PtNi via single atom‐cavity coupling opens an encouraging pathway to design highly efficient single atom‐based (electro)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)0.000
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 score0.045
Threshold uncertainty score0.992

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 head0.012
GPT teacher head0.229
Teacher spread0.216 · 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

Citations126
Published2022
Admission routes2
Has abstractyes

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