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Record W4319826827 · doi:10.1002/smll.202207941

A Heterogeneous Single Atom Cobalt Catalyst for Highly Efficient Acceptorless Dehydrogenative Coupling Reactions

2023· article· en· W4319826827 on OpenAlexaff
Zhijun Li, Xiaowen Lu, Rufang Zhao, Siqi Ji, Mingyang Zhang, J. Hugh Horton, Yang Wang, Qian Xu, Junfa Zhu

Bibliographic record

VenueSmall · 2023
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsQueen's University
Fundersnot available
KeywordsCatalysisCobaltVanadiumMaterials scienceAtom (system on chip)Density functional theoryOxideMetalChemical engineeringInorganic chemistryChemistryComputational chemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract A fundamental understanding of metal active sites in single‐atom catalysts (SACs) is important and challenging in the development of high‐performance catalyst systems. Here, a highly efficient and straightforward molten‐salt‐assisted approach is reported to create atomically dispersed cobalt atoms supported over vanadium pentoxide layered material, with each cobalt atom coordinated with four neighboring oxygen atoms. The liquid environment and the strong polarizing force of the molten salt at high temperatures potentially favor the weakening of VO bonding and the formation of CoO bonding on the vanadium oxide surface. This cobalt SAC achieves extraordinary catalytic efficiency in acceptorless dehydrogenative coupling of alcohols with amines to give imines, with more than 99% selectivity under almost 100% conversion within 3 h, along with a high turnover frequency (TOF) of 5882 h −1 , exceeding those of previously reported benchmarking catalysts. Moreover, it delivers excellent recyclability, reaction scalability, and substrate tolerance. Density functional theory (DFT) calculations further confirm that the optimized coordination environment and strong electronic metal‐support interaction contribute significantly to the activation of reactants. The findings provide a feasible route to construct SACs at the atomic level for use in organic transformations.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.249
Teacher spread0.222 · 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

Citations34
Published2023
Admission routes1
Has abstractyes

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