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Record W4214620521 · doi:10.1007/s12274-022-4196-7

Synthesis of cobalt single atom catalyst by a solid-state transformation strategy for direct C-C cross-coupling of primary and secondary alcohols

2022· article· en· W4214620521 on OpenAlexaff
Zhijun Li, Yuying Chen, Xiaowen Lu, Honghong Li, Leipeng Leng, Tinglei Zhang, J. Hugh Horton

Bibliographic record

VenueNano Research · 2022
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsCatalysisYield (engineering)CobaltDensity functional theoryMaterials scienceCombinatorial chemistryChemistryPhotochemistryNanotechnologyComputational chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Atomic engineering of single atom catalysts (SACs) with high-density available active sites and optimized electronic properties can substantially boost catalytic efficacy. Herein, we report a solid-state transformation strategy to access Co SACs by introducing Co species from commercial Co2O3 powders into nitrogen-doped carbon support. The catalyst exhibited excellent catalytic activity, with a turnover frequency (TOF) of 2,307 h−1 and yield of 95%, in the direct C-C cross-coupling of benzyl alcohol and 1-phenylethanol (1 atm O2@80 °C) to yield chalcone. Density functional theory (DFT) calculations demonstrate the coordination environment and electronic metal-support interaction impact the catalytic pathway. In particular, a wide substrate scope and a broad functional-group tolerance of this SAC were validated, and the employment of this strategy for large-scale synthesis was also shown to be feasible. This work might shed light on the facile and scalable synthesis of highly active, selective, and stable SACs for heterogeneous catalysis.

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)0.002
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 score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.049
GPT teacher head0.349
Teacher spread0.300 · 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

Citations26
Published2022
Admission routes1
Has abstractyes

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