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Record W4205364322 · doi:10.1002/smll.202106614

Atomically Defined Undercoordinated Copper Active Sites over Nitrogen‐Doped Carbon for Aerobic Oxidation of Alcohols

2022· article· en· W4205364322 on OpenAlexaff

Bibliographic record

VenueSmall · 2022
Typearticle
Languageen
FieldChemistry
TopicOxidative Organic Chemistry Reactions
Canadian institutionsQueen's University
FundersChina Postdoctoral Science Foundation
KeywordsCatalysisAlcohol oxidationCopperCinnamyl alcoholOxidation stateCarbon fibersSubstrate (aquarium)Heterogeneous catalysis

Abstract

fetched live from OpenAlex

Abstract Selective aerobic oxidation of alcohols offers an attractive means to address challenges in the modern chemical industry, but the development of non‐noble metal catalysts with superior efficacy for this reaction remains a grand challenge. Here, this study reports on such a catalyst based on atomically defined undercoordinated copper atoms over nitrogen‐doped carbon support as an efficient, durable, and scalable heterogeneous catalyst for selective aerobic oxidation of alcohols. This catalyst exhibits extremely high intrinsic catalytic activity (TOF of 7692 h−1) in the oxidation of cinnamyl alcohol to afford cinnamaldehyde, along with exceptional recyclability (at least eight cycles), scalability, and broad substrate scope. DFT calculations suggest that the high activity derives from the low oxidation state and the unique coordination environment of the copper sites in the catalyst. These findings pave the way for the design of highly active and stable single atom catalysts to potentially address challenges in synthetic chemistry.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.251
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2022
Admission routes1
Has abstractyes

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