A Heterogeneous Single Atom Cobalt Catalyst for Highly Efficient Acceptorless Dehydrogenative Coupling Reactions
Bibliographic record
Abstract
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 VO bonding and the formation of CoO 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".