Preparation and Application of Single‐Atom Cobalt Catalysts in Organic Synthesis and Environmental Remediation
Bibliographic record
Abstract
The development of high-performance catalysts plays a crucial role in facilitating chemical production and reducing environmental contamination. Single-atom catalysts (SACs), a class of catalysts that bridge the gap between homogeneous and heterogeneous catalysis, have garnered increasing attention because of their unique activity, selectivity, and stability in many pivotal reactions. Meanwhile, the scarcity of precious metal SACs calls for the arrival of cost-effective SACs. Cobalt, as a common non-noble metal, possesses tremendous potential in the field of single-atom catalysis. Despite their potential, reviews about single-atom Co catalysts (Co-SACs) are lacking. Accordingly, this review thoroughly summarized various preparation methodologies of Co-SACs, particularly pyrolysis; its application in the specific domain of organic synthesis and environmental remediation is discussed as well. The structure-activity relationship and potential catalytic mechanism of Co-SACs are elucidated through some representative reactions. The imminent challenges and development prospects of Co-SACs are discussed in detail. The findings and insights provided herein can guide further exploration and development in this charming area of catalyst design, leading to the realization of efficient and sustainable catalytic processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".