The catalytic Baeyer–Villiger oxidation of cyclohexanone to ε‐caprolactone over cerium–tin oxide
Bibliographic record
Abstract
Abstract Polycaprolactone synthesized via the ring‐opening polymerization of ε‐caprolactone assumes an irreplaceable and pivotal role in diverse fields including biology, medicine, chemical engineering, and environmental science. Herein, a series of cerium–tin oxide catalysts, denoted as Ce‐Sn(x: y)‐T, were synthesized via the co‐precipitation method. The structure of the catalysts was characterized by means of X‐ray diffraction, X‐ray photoelectron spectroscopy, N₂ adsorption–desorption measurement, transmission electron microscopy, and so forth. The catalytic activity of Ce‐Sn (x: y)‐T was evaluated in the reaction of synthesizing ε‐caprolactone via the oxidation of cyclohexanone with oxygen, and the optimal cerium–tin ratio and calcination temperature conditions for the preparation of the catalyst were obtained. The results indicated that there was a synergistic effect between Ce and Sn in the cerium–tin catalysts. Furthermore, a portion of Sn could incorporate into the lattice of CeO 2 to form a cerium–tin solid solution, which increased the ratio of Ce 3+ /Ce 4+ and the content of oxygen vacancies in the catalyst, thus improving the oxidation performance of the catalyst. Among them, the catalyst Ce‐Sn(2:1)‐500 exhibited the best catalytic performance. Under the optimal reaction conditions, the conversion rate of cyclohexanone could reach 91.41%, and the selectivity of ε‐caprolactone was 91.46%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".