<scp>NHPI</scp> catalyzed air oxidation of cyclohexanone: Mechanism and kinetics study
Bibliographic record
Abstract
Abstract As an important monomer material for synthesizing polycaprolactone, ε‐caprolactone (ε‐CL), is currently produced through the Baeyer–Villiger oxidation of cyclohexanone. As a green and promising process to produce ε‐CL, oxidizing cyclohexanone with air in the presence of sacrificial agents has attracted extensive attention from academia and industry. However, the slow reaction kinetic and the weak oxidation capacity of air limits the application of this green technology. Herein, we used N‐hydroxyphthalimide (NHPI) as a higdhly efficient homogeneous catalyst for the air oxidation of cyclohexanone. Through investigating the influence of reaction conditions such as temperature, gas velocity, aldehyde‐ketone ratio, and solvent ratio on the conversion rate of cyclohexanone and selectivity of ε‐CL, an optimum reaction condition was obtained and the ε‐caprolactone production rate is as high as 3.43 mmol/gcat/min with a selectivity of 90%. The reaction mechanism is investigated through in‐situ UV–visible spectra, electron paramagnetic resonance (EPR) spectra, and starch/KI experiment, and a plausible mechanistic mode involving three pivotal reactions was proposed. The dynamic model was constructed to validate the proposed mechanism and provide good predictions for the actual reaction rate. This work provides meaningful insights into NHPI‐catalyzed air oxidation of cyclohexanone, and the constructed kinetic model provides theoretical guidance for scale‐up 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) | 0.000 | 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) | 0.002 | 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".