Highly selective oxidation of toluene using air over [Fe(III)TPP]Cl supported on chitosan
Bibliographic record
Abstract
The highly selective catalytic oxidation of toluene with air to benzaldehyde and benzyl alcohol in the liquid phase has been studied with the use of iron tetraphenylporphyrin (Fe TPP) supported on chitosan (CTS), (Fe TPP/CTS). The rates of toluene conversion and selectivity (aldehyde + alcohol) were subject to the reaction temperature, air pressure, and amount of iron tetraphenylporphyrin. By the use of the Fe TPP/CTS, containing 2 mg of iron tetraphenylporphyrin as catalyst, toluene oxidation with air under the optimum conditions of 190 °C and 0.6 MPa produced benzaldehyde and benzyl alcohol at 90% selectivity and 5.4% conversion of toluene, the mole turnover of the catalyst was about 6 × 106, and it could be reused efficiently for one more time. It is suggested that the amine groups (–NH2) on the chitosan chain act as a key assistor to the catalysis of Fe TPP for toluene oxidation. Compared with the conventional method of synthesizing benzaldehyde and benzyl alcohol, the new method has the advantages of reduced environmental pollution, higher selectivity for the two main products, and easy recovery of the catalyst.Key words: chitosan-supported iron porphyrin, catalysis, toluene oxidation, benzaldehyde, benzyl alcohol.
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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".