Mechanically mixed <scp>ZnO‐Al<sub>2</sub>O<sub>3</sub></scp> catalysts in the synthesis of propylene carbonate via alcoholysis of urea
Bibliographic record
Abstract
Abstract Alcoholysis of urea with 1,2‐propylene glycol (PG) is a potential industrial process for the synthesis of propylene carbonate (PC) with many advantages. However, the preparation of industrial catalysts such as Zn‐Al oxide by the co‐precipitation method will inevitably produce a large amount of salty water and building desalting equipment will increase the investment cost. The present work attempts to use a mechanically mixed catalyst consisting of commercial ZnO and Al 2 O 3 to solve these problems. This type of catalyst was capable of catalyzing the reaction more than 5 times. The PC yield was gradually increased with each use. The best PC yield of 96.7% is comparable to the co‐precipitated Zn‐Al oxide catalysts and was achieved during the third use. In the reaction, ZnO and Al 2 O 3 were dissolved into the reactants to form complexes, which homogenously catalyzed the reaction. It was found that the dissolved Zn and Al complex significantly influenced the PC yield and re‐precipitated in the late stage of the reaction. Furthermore, the partial dissolution of Al assisted the dissolution and precipitation of Zn, which improved the PC yield. After several dissolution‐precipitation cycles, ZnO‐Al 2 O 3 was homogeneously mixed at the atomic scale. Interestingly, there was an adequate linear relationship between the amount of dissolved Zn and Al in each reaction, the linear correlation coefficient improved after each reaction, and the slope of the line (the ratio of dissolved Zn/Al) was 7.26 in the third use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".