Inorganic‐organic hybrid material based on amine‐functionalized zeolite Y: A study of catalytic activity in transesterification
Bibliographic record
Abstract
Three different structures of amine compound, 3‐aminopropyl trimethoxysilane (N1), N‐[3‐(trimethoxysilyl)propyl] ethylenediamine (N2), and N‐[3‐(trimethoxysilyl)propyl] diethylenetriamine (N3), functionalized on zeolite Y, were used to enhance catalyst lifetime in the transesterification reaction. The presence of amine compound was confirmed by FTIR. Moreover, the grafting percentage of amine on zeolite Y was also considered, since it affected the basicity and basic strength of the catalyst. The catalytic activity testing for transesterification of glyceryl tributyrate (GTB) and methanol was carried out in an autoclave reactor at reaction conditions of 30:1 molar ratio of methanol to GTB and a catalyst loading level of 0.35 g/g (35 mass% of GTB). The highest methyl butyrate production per active site was obtained by N3, due to its having the highest basic strength. The highest methyl butyrate yield was given by N1, due to its having the highest amino group concentration (basicity). Therefore, the catalytic activity, in terms of methyl butyrate yield and methyl butyrate production per active site, was affected by both basic strength and basicity. The reusability of the catalysts showed that the methyl butyrate yield slightly declined to ∼13.8 % yield after four cycles, but the methyl butyrate production per active site remained constant across four cycles.
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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) | 0.000 | 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 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".