Glycerol transesterification with ethyl acetate to synthesize acetins using ethyl acetate as reactant and entrainer
Bibliographic record
Abstract
Transesterification of glycerol with ethyl acetate was performed over acidic catalysts in the batch and semi-batch systems. Ethyl acetate was used as reactant and entrainer to remove the produced ethanol during the reaction, through azeotrope formation. Since the azeotrope of ethyl acetate and ethanol forms at 70 oC, all the experiments were performed at this temperature. Para-toluene sulfonic acid, sulfuric acid, and Amberlyst 36 were used as catalyst. The effect of process parameters including ethyl acetate to glycerol molar ratio (6-12), reaction time (3-9 h), and the catalyst to glycerol weight (2.5-9.0%), on the conversion and products selectivities were investigated. Under reflux conditions, 100% glycerol conversion was obtained with 45%, 44%, and 11% selectivity to monoacetin, diacetin, and triacetin, respectively. Azeotropic reactive distillation led to 100% conversion of glycerol with selectivities of 3%, 48% and 49% for monoacetin, diacetin, and triacetin. During the azeotropic reactive distillation, it was possible to remove ethanol to shift the equilibrium towards diacetin and triacetin. Therefore, the total selectivity to diacetin and triacetin was increased from 55% to 97% through azeotropic distillation.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".