Optimization of lipase‐catalyzed sorbitol monoester synthesis in organic medium
Bibliographic record
Abstract
Abstract The influence of acid/polyol molar ratio and reaction time on lipase‐catalyzed esterification of oleic acid (OA) and sorbitol was studied to determine optimal conditions for monoester synthesis. A simple mathematical model was developed to determine relationships between various parameters of technical and/or economical importance. A direct relationship. independent of OA initial concentration and reaction time, was shown between the percentage of monoester in total esters, monoester concentration (the maximum was 25–30 mM for 70–80% monoester), and sorbitol conversion rate. A high sorbitol conversion was always associated with a low percentage of monoester in total ester. No absolute optimum could be found, so that compromises should be chosen, with the help of the results presented herein, depending on the constraints on the process. Two possible optima are proposed. In both examples, monoester (25–30 mM) is 80% pure. In the first case, productivity is maximized (15 mmol·L−1·h−1), but OA and sorbitol conversions are only 40 and 60%, respectively. In the second case, a high OA conversion (>99%) is favored at the expense of monoester productivity (1.8 mmol·L−1·h−1), 60% of the sorbitol being converted. It was shown that, although sorbitol monoester has better surface properties than diester, the addition of 20% diester did not modify the interfacial activity of monoester and slightly increased its surface activity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".