Lipase‐catalyzed esterification of cinnamic acid and oleyl alcohol in organic solvent media
Bibliographic record
Abstract
Abstract The esterification of cinnamic acid (CA) and oleyl alcohol (OA) in organic solvent media by immobilized lipase Novozym 435 was optimized in terms of selected parameters, including the logarithm of the 1‐octanol/water partition coefficient of the organic solvent (log P, 0.29–4.5), initial water activity (aw, 0.05–0.75), agitation speed (0–200 rpm), temperature (35–65 °C) and ratio of substrates (CA/OA, 1.0:0.5–1.0:6.0). The results showed that the more hydrophobic solvent mixtures and lower initial aw values resulted in a higher enzymatic activity and bioconversion yield. The most appropriate solvent medium and initial aw value was the mixture of iso‐octane/2‐butanone (85:15, v/v) and 0.05, respectively. The results also showed that an agitation speed of 150 rpm and a reaction temperature of 55 °C were optimal for the reaction system. The activation energy (Ea) of the esterification reaction was calculated as 43.6 kJ mol−1. The optimal ratio of CA to OA was 1.0:6.0, with the absence of any inhibition by OA. Using the optimized conditions, the maximum enzymatic activity was 390.3 nmol g−1 min−1, with a bioconversion yield of 100% after 12 days of reaction. In addition, the electrospray ionization‐mass spectroscopy analysis confirmed that the major end product of the esterification reaction was oleyl cinnamate. Copyright © 2005 Society of Chemical Industry
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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) | 0.000 | 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".