Investigation of enzymatic biodiesel production using ionic liquid as a co‐solvent
Bibliographic record
Abstract
Abstract In this study, the lipase catalysed esterification reaction for biodiesel production was investigated in the presence of the ionic liquid [BMIM][PF6]. Unlike regular organic solvents, many ionic liquids have no vapour pressure, and are therefore considered non‐volatile. When used in systems with enzyme catalysts, ionic liquids may enhance their activity, selectivity, and stability. The use of an enzyme (lipase) as a catalyst, and the ionic liquid as a solvent/immobilization agent also represents an environmentally friendly, “green” technology. Methyl acetate was used as the acyl acceptor as opposed to the more commonly used methanol due to the negative effects methanol and the glycerol by‐product has on lipase enzyme activity. The results of this research indicate that methyl oleate (i.e., biodiesel) was successfully produced, with an 80% overall biodiesel yield in the presence of ionic liquid, at a 1:1 ratio (v/v) to the amount of oil. This verified that the presence of an ionic liquid, at a specified amount, improved the activity of the lipase and the overall biodiesel yield. Results also indicate the addition of ionic liquid facilitated the separation of the methyl esters from the triacetylglycerol by‐product. The best conditions investigated was found to be: 14:1 molar ratio between oil and acyl acceptor; 20% (w immobilised lipase/w of oil; and a temperature in the range of 48–55°C. However, additional purification is required in order for the produced biodiesel to meet ASTM standards.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".