Application of green solvent for biodiesel production from sesame oil
Bibliographic record
Abstract
The use of abundant biosources for biodiesel production has attracted the attention of various academicians, as well as the industrial communities. This work provides an alternative biodiesel-manufacturing process that uses green chemistry principles in the form of deep eutectic solvents (DESs) to eliminate use of hazardous chemicals. Three different types of DES mixtures, glycerol-based (DES 1), urea-based (DES 2) and oxalic-acid-based (DES 3), were used with sesame oil. The laboratory-scale results showed that DES 1 and DES 2 were more effective for improving the yield of fatty acid methyl esters. Furthermore, a molar ratio of 6:1 and a contact time of 60 min proved to be optimum conditions. It was possible to get around 12% higher yield compared with the non-DES process under optimum conditions, suggesting that DESs can be used effectively as a co-solvent. The properties of purified biodiesel met the ASTM D 6751-19 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 | 0.000 | 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".