Dispersion of Weathered Biodiesel, Diesel, and Light Crude Oil in the Presence of Sophorolipid Biosurfactant in Seawater
Bibliographic record
Abstract
Sophorolipid biosurfactants have shown promise in remediation of oil-contaminated environments. This study investigated the potential application of sophorolipid biosurfactants for enhanced dispersion of weathered biodiesel, diesel, and light crude oil–contaminated water under salinities (0, 10, 20, and 30 ppt), temperatures (8, 22, and 35°C), and pH (6–8) using bench-scale experiments. Solutions of oil-artificial seawater-sophorolipid were prepared, shaken, solvent extracted, and analyzed. Sophorolipid biosurfactants reduced the surface tension of seawater (30 ppt) to 34 mN/m with a critical micelle concentration of 38 mg/L. Results of dispersion experiments showed that oil dispersion significantly increased as the sophorolipid concentrations increased so that the biodiesel, diesel, and light crude oil dispersion in seawater was enhanced 27, 16, and 12%, respectively, by 80 mg/L of sophorolipid. Salinity, mixing, and temperature influenced the oil dispersion, but pH had the least effect on the oil dispersion. Oil dispersion was because of the decreases in the surface and interfacial tensions and encapsulation of oil droplets in micelles. This study suggests that sophorolipid has the properties to enhance oil dispersion in seawater under the examined laboratory conditions.
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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".