Biosurfactant-based dispersants for oil spill remediation: Salinity effects and mechanistic insights
Bibliographic record
Abstract
Salinity variations, particularly in dynamic environments such as estuaries, may significantly affect the dispersion effectiveness (DE) of oil spill dispersants. While bio-based dispersants are gaining attention as alternatives to chemical dispersants, limited research exists on their adaptability to varying salinity conditions and their associated molecular mechanism and ecological impact. This study evaluated the DE of bio-based dispersants formulated with two biosurfactants (BS); Surfactin (SUC) and Rhamnolipid (RAM), either individually or in combination with Tween 80 (TWE), referred to as BS/TWE, across salinity levels of 10, 20, and 34 psu. The DE of each bio-based dispersant was compared to the chemical dispersant Corexit 9500A under various environmental conditions, including dispersant-to-oil ratios, temperature variations, and mixing energy levels simulating turbulence in natural aquatic environments. The SUC-based dispersant achieved high DE (88 %) with smallest oil droplets size around 5.08 μm at 10 psu but exhibited reduced performance at 34 psu. In contrast, the BS/TWE dispersant showed 90 % DE with droplets size of 10.45 μm at 34 psu, as a result of synergistic surfactant interactions. Molecular dynamics simulation revealed that salinity affects surfactant-water interactions, with SUC-based dispersant losing efficiency at high salinity due to lack of ion bridging, while BS/TWE dispersant remaining effective through reduced electrostatic interactions. Toxicity assessments exhibited minimal inhibitory effects of bio-based dispersants on algal growth, Dunaliella tertiolecta, highlighting their potential for environmental applications. The findings highlight the potential of these dispersants as effective, and environmentally friendly solutions for oil spill response in diverse marine environments.
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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 | 0.001 |
| 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".