A Novel Biosurfactant-Based Oil Spill Response Dispersant for Efficient Application under Temperate and Arctic Conditions
Bibliographic record
Abstract
Synthetic oil spill dispersants have become essential in offshore oil spill response strategies. However, their use raises significant concerns regarding toxicity to phyto- and zooplankton and other marine organisms, especially in isolated and vulnerable areas such as the Arctic and shorelines. Sustainable alternatives may be developed by replacing the major active components of commercial dispersants with their natural counterparts. During this study, interfacial properties of different types of glycolipid-based biosurfactants (rhamnolipids, mannosylerythritol lipids, and trehalose lipids) were explored in a crude oil-seawater system. The best-performing biosurfactant was further mixed with different nontoxic components of Corexit 9500A, and the interfacial properties of the most promising dispersant blend were further explored with various types of crude oils, weathered oil, bunker, and diesel fuel in natural seawater. Our findings indicate that the most efficient dispersant formulation was achieved when mannosylerythritol lipids (MELs) were mixed with Tween 80 (T). The MELs-T dispersant blend significantly reduced the interfacial tension (IFT) of various crude oils in seawater with results comparable to those obtained with Corexit 9500A. Importantly, no leaching or desorption of MELs-T components from the crude oil-water interface was observed. Furthermore, for weathered and more viscous asphaltenic bunker fuel oil, IFT results with the MELs-T dispersant blend surpassed those obtained with Corexit 9500A. This dispersant blend also demonstrated effectiveness at different dosages (dispersant-to-oil ratio (DOR)) and under various temperature conditions. The efficacy of the MELs-T dispersant was further confirmed by standard baffled flask tests (BFTs) and Mackay-Nadeau-Steelman (MNS) tests. Overall, our study provides promising data for the development of effective biobased dispersants, particularly in the context of petroleum exploitation in subsea resources and transportation in the Arctic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".