Generation of oil spill dispersants composed of biosurfactants and chemical surfactants: Mechanism exploration through molecular dynamics simulation
Bibliographic record
Abstract
Dispersants (i.e., chemical surfactants mixed with solvents) are widely used for oil spill response. Sustainable dispersants are continuously desired to minimize potential environmental concerns. There is thus an increasing demand for the generation of more efficient and eco-friendly alternatives. The combination of biosurfactants (BS) and chemical surfactants in dispersants to improve efficiency, along with the optimal method and mechanism for their blending, remains unclear. This study thus focused on the investigation of next-generation dispersant production by combining bio- and chemical surfactants and the evaluation of their interaction mechanism through molecular dynamics simulation (MD). Two BS (i.e., rhamnolipid and surfactin) were combined with one of the following chemical surfactants (i.e., polyoxyethylene sorbitan monooleate (Tween 80), polyoxyethylenesorbitan trioleate, sorbitan monooleate, sorbitan monolaurate, or dioctyl sulfosuccinate sodium) in various ratios (i.e., 8:2, 6:4, 4:6, 2:8 (v/v)). The results showed that a blend of BS with Tween 80 was effective in dispersing hexadecane (C16) at ratios (6:4, 4:6 and 2:8). The BS combination within the 6:4 ratio was further investigated and revealed that a mixture of 50:50 of BS produced droplets with an average size of 168 nm. In addition, we screened the dispersant composition in alcoholic solvents across different salinity, pH, and temperature conditions. The n -propanol-based dispersant proved better dispersion efficiency than Corexit 9500 A when treating Alaska North Slope oil in seawater at the dispersant-to-oil ratios (e.g., 1:10, 1: 25) and mixing energy (e.g., 200 rpm). MD revealed strong interactions between surfactants in the 6:4 ratio at the C16/water interface. These interactions were facilitated by hydrogen bonding between surfactants' functional groups and both water and n -propanol molecules. This research provides insights for designing next-generation biosurfactant-aided dispersants with enhanced oil dispersion efficiency. • Dispersant formulation was explored by mixing biosurfactants and chemical surfactants. • The selected dispersant with optimal mixing achieved an average droplet size of 168 nm. • Stable dispersion was achieved under various salinities, pH, and temperatures. • N-propanol as solvent led to compatible oil dispersion efficiency with Corexit 9500. • Molecular dynamics simulation discovered dispersion mechanism in oil emulsification.
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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".