Biodegradable lignin surfactant disperses oil spills with droplet dynamics mapped by AutoDrop algorithm
Bibliographic record
Abstract
The development of environmentally benign dispersants is critical for mitigating the ecological impact of marine oil spills. This study introduces a novel dispersant that integrates PEG-functionalized lignin with biosurfactant of lecithin and Tween 80. The key advancements are (1) the first use of PEG-functionalized lignin blended with lecithin/Tween 80, (2) the development of the AutoDrop Algorithm for automated, precise droplet analysis, and (3) a comprehensive eco-toxicological profile against commercial dispersants. The optimized formulations, L+T/DAL-Oz-PEG400, demonstrated superior performance, effectively suppressing oversized droplet formation while maintaining high emulsification efficiency. In Hibernia and Hebron crude oils, over 90 % of droplets remained below 12.87 μm and 9.96 μm, respectively. Emulsion stability was evaluated using tail index (TI) and generalized Pareto distribution (GPD), with results showing TI = 1.74, ξ = 0.08, and σ = 4.83 for Hibernia oil, and TI = 2.96, ξ = 0.26, and σ = 10.67 for Hebron oil. Interfacial tension was reduced to 9.33 mN/m and 9.01 mN/m in Hibernia and Hebron oils, respectively. Crucially, the blend showed near-zero mortality in Artemia salina and Atlantic cod larvae, sharply contrasting the Corexit 9500 toxicity. This study established a new approach for designing effective, low toxicity dispersants through chemical modification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".