Exploration of Surface Properties of Engineered Sponges for Effective Marine Oil Spill Cleanup
Bibliographic record
Abstract
Removing emulsified oil from seawater is challenging. Superwetting foams developed using afacile dip coating technique have emerged as a promising approach. They allow for the manipulation of surface properties to remove free and emulsified oil from seawater. Herein, new surface engineered sponges have been developed to remove different crude oils from seawater. These coatings were developed to improve performance for different oils and understand the influence of different factors on oil removal efficacy: surface chemistry, surface roughness and electrostatic compatibility. The developed materials displayed over 99% removal efficacy for light, conventional, and heavy crude oil emulsified in water, across a wide range of environmental conditions, including pH, temperature, and salinity. This approach is simple, scalable, and economical with the potential to address large-scale marine oil spills. The insights provided can provide researchers an understanding of pivotal surface properties necessary to drive emulsified crude oil adsorption when developing future materials.
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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) | 0.001 | 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".