Application of Phase-Selective Organogelators (PSOGs) for Marine Oil Spill Remediation
Bibliographic record
Abstract
Oil spill incidents frequently cause serious impacts on the ecosystem, society, and economy. To cope with this problem, the use of phase-selective organogelators (PSOGs) has been developed in recent years as a promising oil spill response tool, which can congeal oils from biphasic systems, accelerating oil removal and recovery and reducing impacts on the environment. This article systematically reviews reported technologies for the use of PSOGs for potential oil spill remediation in the literature from January 2016 to May 2022. It discusses several kinds of PSOGs based on molecular structures which are possible for gelling oil in the presence of water/seawater. Their mechanisms for phase-selective gelation are summarized, including hydrogen bonding, π–π stacking interactions, van der Waals force, hydrophobic interactions, etc. The currently possible deployment methods for the application of PSOGs are explored; carrier solvents and powder form are frequently used. Moreover, the challenges and the corresponding recommendations regarding standardized testing protocols and evaluation framework, gelation selectivity, as well as challenges in field tests, are further discussed.
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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 | 0.001 | 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 | 0.000 |
| 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".