Two Birds with One Stone: A Dual-Functional Washing Fluid for Shoreline Oil Spill Response
Bibliographic record
Abstract
The gigantic petroleum exploration and production are threatening the lives and well-being of people and other species. Pizeocatalysis is a bridge to transfer ceaseless mechanical energy to chemical energy and thus triggers redox reactions. In this study, piezoelectric MoS 2 was used as a surface-washing fluid to physically and chemically decontaminate oiled sands. The MoS 2 displayed single- and few-layer structures, which were conducive to piezocatalysis. The addition of MoS 2 could decrease the interfacial tension, thereby facilitating oil removal. The oil removal efficiency varied under different environmental conditions. Quenching experiments with different scavengers and electron paramagnetic resonance (EPR) spectra showed the existence of reactive oxygen species (ROS) including 1 O 2, • O 2 –, and • OH. It was found that shaking was a necessary initiator to trigger piezocatalysis and singlet oxygen ( 1 O 2 ) was the dominant ROS. The MoS 2 fluid exhibited good stability, maintaining high removal efficiency after five reuse cycles. Notably, results indicated that high-molecular-weight hydrocarbons were degraded into lower-molecular-weight ones, indicating the existence of chemical oxidation pathways in oil removal. This study provides a facile self-powered piezocatalytic surface-washing process by harnessing natural energy from water agitation to clean up oiled sands.
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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 | 0.001 | 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".