Effect of nanobubbles on the mobilization of microplastics in shorelines subject to seawater infiltration
Bibliographic record
Abstract
The widespread presence of microplastics (MPs) in the ocean has varying degrees of impact on ecosystems and even human health. Coastal tidal zones are crucial in controlling the movement of MPs, which are influenced by waves and tidal forces. Meanwhile, natural nanobubbles (NBs) in the ocean can affect the hydrodynamic properties of the tidal zone. The mobilization of MPs in coastal tidal zones under the effect of NBs has been less studied. In this study, we explored natural NBs' influence on the mobilization of MPs in shorelines subject to seawater infiltration. Using glass beads as a substrate, a coastal porous environment was constructed through column experiments, and the pump-controlled water flow was used to study the transport of MPs subject to seawater movement within the substrate. The infiltration of MPs under continuous and transient conditions, as well as the upward transport induced by flood tide, were considered. The role of salinity in the interactions between NBs, MPs, and substrates was evaluated. Salinity altered the energy barriers between particles, which in turn affected the movement of MPs within the substrate. In addition, hydrophilic MPs were more likely to infiltrate within the substrate and had different movement patterns under continuous and transient flow conditions. The motion of the MPs within the substrate varied with flow rate, and NBs limited the vertical movement of MPs in the tidal zone. It was also observed that NBs adsorbed readily onto substrates, altering the surface properties of substrates, particularly their ability to attach and detach from other substances.
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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".