Overlooked Role of Bulk Nanobubbles in the Alteration and Motion of Microplastics in the Ocean Environment
Bibliographic record
Abstract
The increasing enrichment of microplastics (MPs) in the shoreline environment poses both ecological and social-economic risks. The alteration and motion of MPs in the ocean under the effect of bulk nanobubbles (NBs) have been less extensively studied. In this study, we explored the behavior and movement of various MPs in the presence of bulk NBs. The role of salinity and external energy in the interactions between NBs and MPs was evaluated, and the mechanism underlying these interactions was analyzed. In the presence of NBs, the binding of MPs and NBs resulted in an increase in the measured average particle size and concentration. Meanwhile, NBs reduced the aggregation between MPs, while the NBs present combined with MPs to make them more stable in suspensions. The velocity of motion of MPs driven by NBs varies under different salinity conditions. The increase in ionic strength reduced the energy barrier between particles and promoted their aggregation. Thus, the binding of NBs and MPs became more stable, which in turn affected the movement of MPs in suspensions. Polyethylene (PE1) with small particle size was mainly affected by Brownian motion, and its rising was limited; therefore, polyethylene (PE2) with large particle size rose faster than PE1 in suspension, especially in the presence of NBs. The rising velocity of poly(tetrafluoroethylene) (PTFE) was higher than that of PE1 and PE2. However, when NBs were present, the trend of the change in velocity was the opposite compared to the absence of NBs for PTFE. Moreover, various types of MPs were found to be affected distinctly by external energy. The presence of NBs had a clear effect on PE under shaking conditions, whereas the effect on PTFE was less obvious.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".