Impact of coagulation characteristics on the aggregation of microplastics in upper-ocean turbulence
Bibliographic record
Abstract
The dynamics and aggregation of microplastics in marine environments are investigated through high-fidelity direct numerical simulations with Lagrangian point-particle tracking. The properties of microplastics and biogenic particles, including size, density, and concentration, align with scenarios typical of seawater systems. The stickiness nature of microplastics, induced by biofilm formation (biofouling), is modeled through coagulation efficiency (stickiness parameter), which represents the probability of aggregation following a collision event. Two main aspects are at the core of the present work: analyzing the mechanisms of collision and coalescence between microplastics and biogenic particles, along with their spatial distribution, and characterizing the emerging aggregates. The results indicate that particles stickiness, concentration and (especially) size impact on the collision and coalescence rates. Furthermore, microplastics exhibit a strong tendency to accumulate near biogenic particles, leading to the creation of hetero-aggregates whose tendency to sink supports the general hypothesis of “missing microplastics”. Particularly, in cases where microplastics and biogenic particles are evenly concentrated, microplastics primarily contribute to the formation of aggregates. The stickiness mainly determines the most complex and large aggregates, which are less than 1% of the total. • Microparticles model within upper-ocean turbulence is examined. • Properties of microparticles influence their dynamics and aggregate formation. • Wide range of aggregates created, including both lighter and heavier than water. • Aggregates formation makes microplastics sink by coagulating with biogenic material.
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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 | 0.000 | 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) | 0.000 | 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".