Environmental Fate Modeling of Nanoplastics in a Salinity Gradient Using a Lab-on-a-Chip: Where Does the Nanoscale Fraction of Plastic Debris Accumulate?
Bibliographic record
Abstract
The aim of this study is to demonstrate how the flow and diffusion of nanoplastics through a salinity gradient (SG), as observed in mangrove swamps (MSPs), influence their aggregation pathways. These two parameters have never yet been used to evaluate the fate and behavior of colloids in the environment, since they cannot be incorporated into classical experimental setups. Land-sea continuums, such as estuaries and MSP systems, are known to be environmentally reactive interfaces that influence the colloidal distribution of pollutants. Using a microfluidic approach to reproduce the SG and its dynamics, the results show that nanoplastics arriving in a MSP are fractionated. First, a substantial fraction rapidly aggregates to reach the microscale, principally governed by an orthokinetic aggregation process and diffusiophoresis drift. These large nanoplastic aggregates eventually float near the water's surface or settle into the sediment at the bottom of the MSP, depending on their density. The second, smaller fraction remains stable and is transported toward the saline environment. This distribution results from the combined action of the spatial salt concentration gradient and orthokinetic aggregation, which is largely underestimated in the literature. Due to nanoplastics' reactive behavior, the present work demonstrates that mangrove and estuarine systems need to be better examined regarding plastic pollution.
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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 | 0.001 | 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".