Sorption Behavior of Trace Organic Chemicals on Carboxylated Polystyrene Nanoplastics
Bibliographic record
Abstract
Nanoplastics possess unique characteristics (e.g., high surface area/volume ratio) that enhance the adsorption of organic chemicals onto their surface. Their occurrence raises human health and ecotoxicological concerns, as pollutants bound to nanoplastics can have a larger effect than they would on their own. This study assessed the sorption of plant protection products (glyphosate and methyl parathion), an antidepressant (fluoxetine), a perfluorochemical (perfluorooctanoic acid [PFOA]), and a polycyclic aromatic hydrocarbon (phenanthrene) onto commercially available carboxylated polystyrene (PS) nanoplastics (NPs, 500 and 20 nm). Based on the calculated sorption coefficients ( K d, L/kg), the sequence of chemicals displaying the highest to lowest affinity toward PSNPs is fluoxetine > phenanthrene > methyl parathion > PFOA > glyphosate, with 20 nm PS showing a higher potential to sorb organic chemicals. Cationic (fluoxetine) and hydrophobic (phenanthrene) substances were more amenable to sorption, whereas negatively charged and more hydrophilic ones (i.e., PFOA and glyphosate) showed poor sorption. pH influenced sorption for all target chemicals except phenanthrene. Sorption capacity was further reduced in water spiked with natural organic matter and in tertiary-treated wastewater effluent. Overall, our work enhances the understanding of how representative organic chemicals sorb onto nanoplastics and provides quantitative information (i.e., K d ) on future simulations of nanoplastics’ fate and transport.
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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".