Adsorption of Per- and Polyfluoroalkyl Substances (PFAS) and Microcystins by Virgin and Weathered Microplastics in Freshwater Matrices
Bibliographic record
Abstract
Microplastics and per- and polyfluoroalkyl substances (PFAS) both represent persistent groups of environmental contaminants that have been associated with human health risks. Microcystin toxins are produced and stored in the cells of cyanobacteria and may be released into sources of drinking water. Recent concerns have emerged regarding the ability of microplastics to adsorb a range of organic contaminants, including PFAS and microcystins. This study examined the adsorption of two long-chain and two short-chain PFAS, as well as two common microcystins, by both virgin and weathered microplastics in freshwater. Natural weathering of microplastic surfaces may decrease adsorption by introducing hydrophilic oxygen-containing functional groups. Up to 50% adsorption of perfluorooctanesulfonic acid (PFOS) was observed for virgin PVC compared to 38% for weathered PVC. In contrast, adsorption capacities for microcystins by virgin LDPE were approximately 5.0 µg/g whereas no adsorption was observed following weathering. These results suggest that adsorption is driven by specific polymer types and dominated by hydrophobic interactions. This is the first known study to quantify PFAS and microcystins adsorption when considering environmentally relevant concentrations as well as weathered microplastics.
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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".