Methylmercury sorption to polyethylene terephthalate (PET) fibers and relevance to environmental exposure
Bibliographic record
Abstract
Considerable amounts of polyethylene terephthalate (PET) microplastic fibers are released into the environment by the laundering of polyester clothing. Microplastic fibers can be ingested by organisms in the environment. Therefore, it has been suggested that microplastic fibers act as vectors for adsorbed contaminants, which are subsequently desorbed in the gut of the organism. We undertook sorption isotherm experiments at pH 6, 7, and 8 to quantify the sorption of methylmercury (MeHg) to PET fibers. Sorption isotherms were fit to Langmuir, Freundlich, and Brunauer-Emmett-Teller models. Sorption decreased with increasing pH, which can be explained by physisorption on the negatively charged PET surfaces and the greater presence of neutral or negatively charged MeHg species at higher pH. We used the parameters obtained by the model fits to predict the likely concentration of MeHg on PET microplastic fibers in aquatic ecosystems with environmentally realistic MeHg concentrations. We calculated MeHg concentrations on PET microplastic fibers to be four orders of magnitude lower than previously observed concentrations of MeHg in seston (suspended particles comprising algae and bacteria) at the base of the aquatic food web. The results indicate that the presence of PET microplastic fibers in the environment do not elevate the MeHg exposure to organisms that ingest fibers in the environment.
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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 | 0.001 |
| 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 | 0.001 | 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".