Unanticipated Thio-oxidation of Organophosphite Chemical Additives in PVC Microplastics Following In-Situ Weathering
Bibliographic record
Abstract
Microplastics have emerged as contaminants of concern due to their worldwide distribution and persistence. Following environmental weathering the chemical composition of microplastics may be altered by physicochemical processes. In this study, nontargeted analysis was employed to examine changes in the chemical composition of five different types of microplastics that had been subjected to 16 weeks of in-situ exposure to flowing river water. The highest number of observed peak features was associated with PVC (12,043), among which 2,086 were newly formed. It was unanticipated that three organothiophosphates, including triphenyl thiophosphate (TPTP), would have the highest abundance following in-situ exposure of PVC microplastics. The abiotic formation of organothiophosphates was confirmed via in-lab simulation trials following 6 weeks of artificial weathering. To further investigate potential reaction mechanisms, triphenyl phosphite (TPPi) and PVC microplastic particles were individually incubated along with five major sulfur species. Elementary sulfur (S8) and sulfide (S2-) were observed to be responsible for the formation of TPTP via the thio-oxidation of TPPi. This is the first known report of thio-oxidation as a transformation pathway, highlighting the importance of considering chemical transformations when conducting microplastic risk assessments.
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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".