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Record W4416784277 · doi:10.1021/acsestwater.5c00798

Unanticipated Thio-Oxidation of Organophosphite Chemical Additives in PVC Microplastics Following <i>In Situ</i> Weathering

2025· article· en· W4416784277 on OpenAlexafffund
Wanzhen Chen, Husein Almuhtaram, Robert C. Andrews, Hui Peng

Bibliographic record

VenueACS ES&T Water · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change Canada
KeywordsMicroplasticsPolyvinyl chlorideAbiotic componentContaminationChemical compositionWeatheringEcotoxicologyEcotoxicity

Abstract

fetched live from OpenAlex

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 compositions of five types of microplastics that had been subjected to 16 weeks of in situ exposure to river water. The highest number of observed peak features was associated with polyvinyl chloride (PVC) (12,043), among which 2,086 were newly formed during exposure. It was unanticipated that three organothiophosphates, including triphenyl thiophosphate (TPTP), were identified with the highest abundance. The abiotic formation of organothiophosphates was confirmed via in-lab simulation trials following 6 weeks of artificial weathering. To further investigate potential reaction mechanisms, isotope-labeling trials were used to confirm the thio-oxidation of TPPi by polysulfides via the phosphonium intermediate. This is the first report of thio-oxidation of organophosphites, highlighting the importance of considering chemical transformations when conducting microplastic risk assessments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.202
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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