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Record W4415317554 · doi:10.1021/acsestwater.5c00805

Adsorption of <i>N</i> -Nitrosodimethylamine onto Polyvinyl Chloride and Polyethylene Terephthalate Microplastics in Drinking Water

2025· article· en· W4415317554 on OpenAlexafffund
Yi Li, Susan Andrews

Bibliographic record

VenueACS ES&T Water · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsMicroplasticsAdsorptionPolyethylene terephthalatePolyvinyl chlorideNatural organic matterPolyethyleneHumic acidPollutionWater pollution

Abstract

fetched live from OpenAlex

N -Nitrosodimethylamine (NDMA) poses significant public health risks as a potent carcinogen found in drinking water, while microplastics have raised concerns due to their ubiquity and potential to act as contaminant carriers. This study investigates the adsorption behavior of NDMA onto virgin and weathered polyvinyl chloride (PVC) and polyethylene terephthalate (PET) microplastics and evaluates the influence of water quality parameters on their adsorptive capacities. Adsorption isotherm experiments were conducted across diverse water matrices (ultrapure water, artificial freshwater, lake, river, and groundwater). The results demonstrated low NDMA adsorption on virgin or weathered PVC (generally <1 ng/g), and while virgin PET also experienced minimal adsorption, weathered PET displayed up to an order of magnitude higher adsorption than other materials, ranging from 0.5 to 7.5 ng/g. The adsorption capacity was influenced by ionic strength, natural organic matter, and polymer surface properties. Higher adsorption occurred in matrices with lower natural organic matter (NOM), and the NOM fractions of humic substances, biopolymers, and low-molecular-weight neutrals competitively reduced NDMA adsorption. These findings highlight the need for further research on microplastics as vectors for toxic contaminants and the regulation of microplastic pollution to mitigate associated risks in water systems.

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 score0.014
Threshold uncertainty score0.550

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 head0.005
GPT teacher head0.200
Teacher spread0.194 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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