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Record W4410384820 · doi:10.1021/acsestwater.4c01233

Effects of Chemical Pretreatment on Natural Fibers Removal and Microplastics Integrity for Wastewater Characterization

2025· article· en· W4410384820 on OpenAlex
Ambroise Bellamy, Yves Comeau, Dominique Claveau-Mallet

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS ES&T Water · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsMicroplasticsWastewaterNatural (archaeology)Environmental scienceCharacterization (materials science)Waste managementEnvironmental chemistryPulp and paper industryChemistryEnvironmental engineeringMaterials scienceEngineeringBiologyNanotechnology

Abstract

fetched live from OpenAlex

Nine digestion protocols were tested to quantify microplastics in wastewater using nine polymeric and three natural fiber controls representative of common microplastics in wastewater. Protocols were also evaluated for their impact on natural fibers, which can interfere with microplastic quantification. Control size change and visual integrity were assessed, revealing that a sequential 24-h treatment with 6% NaClO at room temperature (RT) followed by 24 h with 30% H 2 O 2 at 40 °C preserved polymer integrity while fully oxidizing natural fibers, even when preincubated in real wastewater samples. A Fourier-transform infrared spectroscopy (FTIR) validation using the carbonyl index (CI) and carbon–oxygen index (COI) showed significant changes in poly(ethylene terephthalate) (PET) and polyvinyl chloride (PVC) after digestion but did not compromise FTIR spectrum recognition. The protocol applied to raw wastewater samples showed optimal performance at 300 mg Cl 2 /L, achieving up to 95% Chemical Oxygen Demand (COD) and 92% turbidity reduction. No further improvements in COD or turbidity removal were observed beyond this dose, regardless of initial COD levels. The present approach affords greater comparability with existing studies thanks to a large range of polymeric, natural controls, and oxidant dose investigations regarding common water quality parameters.

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.

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 score0.006
Threshold uncertainty score0.328

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 head0.195
Teacher spread0.192 · 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