Effects of Chemical Pretreatment on Natural Fibers Removal and Microplastics Integrity for Wastewater Characterization
Bibliographic record
Abstract
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.
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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) | 0.001 | 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".