Assessment of microplastic digestion methods in source and treated drinking waters
Bibliographic record
Abstract
Analysis of microplastic particles in source and treated drinking waters requires filtration, isolation of microplastics from non-plastic organic and inorganic particles, and subsequent quantification and characterization using spectroscopic techniques. Microplastic isolation has been achieved in previous studies using a range of acids, bases, oxidants, and enzymes, however no study has systematically assessed multiple methods to identify one that is optimal for source and treated drinking waters in terms of reduction of non-plastic particles. In this study, seven oxidation, digestion, and acidification methods which have been applied individually in previous studies were directly evaluated to compare their relative performance for the isolation of microplastics >2 μm in size from non-plastic particles in drinking water. Among all seven methods evaluated, oxidation using Fenton's reagent followed by enzymatic digestion using cellulase and trypsin resulted in the greatest improvement in the amount of clean filter area as well as reduction in particle counts. Subsequent trials were conducted to improve the method by applying acidification at varying timesteps, as well as reducing oxidation and digestion reaction times. These modifications served to minimize the formation of iron precipitates as well as reduce overall sample processing time. The improved method was then evaluated using three different surface waters to confirm its applicability and reproducibility. The modified digestion method may be applied as a standard procedure across a range of source and treated waters, prior to microplastic characterization using spectroscopic techniques.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".