Optimized Extraction Methods for Pristine and Aged Microplastics from Complex Water Samples
Bibliographic record
Abstract
Efficient and replicable extraction of microplastics (MPs) and other anthropogenic particles from complex environmental matrices remains challenging. We tested and optimized the extraction of water samples with and without organic matter (OM) spiked with 9 MP polymers with 16 different morphologies and/or colors, that were pristine (63-1000 μm) and aged (300-1000 μm). Statistical analyses showed that OM presence most significantly influenced MP (300-1000 μm) recoveries, followed by the strength of digestion reagents, temperature, and exposure time. Optimal recovery of MPs in a matrix with OM of <2 g/L can be obtained with a single-step digestion of Fenton's reagent. A sequential combination of two or more digestion solutions (e.g., Fenton's reagent +10% potassium hydroxide) is recommended when OM >10 g/L. Recoveries of aged MPs susceptible to degradation were up to 6 times lower than those of their pristine version after applying the same digestion method. Thus, while the digestion method may be nondestructive for pristine MPs, weathered MPs could be partially or completely digested. We recommend that the characteristics of the spiked MPs closely match those of the targeted particles in real samples during quality control tests, which allows for the generation of robust and reliable monitoring data sets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".