Microwave-Assisted Extraction for Quantification of Microplastics Using Pyrolysis–Gas Chromatography/Mass Spectrometry
Bibliographic record
Abstract
Microplastics are now recognized as a persistent and global pollutant. To quantitively measure microplastics in environmental matrices, several techniques are used including new methods using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). In the present study, a new extraction method using microwave-assisted extraction (MAE) combined with Py-GC/MS was developed to extract and quantify a wide range of plastic polymers, and the method was validated using different environmental matrices. This new extraction method was able to extract polyethylene, polystyrene, polypropylene, poly(methyl-methacrylate) (PMMA), polyvinylchloride (PVC), and polycarbonate in dichloromethane with good recoveries (92.9-119.7%). The limit of detection and limit of quantification (LOQ) of the method ranged from 0.002 to 0.18 µg and from 1.2 to 5.8 µg, respectively. Intra- and interday repeatability values with coefficients of variation less than 25% for all polymers were obtained. Method validation also included a spike and recovery using all polymers from clean water, dirty water, and shrimp and salmon fillet samples, with recoveries of 85 to 111, 87 to 138, 81 to 122, and 50 to 151%, respectively. Finally, the method was tested on unspiked wild mussels and bottled water for proof-of-concept. Both polyethylene and PVC were detected and quantified in mussels, and polycarbonate and polypropylene were detected below the LOQ. For bottled water, polypropylene, polystyrene, and polycarbonate were all detected below the LOQ. We introduce a method combining MAE and Py-GC/MS as a tool for mass quantification of microplastics. This method can be used as a stand-alone, or as a complementary method to spectroscopic techniques. Environ Toxicol Chem 2021;40:2733-2741. © 2021 SETAC.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".