Monitoring Poly(methyl methacrylate) and Polyvinyl Dichloride Micro/Nanoplastics in Water by Direct Solid-Phase Microextraction Coupled to Gas Chromatography–Mass Spectrometry
Bibliographic record
Abstract
A simple, sustainable, and sensitive monitoring approach of micro/nanoplastics (MNPs) in aqueous samples is crucial since it helps in assessing the extent of contamination and understanding the potential risks associated with their presence without causing additional stress to the environment. In this study, a novel strategy for qualitative and quantitative determination of MNPs in water by direct solid-phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS) was proposed for the first time. Spherical poly(methyl methacrylate) (PMMA) and irregularly shaped polyvinyl dichloride (PVDC) were used to evaluate the feasibility and performance of the proposed method. The results demonstrated that both PMMA and PVDC MNPs were efficiently extracted by the homemade SPME coating of nitrogen-doped porous carbons (N-SPCs) and exhibited sufficient thermal decomposition in the GC-MS injection port. Excellent extraction performances of N-SPCs coating for MNPs are attributed to hydrophobic cross-linking, electrostatic forcing, hydrogen bonding, and pore trapping. Methyl methacrylate was identified as the marker for PMMA, while 1,3-dichlorobenzene and 1,3,5-trichlorobenzene were the indicators for PVDC. Under the optimal extraction and decomposition conditions, the proposed method exhibited ultrahigh sensitivity, with a limit of detection of 0.0041 μg/L for PMMA and 0.0054 μg/L for PVDC. Notably, a programmed temperature strategy for the GC-MS injector was developed to discriminate and eliminate the potential interferences of intrinsic indicator compounds. Owing to the integration of sampling, extraction, injection, and decomposition into one step by SPME, the proposed method demonstrates exceptional sensitivity, eliminating the necessity for complex sample pretreatment procedures and the use of organic solvents. Finally, the proposed method was successfully applied in the determination of PMMA and PVDC MNPs in real aqueous samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".