Analysis of microplastics in the environment: Identification and quantification of trace levels of common types of plastic polymers using pyrolysis-GC/MS
Bibliographic record
Abstract
This work describes the development of analytical workflows based on pyrolysis coupled with gas chromatography-mass spectrometry (Pyr-GC/MS) for the qualitative and quantitative analysis of 12 of the most common plastic polymers in environmental samples. The most suitable characteristic pyrolyzate compounds and respective indicator ions were selected for each polymer in order to obtain the most appropriate response for analytical purposes. Additionally, commercial pyrolyzates and polymers libraries were used to confirm the identity of the detected microplastics. The method was validated, showing a good linearity for all the plastic polymers (R 2 > 0.97) and limits of detection between 0.1 (polyurethane) to 9.1 µg (polyethylene). The developed methodology was successfully applied for the analysis of plastic polymers in environmental microplastic samples collected in three Mediterranean beaches (NE Spain). •Fast and reproducible Pyr-GC/MS method for the analysis of the 12 most common plastic polymers in a single GC/MS run •Straightforward analytical workflows using pyrolyzates and polymers libraries enable a fast identification and quantification of microplastics in environmental samples
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".