Assessing the Efficacy of Pyrolysis–Gas Chromatography–Mass Spectrometry for Nanoplastic and Microplastic Analysis in Human Blood
Bibliographic record
Abstract
Humans are constantly exposed to micro- and nanosized plastics (MNPs); however, there is still limited understanding of their fate within the body, partially due to limitations with current analytical techniques. The current study assessed the appropriateness of pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) analysis for the quantification of a range of polymers in human blood. An extraction protocol that reduced matrix interferences (false positives) of polyethylene (PE) and polyvinyl chloride (PVC) was developed and validated. Extraction recoveries ranged 7-109%, although surface-modified polystyrene (carboxylated) increased nanoparticle recoveries from 17 to 52%. Realistic detection limits were calculated for each polymer, accounting for matrix suppression and extraction recovery. These were up to 20 times higher than nominal detection limits calculated with Milli-Q water. Finally, the method was tested with a pilot study of the Australian population. PE interferences were reduced but still present, and no other polymers were above detection limits. It was concluded that Py-GC-MS is currently not a suitable analysis method for PE and PVC in biological matrices due to the presence of interferences and nonspecific pyrolysis products. Furthermore, while it is plausible to detect some polymers in blood, the estimated exposure concentrations needed are approaching the detection limits of the technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".