Characterization of airborne PET nanoplastic particles using Aerosol mass spectrometry
Bibliographic record
Abstract
Atmospheric nanoplastics (NPs) have emerged as a significant environmental concern, but knowledge about these emerging contaminants remains limited due to challenges associated with analytical techniques for detection and quantification. Aerosol mass spectrometry (AMS) has demonstrated potential to measure airborne NPs, but there is a need for a systematic assessment of its capability to measure NPs. Here we used polyethylene terephthalate nanoplastic particles (PET-NPs) as a proxy of NPs to investigate the ability of high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) to determine monomeric composition and evaluate its sensitivity toward NPs. Different PET consumer products were used to generate PET-NPs under thermo-oxidation conditions, using either a 3D printer or a tube furnace. The relative ionization efficiency (RIE) of PET-NPs was determined to be around 0.75. The particle mass spectra observed by AMS under standard operating conditions reveal notable marker ions of emitted PET-NPs such as those at m/z 149 and 166. Upon comparing the particle composition data obtained from AMS with that from pyrolysis gas chromatography mass spectrometry, we note consistencies in the mass spectra between online and offline techniques, implying similar thermal decomposition and ion fragmentation mechanisms under electron ionization (EI) at the respective operating temperatures. No evidence of vaporization delay was observed for PET at the standard vaporization temperature (600 °C) of HR-ToF-AMS. The results of this study further inform the ability and limitations of using real time aerosol mass spectrometry to measure airborne NPs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".