Exposure to (micro/nano)-plastics and their combustion products studied \nby cyclic ion mobility-mass spectrometry
Bibliographic record
Abstract
Degradation of plastics in the environment has led to formation of micro/nano-plastics \n(MNPs). Currently, there are only a few studies measuring plastic particles smaller than 1 µm in \nair. As such, the goal of this study was to develop a method for identification and quantification of \nMNPs in indoor air. Particulate matter (PM) from two indoor environments was size-resolved \nusing a Micro-Orifice Uniform Deposit Impactor (MOUDI) model 110 cascade impactor ranging \nfrom 56 nm to 18 µm in size. The GCxcIM-MS method was then developed to characterize four \ncommon plastics: polystyrene (PS), polyethylene (PE), polypropylene (PP), and polymethyl \nmethacrylate (PMMA). The results indicated that approximately 57-67% of MNPs had particle \ndiameters >2.5 µm, and these microplastics constituted 50-60% of the total particulate matter in \nprivate residences. Moreover, the comprehensive two-dimensional separation provided by the \ndeveloped method enabled us to analyze other polymers and plastic additives. For instance, plastic \nadditives such as TDCPP (Tris (1, 3-dichloro-2-propyl) phosphate) was detected, and its \nconcentration correlated with polyurethane (PU). \nPlastic can also pose a risk to human health when they are combusted. The goal of second \nchapter was differentiation between toxic and non-toxic halogenated of polycyclic aromatic \nhydrocarbons (HPAHs) isomers that were released during combustion of plastics. The geometry \nof cIM-MS allows ions to travel multiple passes through cyclic cell such that, the greater of pass \nnumbers, the better resolution of isomers. When a complex real sample was studied in this way, \nthe toxic 2367-tetrachloroanthracene (2367-TCA) was separated from a mix of 17 other isomers \nwith the assistance of an advanced “unwrapping” data analysis technique.
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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) | 0.002 | 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".