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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".