Autoxidation of Glycols Used in Inhalable DailyProducts: Implications to the Use of ArtificialFogs and E-cigarettes
Bibliographic record
Abstract
The use of glycols is seen in various industries and occupations. In past decades, health implications from inhalable glycols have gained public attention. By impacting indoor air quality, inhalable glycols may cause adverse health effects, especially for workers in different occupations receiving frequent exposure. Our previous work highlighted the rapid accumulation of formaldehyde and glycolaldehyde in fog juice, thus proposing the occurrence of glycol autoxidation. However, the fundamentals of glycol autoxidation remained unclear and unexplored. Our goal is to investigate the autoxidation of common glycols during indoor storage. Carbonyls were quantified using liquid chromatography-mass spectrometry (LC-MS), and peroxides from autoxidation were monitored via iodometry and UV-Vis spectrometry. The impact of external factors was also investigated, such as the water mixing ratio, and antioxidants (Vitamin C). Via weekly monitoring, a rapid formation of aldehydes in many glycols was observed, such as E-cigarette juice and triethylene glycol (TEG). Occurrence of autoxidation was confirmed by the rise of the total peroxide concentration. Additionally, we highlighted the dependence of carbonyl formation rate on the TEG-water mixing ratio, demonstrating the complicated role water plays in glycol autoxidation. We have also tested the effectiveness of Vitamin C, with suggestions for minimizing the formation of toxic carbonyls in consumer products.
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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 | 0.001 | 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".