Acetal Formation of Flavoring Agents with Propylene Glycol in E-Cigarettes: Impacts on Indoor Partitioning and Thirdhand Exposure
Bibliographic record
Abstract
The widespread use of flavored e-cigarettes has led to a significant rise in teenage nicotine use. In e-liquids, the flavor carbonyls can form acetals with unknown chemical and toxicological properties. These acetals can cause adverse health effects on both smokers and nonsmokers through thirdhand exposure. This study aims to explore the impacts of these acetals formed in e-cigarettes on indoor partitioning and thirdhand exposure. Specifically, the acetalization reactions of commonly used flavor carbonyls in laboratory-made e-liquids were monitored using proton nuclear magnetic resonance ( 1 H NMR) spectroscopy. EAS-E Suite and polyparameter linear free energy relationships (PP-LFERs) were employed to estimate the partitioning coefficients for species. Further, a chemical two-dimensional partitioning model was applied to visualize the indoor equilibrium partitioning and estimate the distribution of flavor carbonyls and their acetals in the gas phase, aerosol phase, and surface reservoirs. Our results demonstrate that a substantial fraction of carbonyls were converted into acetals in e-liquids and their chemical partitioning was significantly influenced. This study shows that acetalization is a determinant factor in the exposure and toxicology of harmful carbonyl flavorings, with its impact extending to both direct exposure to smokers and involuntary exposure to nonsmokers.
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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".