Thermal reaction products and formation pathways of two monoterpenes under in situ thermal desorption conditions that mimic vaping coil temperatures
Bibliographic record
Abstract
Vaping has become more popular and different brands and types of vaping devices have rapidly emerged. However, little is known about the potential health risks of human inhalation exposures to the volatile chemicals in the vapour, which includes both directly vaporised components of vaping liquid and their reaction products formed during vaping processes. This study investigated reaction products of two monoterpenes (α-pinene and terpinolene) that are used as flavouring agents in vaping liquids with a focus on the identification of reaction products and their formation pathways. The thermal desorption was conducted under an in situ condition that is in the range of heating coil temperature in vaping by thermally desorbing the chemicals at a temperature range of 100-300 °C. Additional clean air was introduced during the thermal desorption. 36 and 29 reaction products were identified from α-pinene and terpinolene, respectively, at a relative concentration of 0.01% and greater in the desorbed mixture. 3-Carene was the dominant reaction product of α-pinene, while reaction products of terpinolene was dominated by p-isopropenyltoluene. Several reaction pathways including ring opening, allylic oxidation, cyclo-etherification, Wagner-Meerwein rearrangement, epoxidation, cleavage and removal of partial structure, and dehydration were involved in the formation of various reaction products. These pathways and resulting relative concentrations of residual parent compound and reaction products were influenced by both temperature and amount of air present during thermal desorption. The study results demonstrate possible existence of reaction products from thermally labile chemicals like monoterpenes in vaping aerosols and can help inform policies regulating vaping devices and products to protect public health.
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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".