Quantitation and Stability of Nicotine in Canadian Vaping Liquids
Bibliographic record
Abstract
Electronic cigarettes (e-cigarettes, vaping products) have become increasingly popular, with recent increases in use associated with closed systems delivering higher concentrations of nicotine. Most vaping products designed as an alternative to combustible cigarettes contain nicotine. A number of published studies have examined the reported concentrations of nicotine in vaping liquids (e-liquids) and found discrepancies between labelled and measured levels. Some discrepancy can also be explained by the lack of stability of nicotine in these types of products. Recently, a chemical analysis method for the quantitative determination of low and high levels of nicotine in vaping liquids was developed. This method uses dilution with acetonitrile prior to analysis with gas chromatograph mass spectrometry (GC-MS) in single ion monitoring mode (SIM). The developed method was validated using a laboratory-prepared vaping liquid as well as commercially available, nicotine-free products fortified with nicotine in the laboratory. The method detection limit (MDL) and the limit of quantitation (LOQ) for nicotine were calculated to be 0.002 mg/mL and 0.006 mg/mL, respectively. The newly developed method was applied to quantify nicotine in commercially available vaping liquids of various flavour profiles and across a wide range of nicotine concentrations, including those with nicotine salts. Furthermore, a subset of vaping liquids were analyzed to elucidate nicotine stability in various product subtypes. After a period of six months of accelerated storage to mimic one year, the overall mean percent of the original nicotine concentration remaining in the salt-based vaping products was 85% (minimum 64%, maximum 99%) while in the free-base nicotine products it was 74% (minimum 31%, maximum 106%). Nicotine stability in vaping liquids was found to be influenced by the nicotine form (pH) of formulation and its chemical composition. Non-targeted, qualitative analysis of chemical composition of vaping products showed that most constituents were identified and found to be remaining in the products following stability trials; however, three new compounds were tentatively identified in some vaping liquids at the end of the stability trials. Stability studies and the accurate quantitation of nicotine in vaping products can help inform product standards related to the safety, quality and utility of vaping products as a smoking cessation tool.
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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 | 0.000 | 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) | 0.000 | 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 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".