Formaldehyde in Electronic Cigarette Liquid (Aerosolized Liquid)
Bibliographic record
Abstract
BACKGROUND: Aerosolized liquid (e-liquid) of electronic cigarettes can be toxic. Beyond the solvent (propylene glycol, vegetable glycerin) and nicotine, little is known about the liquid composition. Formaldehyde, a carcinogen and source of contact dermatitis, has been reported in the vaporized e-liquid, but no studies have assessed the actual e-liquid. OBJECTIVE: The aim of the study was to evaluate e-liquid products for the presence of formaldehyde. METHODS: Sixteen e-liquid products were purchased and analyzed for the release of formaldehyde using the chromotropic acid method of detection. RESULTS: Of the 16 e-liquids purchased, 4 (25%) were positive for the presence of formaldehyde; 2 were flavored and 2 were nonflavored. All positive e-liquids were in pods or disposable electronic cigarette devices, and 2 were purchased from local vape shops. The average nicotine content in the positive e-liquids was 3.85% versus 4.03% in the negative e-liquids. CONCLUSIONS: The e-liquid products contain toxic chemicals not declared on product labels, as shown in this study with 25.0% of e-liquids containing formaldehyde. All positive e-liquids were within pods or disposable devices. Continued analysis of e-liquids and increased product regulation are needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".