Occurrence of metals in e-cigarette liquids: Influence of coils on metal leaching and exposure assessment
Bibliographic record
Abstract
Electronic cigarettes are generally recognized as a safer alternative than conventional cigarettes. Nevertheless, previous research suggests metal (loid) leaching due to coil contact, potentially transferring to the e-liquid and its aerosolized form. In this study, Cr, Cd, Ni, and Pb levels were measured by inductively coupled plasma mass spectrometry (ICP-MS) on 17 samples of e-liquids with different chemical properties (e.g., pH, nicotine content, flavoring, free-base, and nicotine salts). Twelve e-liquids were then put in contact with 36-gauge Kanthal A-1, Nichrome 80, Stainless steel 317 L and disposable coils such as Juul, and Aspire BVC for three days at 200-250 °C for 1 h each day. Metal levels expressed as mean (standard deviation) metal concentration, were below detection (Cd) to very low in bottle samples (Ni ≤ 76 (18); Pb ≤ 16 (1.5); and Cr ≤ 386 (15.6) μg/kg). In the coil extracts, varying concentrations of the same metal (loid) were found, indicating that metal leaching capacity may differ per sample. All samples contained Ni and Cr, followed by Pb to a much lesser extent. Cd levels were mostly below detection limits. Coil + e-liquid combinations with the highest Ni, Cr, and Pb concentrations were: Aspire BVC + Melon 0 mg/mL: Ni = 1.22 E+04 (281); Aspire BVC + Hit Nicotine 40 mg/mL: Cr = 864 (116); and Nichrome 80 + Melon 0 mg/mL: Pb = 56 (5) μg/kg. Overall, results suggest that nicotine salts at 40 mg/mL enhance Cr and Ni transfer. Stainless steel 317 L released very low metal concentrations. A conservative screening level risk characterization showed that 10.5% and 3.5% of the coil extracts may exceed Ni and Cr (III) safe concentrations, respectively. In the aerosol phase, 8.8% of samples might be above Ni equivalent daily dose for chronic exposure and 1.8% for intermediate exposure. Further studies on coil metal leaching could aid in establishing coil manufacturing regulations.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".