Characteristics of metallic nanoparticles emitted from heated Kanthal e-cigarette coils
Bibliographic record
Abstract
Electronic (e-) cigarette use is becoming more common among youth and young adults. E-cigarette users may inhale metallic particles from the heating coil along with the nicotine vapor. This study aims to develop and validate an e-cigarette generation system for future inhalation toxicology studies of e-cigarettes by characterizing the size and number of metallic nanoparticles produced and their chemical compositions. An e-cigarette generation system was constructed and Kanthal A1 coils were tested without a nicotine solution and wick under operating conditions of varying coil resistance (0.1–1.0 Ω), applied power (10–70 W), and duty cycle (5–50%). The size distribution and morphology of particles were characterized using a scanning mobility particle sizer and a transmission electron microscopy, respectively. The size of generated particles, as well as the number of particles generated, exhibited an increasing trend as the resistance of the coil increased. An initial large increase in size and number of particles generated was observed with increasing applied power, stabilizing with further increases in power. Increases in duty cycle resulted in increased particle generation. A similar pattern of particle generation was observed from the metal heating coils under various operating conditions. Under each operating condition, the number of generated particles exhibited a steep decrease during the first 15 min of each test run. Our results show this e-cigarette generation system is useful for future research investigating the health impact of metallic particle inhalation on e-cigarette users.
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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".