Particle size distribution of a fourth-generation vaping device at various low powers, nicotine concentrations and dilutions
Bibliographic record
Abstract
A lack of compatibility among many studies on e-cigarette aerosol measurement suggests further investigations establishing a standard testing condition. Considering a lesser emission of harmful substances, relatively low heating powers are beneficial in aerosol generating devices. However, comprehensive study on particle size distribution (PSD) at relatively low heating powers (e.g., power <10 W) is still deficient. In this study, particle number count of undiluted aerosols, produced by a latest fourth-generation vaping device, was measured at heating powers from 3.5 to 6.5 W and nicotine concentration of 12 and 18 mg/mL. Also, effects of a semi-static dilution process on PSD based on both mass (using cascade impactor) and number count (using real-time particle counter) were explored. Higher power and/or lower nicotine concentration led to more asymmetricity in PSD. A novel function, combining exponential, Gaussian and polynomial (EGP) distributions, was introduced to describe asymmetric PSD successfully. Particle size statistics up to the fourth order moment were reported. Count median aerodynamic diameter and geometric standard deviation exhibited a linear positive correlation with power with a slope of 0.02 and 0.174, respectively, at 18 mg/mL nicotine. For the same nicotine case, the variation of skewness and kurtosis with heating power followed a power law curve with a degree of 0.44 and 0.66, respectively. A semi-static dilution resulted in an about 6% higher PSD mode relative to undiluted condition. The present study provides useful data, including EGP function and higher order moments, which have potential implications in simulating PSD and validating aerosol dosimetry model.Copyright © 2024 American Association for Aerosol Research
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".