Hygroscopic effects on the mobility and mass of cigarette smoke particles
Bibliographic record
Abstract
The hygroscopic growth of particles, produced from a University of Kentucky 3R4F reference cigarette smoked following Health Canada Intense (HCI) puffing parameters (55 mL puff of 2 s duration, every 30 s), was measured in terms of the electrical mobility diameter and particle mass, using a Hygroscopic Tandem Differential Mobility Analyzer (HTDMA) and Centrifugal Particle Mass Analyzer (CPMA) system. Both the particle mobility diameter and mass growth factors were found to agree with previously determined values and hygroscopicity models. The mobility diameter growth factor of the particles produced from either a University of Kentucky 3R4F or 1R5F reference cigarette, following HCI puffing parameters, were found to be very similar. As the relative humidity (RH) approached saturation, the effects of the initial particle size on the mobility growth factor became more dominant, with larger particles growing proportionally larger than smaller particles. From the measured mobility diameter and mass growth factors, the density growth factor was calculated. This parameter showed that the particle density increased as the sample relative humidity increased. This case is only possible, given that the dried smoke particle density (1109±118 kg/m3) was determined to be greater than the density of water, if the water condensation on the smoke particle dissolves at least a portion of it, resulting in a significant increase in mass with only a small increase volume.
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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".