Filtration and Retention Characteristics of Smoke Components in Filters
Bibliographic record
Abstract
SUMMARY The filtration and retention characteristics of nicotine, phenol, benzo[a]pyrene (B[a]P), 4-(methylnitrosamino)-1- (3-pyridyl)-1-butanone (NNK), crotonaldehyde, hydrogen cyanide (HCN) and ammonia in conventional cellulose acetate fiber filters were investigated. By quantitatively analyzing their contents released in mainstream smoke and retained in filters, their filtration efficiencies, taken as the ratio of filter retention content to total yield, were determined under both International Organization for Standardization (ISO) and Health Canadian Intense (HCI) smoking regimes. Using a precision laser cutter, the filters were either cut transversely into 5-7 segments for longitudinal distribution pattern study, or cut transversely into 3 segments firstly and then each segment was cut concentrically into 3 concentric segments for spatial distribution pattern study. Contents of the named smoke components retained in these filter segments were quantitatively analyzed. The data were calibrated and then processed with interpolation analysis and polynomial fitting. The longitudinal distribution patterns for all components mentioned above, as well as spatial distribution patterns for nicotine, phenol, HCN, ammonia and crotonaldehyde, were obtained. The filtration efficiencies of different smoke components varied between 24% and 15% for HCN, 87% and 92% for phenol under ISO and HCI smoking regimes respectively. The filtration efficiencies of all the studied components under HCI smoking were lower than under ISO smoking to different extents except phenol which showed the opposite trend. Different mainstream smoke components have their own retention behavior and distribution characteristics which are determined by the physical and chemical properties of the component and its interaction with cellulose acetate fiber and the glycerol triacetate within the filter. The diversity of retention distribution patterns of different components shows the high complexity of cigarette smoke filtration in filters. [Beitr. Tabakforsch Int. 26 (2014) 121-131]
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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".