Selective Uptake of Third-Hand Tobacco Smoke Components to Inorganic and Organic Aerosol Particles
Bibliographic record
Abstract
Third-hand smoke (THS) is an emerging route of exposure to tobacco smoke in the indoor environment. Few studies have investigated the chemical behavior of THS, although initial findings suggest that semivolatile components of THS can partition to indoor aerosol. By exposing single-component particles to THS in an environmental chamber, this study demonstrates a pronounced dependence of THS uptake on aerosol composition. First, it was found that primarily reduced nitrogen compounds (that produced C x H y N z + ion signal) in THS partitioned strongly to acidic ammoniated sulfate particles, whereas overall THS uptake to more pH-neutral sodium sulfate particles was minimal. Second, THS uptake to pure hydrocarbon particles (squalane) was even greater than to ammoniated sulfate particles with the uptake arising from mainly C x H y compounds. The greater uptake of THS to squalane was mostly driven by the dominant fraction of C x H y compounds in the side stream cigarette smoke aerosol, the composition of which is likely to be broadly similar to THS in these experiments. Third, oxygenated organic particles (sucrose) and solid ammonium sulfate particles showed minimal uptake. These results indicate that particulate THS inhalation exposure will be strongly dependent on the chemical nature of the particles present in the indoor environment.
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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".