Deposition of Inhaled Ultrafine Aerosols in Replicas of Nasal Airways of Infants
Bibliographic record
Abstract
Experimentally measured deposition of ultrafine particles, ranging from 13–100 nm in diameter, in nasal airway replicas of ten infants aged 3–18 months is presented. The replicas included the face, nostrils, and nasal airways including the upper trachea. A differential mobility analyzer (DMA) and a condensation particle counter (CPC) were used to quantify the nasal deposition by comparing the number of polydisperse sodium chloride particles, generated by evaporation from a Collison atomizer, at the inlet and outlet of the replicas. Particles were individually classified in size by DMA and subsequently were counted one size bin at a time by CPC upstream and downstream of each replica. Since in vivo data is not available for infants to compare to, we validated our experimental procedure instead by comparing deposition in nasal airway replicas of six adults with in vivo measurements reported in literature. In the infant replicas, tidal inhalation was simulated at two physiologically compatible flow rates and the effect of flow rate on deposition was found to be small. Deposition obtained at constant flow rates is lower than with tidal breathing, indicating the importance of unsteadiness, in contrast to similar data in adults where unsteadiness is known to be unimportant. An empirical equation, containing geometrical features of the nasal airways in the form of related non-dimensional dynamical parameters (Reynolds, Schmidt, and Womersley numbers), was best fitted to the infant data. This equation may be useful for a priori prediction of nasal deposition and intersubject variability during exposure of infants to ultrafine aerosols.
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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 | 0.001 |
| 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) | 0.000 | 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 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".