Experimental Measurement and Numerical Study of Particle Deposition in Highly Idealized Mouth-Throat Models
Bibliographic record
Abstract
Particle deposition in the human mouth-throat is an important factor in evaluating efficiency of drug delivered by inhalation devices, such as pMDIs or DPIs. Current USP standard impactor induction ports (which serve as an in vitro representative of the adult mouth-throat) underpredict in vivo mouth-throat deposition. However, preliminary experimental data in the prototype geometry of a highly idealized mouth-throat has indicated that it is a promising replacement for the USP mouth-throat (CitationZhang et al. 2004). In this study, partial optimization design of the highly idealized mouth-throat was performed using computational fluid dynamics (CFD) simulations. The performance of six candidate highly idealized mouth-throat geometries, each with a straight tube inlet of inner diameter 17.3 mm, was evaluated experimentally by measuring monodisperse particle deposition efficiency. Gravimetry was used to determine particle deposition in these geometries. Monodisperse particles of di-2-ethylhexyl-sebecate (DEHS) oil with mass median diameters of 2.1–7.5 μm (GSD < 1.1) were used at two steady inhalation flow rates of 30 and 90 l/min. The results showed that, at the higher flow rate of 90 l/min, a mouth-throat with a bend region 8.5 mm in diameter and a curvature radius of 50 mm follows the in vivo average summarized by CitationStahlhofen et al. (1989) most closely. In contrast, at the lower flow rate of 30 l/min, the mouth-throat with a bend region 7.5 mm in diameter mimics the above in vivo curve but the in vivo-in vitro match is worse than for 90 l/min case. Enhanced particle deposition caused by a Reynolds number effect was observed in all geometries studied. Overall, an adequately designed highly idealized mouth-throat can largely reproduce in vivo mouth-throat deposition. For different flow rates, different key dimensions are required with the present highly idealized mouth-throats in order to adequately reproduce the in vivo average curve.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".