Evaluation of Soft Mist Inhaler Aerosol Velocity, Size, and Deposition Inside the Mouth—A Computational Fluid Dynamics Study
Bibliographic record
Abstract
Respiratory diseases debilitate more than 250 million people around the world. Among available inhalation devices, the soft mist inhaler (SMI) is the most efficient at delivering drugs to ease respiratory disease symptoms. In this study, we analyzed the SMI performance in terms of the aerosol's velocity profiles, flow pattern, size distribution, and deposition by employing computational fluid dynamics (CFD) simulations. We modeled two different simplified mouth geometries, idealized mouth (IM), and standard mouth (SM). Three different locations (x = 0, x = 5, and x = 10 mm) for the SMI nozzle orifice were chosen along the mouth cavity centerlines, followed by two different SMI nozzle angles (10 deg and 20 deg) for IM geometry. A flowrate of 30 L/min was applied. The simulation results were evaluated against experimental data. It was found that the SMI could be simulated successfully with a level of error of less than 10%. The inhalation flowrate significantly impacted the aerosol's velocity profile and deposition efficiency on both the IM and SM walls. The lowest particle deposition on the mouth wall occurred when a fixed flowrate (30 L/min) was applied inside both geometries, and the SMI nozzle position moved forward to x = 10 mm from the IM and SM inlets. An increase in the SMI nozzle angle increased particle deposition and decreased the deposition fraction for particles with a diameter above 5 μm inside the IM.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".