Atomization characteristics of soft mist inhaler (SMI) devices: aerosolized particle delivery through the respiratory tract—an innovative numerical and experimental study
Bibliographic record
Abstract
Soft mist inhalers (SMIs) stand out for their innovative design and high efficiency, making them promising candidates for advanced research in inhalation therapy. This study presents both experimental and numerical methods to differentiate multiphase flow fields within the device mouthpiece and the realistic VCU (Virginia Commonwealth University) medium-sized mouth-throat (MT) airway. Using a numerical approach, the volume of fluid (VOF) method was coupled with the discrete phase model (DPM), incorporating an adaptive mesh refinement technique to thoroughly analyze the liquid jet breakup mechanisms for SMI's two nozzles. Furthermore, we introduced a novel particle data transmission method (PDTM) to track atomized particles in the realistic MT airway. To validate the plume generated by the VOF-DPM model, a high-speed camera along with image analysis techniques were employed. Experimental results from a next-generation impactor (NGI) further confirmed the accuracy of the numerical model in simulating airway particle deposition. We compared our results from the proposed VOF-DPM-PDTM model to the traditional DPM-stochastic collision model. Our findings indicate that integrating the VOF-DPM model with the novel PDTM improved the prediction of drug deposition in the SMI mouthpiece by up to 90 %. According to the VOF-DPM model, approximately 28 % of the drug is deposited in the mouthpiece area, which aligns closely with the experimental outcome of 30 %. Analysis of the particles revealed that about 65 % undergo bag and multimode breakup, resulting in a mass median diameter of approximately 4.9 μm, with distinct secondary peaks in both fine and coarse particle size ranges. Image analysis further showed that drug aerosols disperse at an angle of approximately 36.5° and travel about 0.80 mm from the SMI nozzle exit at a speed of 25.53 m/s. This contributes to increased drug loss within the device's mouthpiece. We also introduced a more refined particle injection data model for the DPM framework, offering greater detail and improved accuracy compared to the existing predefined version. • A new numerical model was developed to simulate the drug aerosols produced by SMIs. • Image analysis and NGI data validate the model's effectiveness within the studied domain. • The proposed model led to a 90 % improvement in predicting drug loss in SMI's mouthpiece. • A more detailed initial particle size distribution near the nozzle orifice was proposed. • An outer spreading angle of 36.5° was identified, reaching 0.8 mm beyond the nozzle exit.
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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) | 0.001 | 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".