Experimental Study of Spiriva Respimat Soft Mist Inhaler Spray Characterization: Size Distributions and Velocity
Bibliographic record
Abstract
Abstract Background : Respiratory illnesses such as asthma, bronchitis, and chronic obstructive pulmonary disease (COPD) are considered to be debilitating diseases. A variety of inhalation devices have been used to deliver aerosol medication to patients in the treatment of those diseases. Soft Mist Inhalers (SMIs, for example, the Spiriva Respimat) are a new generation of propellant-free inhalers. In this type of inhalation device, inhalable droplets are generated from an aqueous solution. Droplet size and velocity are two of the most substantial factors that impact the deposition of SMI aerosol medication into the patient lungs. Methods: In this study, size and velocity of droplets generated from the Spiriva Respimat inhaler were measured using phase Doppler anemometry (PDA). Measurements were taken at four locations along the centerline of the Spiriva Respimat inhaler, in addition to three cross sections (free-spray configuration). In addition, measurements were also performed at a single cross section in confined spray configurations using two separate idealized mouth cavities. Results and Conclusions : Measurements along the centerline of the aerosol mist generated using the Spiriva Respimat inhaler (6.5, 25, 100, and 125 mm downstream of the inhaler nozzle’ orifice) showed that droplets at the mouthpiece have the highest velocity of 10.95 m/s, decreasing to 1.33 m/s at the 125 mm location away from the nozzle. The mean diameter D 10 values ranged from 3.97 to 3.67 μm at 6.5 and 125 mm locations, respectively. In addition, of the three probability density functions (PDFs) that were tested, the log-normal PDF showed better curve fitting for the empirical data (droplet size distributions) that were measured. The effect of spray confinement using two idealized mouth cavities shows that there was a drop in the particles' velocity for both models on each axes compared with the open-air environment (free-spray configuration).
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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".