Aerosol deposition and emission from a United States Pharmacopeia‐induction port when using a pressurized metered‐dose inhaler with and without a valved holding chamber
Bibliographic record
Abstract
Abstract The testing of pharmaceutical aerosols includes measuring the aerodynamic particle size distribution, which is usually performed on cascade impactors. In the next generation impactor (NGI), the aerosol dose is introduced through an induction port (IP), being separated into different aerodynamic diameter ranges by seven stages and a micro‐orifice collector. The IP plays an important role in estimating the oropharyngeal deposition fraction. While the IP retains mainly large particles, it also tends to retain particles in the respirable range. In this work, the deposition of particles in the IP of an NGI is studied, both experimentally and through computational fluid dynamic (CFD) simulations. Experimental tests are conducted both with the pressurized metered‐dose inhaler (pMDI) alone and in conjunction with a cylindrical valved holding chamber (VHC). For each case, the total mass deposition that occurs in the VHC, IP, and NGI stages is measured, as well as the mass median aerodynamic diameter of the aerosol leaving the IP. The CFD simulations show minimal variations in results regardless of the injection method due to the relatively low velocity and spray angle of the pMDI‐generated aerosol. If the flap‐valve is removed from the VHC, significant particle deposition occurs upstream or within the valve region, with downstream air recirculation contributing to small particle deposition. Based on the CFD results, a correlation is proposed to accurately predict the particle escape fraction of the IP and VHC, which allows estimation of the tannin distribution of particles collected in the NGI stages, especially those corresponding to the range of respirable particles.
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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".