Next Generation Pharmaceutical Impactor (A New Impactor for Pharmaceutical Inhaler Testing). Part I: Design
Bibliographic record
Abstract
A new cascade impactor has been designed specifically for pharmaceutical inhaler testing. This impactor, called the Next Generation Pharmaceutical Impactor (NGI), has seven stages and is intended to operate at any inlet flow rate between 30 and 100 L/min. It spans a cut size (D50) range from 0.54-microm to 11.7-microm aerodynamic diameter at 30 L/min and 0.24 microm to 6.12 microm at 100 L/min. The aerodynamics of the impactor follow established scientific principles, giving confident particle size fractionation behavior over the design flow range. The NGI has several features to enhance its utility for inhaler testing. One such feature is that particles are deposited on collection cups that are held in a tray. This tray is removed from the impactor as a single unit, facilitating quick sample turn-around times if multiple trays are used. For accomplishing drug recovery, the user can add up to approximately 40 mL of an appropriate solvent directly to the cups. Another unique feature is a micro-orifice collector (MOC) that captures in a collection cup extremely small particles normally collected on the final filter in other impactors. The particles captured in the MOC cup can be analyzed in the same manner as the particles collected in the other impactor stage cups. The user-friendly features and the aerodynamic design principles together provide an impactor well suited to the needs of the inhaler testing community.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".