Next Generation Pharmaceutical Impactor: A New Impactor for Pharmaceutical Inhaler Testing. Part III. Extension of Archival Calibration to 15 L/min
Bibliographic record
Abstract
An extension of the archival calibration of the recently developed 30-100-L/min seven-stage impactor, the Next Generation Pharmaceutical Impactor (NGI), has been undertaken at 15 L/min. The NGI stage cut sizes are 0.98-14.1 microm aerodynamic diameter at this flow rate. This 15-L/min calibration was motivated by the desire to sample the entire aerosol produced by a nebulizer when tested in accordance with a new international standard developed by the Comite Européen de Normalisation (CEN), as well as the need to test various types of inhalers at flow rates lower than 30 L/min for pediatric applications. Measurements were undertaken with monodisperse oleic acid droplets in the range of 0.7-22 microm aerodynamic diameter following a procedure established in the original 30-100-L/min calibration study. The NGI was found to be effective for particle size separation at 15 L/min. Users should decide the most applicable configuration that meets their needs, based on the following recommendations: (1) the pre-separator should not normally be used, as its performance is significantly degraded by the influence of gravity, resulting in interference with stage 1; and (2) a filter should be inserted below the micro-orifice collector (MOC), as the size corresponding to 80% collection efficiency of the MOC becomes excessively large with decreasing flow rate, so that this component becomes ineffective as a means of collecting fine particles that penetrate beyond stage 7.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".