Cascade Impactors for the Size Characterization of Aerosols from Medical Inhalers: Their Uses and Limitations
Bibliographic record
Abstract
Cascade impactors, including the multi-stage liquid impinger, are by far the most widely encountered means for the in vitro determination of the particle size distribution of aerosols from medical inhalers, both in product development, batch release and in applications with add-on devices. This is because they directly measure aerodynamic size, which is the most relevant parameter to describe particle transport within the respiratory tract. At the same time, it is possible to quantify the mass of active pharmaceutical ingredient in different size ranges independent of other non-physiologically active components of the formulation. We begin by providing an overview of the operating principles of impactors and then highlight the various configurations and adaptations that have been adopted to characterize the various classes of inhaler. We continue by examining the limitations of the cascade impaction method, in particular looking at potential sources of measurement bias and discussing both appropriate and inappropriate uses of impactor-generated data. We also present a synopsis of current developments, including the Next Generation Pharmaceutical Impactor, and automation of cascade impactors for routine inhaler performance measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".