The Effect of Nonideal Cascade Impactor Stage Collection Efficiency Curves on the Interpretation of the Size of Inhaler-Generated Aerosols
Bibliographic record
Abstract
Cascade impactors, operating on the principle of inertial size separation in (ideally) laminar flow, are used to determine aerodynamic particle size distributions (APSDs) of orally inhaled product (OIP) aerosols because aerodynamic diameter can be related to respiratory tract deposition. Each stage is assumed typically to be an ideal size fractionator. Thus, all particles larger than a certain size are considered collected and all finer particles are treated as penetrating to the next stage (a step function stage efficiency curve). In reality, the collection efficiency of a stage smoothly increases with particle size as an "S-shaped" curve, from approximately 0% to 100%. Consequently, in some cases substantial overlap occurs between neighboring stages. The potential for bias associated with the step-function assumption has been explored, taking full resolution and two-stage abbreviated forms of the Andersen eight-stage nonviable impactor (ACI) and the next-generation pharmaceutical impactor (NGI) as example apparatuses. The behavior of unimodal, log-normal APSDs typical of OIP-generated aerosols has been investigated, comparing known input values to calculated values of central tendency (mass median aerodynamic diameter) and spread (geometric standard deviation, GSD). These calculations show that the error introduced by the step change assumption is larger for the ACI than for the NGI. However, the error is sufficiently small to be inconsequential unless the APSD in nearly monodisperse (GSD ≤1.2), a condition that is unlikely to occur with realistic OIPs. Account may need to be taken of this source of bias only for the most accurate work with abbreviated ACI systems.
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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 | 0.018 | 0.059 |
| 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 | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".