Characterisation of the Aerodynamic Aerosol Classifier Transfer Function for Particle Sizes up to 5 Micrometres
Bibliographic record
Abstract
The Aerodynamic Aerosol Classifier (AAC) classifies particles with the desired aerodynamic diameter by generating opposing centrifugal and drag forces on the particles using rotating concentric cylinders and a clean sheath flow. Particle transmission through the classifier is described by its transfer function, which is an important indicator of classifier performance. Characterisation of the classifier’s transfer function improves the accuracy of its common aerosol applications, such as measuring particle size distributions or providing classified particles to other aerosol instruments for calibration or further analysis. This characterisation is commonly achieved experimentally using a tandem set-up of the same classifier. While this approach was previously used to characterise the AAC’s transfer function for particle aerodynamic diameters up to 2.4 µm using a nebuliser and a Condensation Particle Counter (CPC), the current study expands the AAC’s characterisation up to 5 µm using a condensation aerosol generator and an Optical Particle Counter (OPC). This upper size range is significantly higher than that offered by other common aerosol classifiers, such as the approximate 1 µm upper limit typical of the Differential Mobility Analyser (DMA), and it is well suited to many applications, including OPC calibration, bio-aerosols, drug delivery and inhalation studies, and atmospheric dust analysis. This study found that the AAC maintains its high transmission efficiency (≥ 60%) for particles up to 5 µm and that its transfer function width factor increases (i.e., the distribution becomes narrower) as the aerodynamic diameter increases. Setpoint agreement between two different production AACs remained within 2% over the size range tested. Therefore, the AAC has excellent performance for classifying particles up to 5 µm.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".