The Influence of Air Flow Velocity and Particle Size on the Collection Efficiency of Passive Electrostatic Aerosol Samplers
Bibliographic record
Abstract
Electrostatic sampling is a promising method for the collection of bioaerosol particles. Although the underlying physics responsible for particle collection are well understood, the collection efficiency of simple passive electrostatic samplers is difficult to predict. Under these conditions, the collection efficiency becomes very sensitive to ambient air current and particle size, especially for submicron particles relevant for airborne virus transmission. In this paper, we compare two electrostatic aerosol sampler designs, a commercial product consisting of a flat collector plate located in the same plane as the charging needles and an axisymmetric design sampling directly to a liquid droplet. The aerosol particle collection efficiency of the samplers is investigated for particle size ranging from 0.25 to 2 µm while the air flow velocity surrounding the samplers is varied from 0.3 to 1 m s–1. For the planar design, at all ambient flow velocities, the submicron fraction of the particles captured originates in streamlines up to a maximum of 75 mm above the surface of the device collector, which greatly limits the volume of air being effectively sampled. The axisymmetric design features a non-monotonic capture efficiency as a function of particle size, with a minimum between 0.4 and 0.8 µm. The flow field in the inter-electrode region, captured using particle image velocimetry (PIV) reveals the presence of strong recirculation zones that can be responsible for the increased collection efficiency for very small particles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".