Determining the cutoff diameter and counting efficiency of optical particle counters with an aerodynamic aerosol classifier and an inkjet aerosol generator
Bibliographic record
Abstract
A method to determine the cutoff diameter and counting efficiency of single-particle optical particle counters (OPC) is presented. An aerodynamic aerosol classifier (AAC) coupled with an atomizer was used to generate monodisperse dioctyl phthalate particles with a condensation particle counter (CPC) as the reference instrument. This method is accurate for capturing the counting efficiency of OPCs at lower size ranges and determining the cutoff diameter. The CPC was found to be a poor reference instrument for particles larger than 1 µm in which case an inkjet aerosol generator (IAG) was used as the monodisperse particle source and reference instrument. Two different particle materials were used with the IAG: lactose monohydrate and an ionic liquid (1-ethyl-3-methylimidazolium trifluoromethanesulfonate). A combination of the AAC to size smaller particles (<1 µm) and IAG to generate larger particles (>1 µm) was found to provide a comprehensive method capable of covering the counting efficiency over the entire operating range of OPCs.Copyright © 2020 American Association for Aerosol Research
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".