Aerosol generation using a joint vapor–nuclei type generator: Factorial design to characterize its performance
Bibliographic record
Abstract
The performance of a joint vapor–nuclei type aerosol generator was investigated using a 24 factorial design experiment. The generator produced stable, reproducible monodisperse aerosols with aerodynamic diameters from 0.79 to 3.1 µm. The geometric standard deviation (GSD) ranged from 1.09 to 1.31. The factorial design experiment indicated that atomizer flow rate and temperature of aerosol material were the significant factors that control the number mean diameter (NMD) of product aerosols, while the diameter of the condensation chimney had a significant effect on aerosol monodispersity. Among these significant factors, the generator had a rapid, reproducible response to atomizer flow rate. As a result, it was used as an adjustment parameter to fine-tune the performance of the generator. Its main effect on NMD was –1.3 µm per increase of 1.0 L/min in atomizer flow rate. Experimental results suggested that a power-law relationship could be used to predict the NMD of product aerosols. This study confirmed that the atomizer must produce >105 cm–3 of primary mists to generate monodisperse aerosols by heterogeneous condensation. Key words: aerosol generator, monodisperse, condensation, nuclei, factorial design, heterogeneous, homogeneous.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".