Granular filtration for airborne particles: Correlation between experiments and models
Bibliographic record
Abstract
Abstract Granular filtration has been widely used for liquid filtration and hot gas filtration, but less is known for the filtration of airborne particles, especially the ultrafine ones, at the room conditions. A cylindrical packed bed was designed and tested for the filtration of particles in the range of about 10 nm to 15 µm in diameter at different configurations and kinetic conditions. Three sizes of uniform glass beads (2, 4, and 6 mm in diameter) were tested as the filtration media each at three media thicknesses (H = 2.5, 7.6, and 12.7 cm), and at two airflow rates (50 and 65 liters per minute). The filtration efficiencies were the lowest for particles between 0.1 and 1 µm in diameter. The particle filtration efficiency decreased with the increase in the granule size and the airflow rate, but a thicker bed corresponded to higher filtration efficiency. The experimental results showed much higher efficiency than existing models can predict, therefore, an empirical model using least square method is reported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".