Performance of mechanical filters used in general ventilation against nanoparticles
Bibliographic record
Abstract
With an equal mass, nanoparticles (NP) have a higher toxicity than particles with the same chemical composition but with larger surface area. However, the toxicological knowledge concerning NP is still insufficient to establish limit values of exposure. To seek the lowest exposure level, filtration is a simple and effective way to capture particles, including NP. According to ANSI/ASHRAE 52.2 standard, ventilation filters efficiency is tested for particles ranging from 0.3 to 10.0 μm. Performances of entire filters for NP are still very limited and particle size of 300 nm (0.3 μm) is commonly used as the most penetrating particle size (MPPS) for mechanical media. To evaluate the filter performance for NP, five type of filters were investigated to measure their performance for particles smaller than 300 nm including NP. The performance of these filters was evaluated in terms of penetration and pressure drop. Experimental data permit to evaluate the MPPS for these mechanical filters. Nevertheless, 150–500 nm range provides a better estimation of the MPPS in the conditions which were tested. Also, filtration velocity influences efficiency for nanoparticles at 50 nm but no effect was observed for MPPS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".