Performance of Mechanical Filters and Respirators for Capturing Nanoparticles ―Limitations and Future Direction
Bibliographic record
Abstract
There is an increasing concern about the health hazard posed to workers exposed to inhalation of nanoparticles. Inhaling nanoparticles possess an occupational hazard due to elevated amount emitted to the atmosphere and working environment. Nanoparticles have potential toxic properties: the high particle surface area, number concentration, and surface reactivity. Inhalation, the most common route of nanoparticle exposure, has been shown to cause adverse effects on pulmonary functions and the deposited particles in the lung can be translocated to the blood system by passing through the pulmonary protection barriers. Filtration is the simplest and most common method of aerosol control. It is widely used in mechanical ventilation and respiratory protection. However, concerns have been raised regarding the effectiveness of the filters for capturing nanoparticles. This paper reviews the literature on the filtration performance of mechanical filters and respirators against nanoparticles. It includes the discussion about filtration mechanisms, theoretical models, affecting factors of the filtration efficiency, and testing protocols for respirator and filter certification.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".