Performance of Different Class of Filtering Facepiece Respirators Toward Nanoparticles
Bibliographic record
Abstract
Introduction: In the case of exposure to harmful substances and the use of a filtering facepiece respirator (FFR) to protect workers, it is important to know the performance of commercial FFR. In previous studies, the authors investigated a series of parameters on the effectiveness of a standard N95 FFR: particle diameter, airflow rate intensity, breathing simulation, time of use, relative humidity. It remains uncertain whether previous results can be extrapolated to all commercial FFRs. Objective: To determine whether previous conclusions apply to other FFRs, different models were selected for evaluation. The initial penetrations are then measured in order to compare the penetrations according to the differences between the FFRs. Methods: To do so, an experimental setup generates NaCl nanoparticles before introducing them into a test chamber containing the FFR and filtered at a constant flow of 85 L/min. It allows to obtain the penetration as a function of the particle diameter from 10 to 200 nm, as well as the pressure drop. Results: The benchmark established in this study confirms that penetration measurements align with previously reported values. All tested FFRs exhibit comparable trends: the most penetrating particle size (MPPS) remains between 30 and 50 nanometers, with penetration decreasing for particles smaller or larger than this range. Conclusion: The results obtained thus indicate that the outcome achieved previously for the standard N95 FFRs and can be extrapolated to the other FFRs presented here.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".