Contribution of Breathing Frequency and Inhalation Flow Rate on Performance of N95 Filtering Facepiece Respirators
Bibliographic record
Abstract
The investigation of particle penetration through filtering facepiece respirators under cyclic flows is very necessary because cyclic flows represent actual breathing flow patterns. This article reports the development of a procedure to investigate the individual impact of breathing frequency and flow rate on the performance of N95 filtering facepiece respirators. Experiments were performed for two peak inhalation flows (PIFs; 135 and 360 l min(-1)) and two breathing frequencies [24 and 42 breaths per minute (BPM)] for a total of four cyclic flows (Flow A: 135 l min(-1) and 24 BPM; Flow B: 135 l min(-1) and 42 BPM; Flow C: 360 l min(-1) and 24 BPM; and Flow D: 360 l min(-1) and 42 BPM). Each experiment was performed using two different set-ups: the first set-up included both inhalations and exhalations through the filter media and test chamber, while with the second set-up, only inhalation flows were considered. The results showed that, for the most penetrating particle size range, an increase in both PIF and breathing frequency could potentially enhance the penetration with both set-ups; however, the effect of PIF was observed to be much more pronounced than that of frequency. The results indicated that with both set-ups, when the PIF was increased from 135 to 360 l min(-1) (for the given frequency: 24 or 42 BPM), an increase of up to 139-152% in penetration was observed. On the other hand, only a 10-16% increase in penetration occurred when the frequency was changed from 24 to 42 BPM (for a given PIF: 135 or 360 l min(-1)). This suggests that, from low to high respiratory efforts, a huge portion of penetration enhancement is due to PIF variations and only a small portion is contributed by frequency variations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".