Comparison of measurement systems for assessing number- and mass-based particle filtration efficiency
Bibliographic record
Abstract
The particle filtration efficiency (PFE) of a respirator or face mask is one of its key properties. While the physics of particle filtration results in the PFE being size-dependent, measurement standards are specified using a single, integrated PFE, for simplicity. This integrated PFE is commonly defined concerning either the number (NPFE) or mass (MPFE) distribution of particles as a function of size. This relationship is non-trivial; it is influenced by both the shape of the particle distribution and the fact that multiple practical definitions of particle size are used. This manuscript discusses the relationship between NPFE and MPFE in detail, providing a guide to practitioners. Our discussion begins with a description of the theory underlying different variants of PFE. We then present experimental results for a database of size-resolved PFE (SPFE) measurements for several thousand candidate respirators and filter media, including filter media with systematically varied properties and commercial samples that span 20%-99.8% MPFE. The observed relationships between NPFE and MPFE are discussed in terms of the most-penetrating particle size (MPPS) and charge state of the media. For the sodium chloride particles used here, we observed that the MPFE was greater than NPFE for charged materials and vice versa for uncharged materials. This relationship is observed because a shift from NPFE to MPFE weights the distribution toward larger sizes, while charged materials shift the MPPS to smaller sizes. Results are validated by comparing the output of a pair of automated filter testers, which are used in gauging standards compliance, to that of MPFE computed from a system capable of measuring SPFE over the 20 nm-500 nm range.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".