Performance of nanofibrous media in portable air cleaners
Bibliographic record
Abstract
The benefits of nanofibrous media have been extensively explored in laboratory-scale research but are less clear in real filtration applications. Thus, this study investigated the links between the size-resolved filtration efficiency of nanofibrous media mounted in portable air cleaners in a chamber test and results from the media mounted in a cone-shape filter holder in a more conventional duct test. Results showed a similar trend for the filtration efficiency curves for these two experimental tests, despite differences in test type, challenge aerosol, instrumentation, and the calculation of filtration efficiency. Long-term operation, surface area blockage, and isopropyl alcohol treatment adversely impacted the filtration efficiency of the tested nanofibrous media in the chamber test, with a different magnitude of impact for three different media tested. The filtration efficiencies were lower than most of the previously reported data in the literature, which may be due to the differences in medium type, fiber diameter, filter thickness, porosity, face velocity, filter charge, and the number of filter layers. This study suggests the needs for determining the performance of nanofibrous media in terms of filtration efficiency and quality factor in real environmental systems.Copyright © 2021 American Association for Aerosol Research
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".