Charge Protection in Electret Air Filtration Nonwoven Materials
Bibliographic record
Abstract
Abstract Nonwoven media used as electret air filters are often embedded with charges to improve particle capture efficiency. These charged filters are invariably exposed to low surface tension fluids such as oils and alcohols leading to charge loss. In this study, filtration media are endowed with charge protection through increased surface repellency using melt additives that can migrate to the surface during processing. Nonwovens containing fluorochemical melt additives are produced, and examined to determine the relationship between surface chemistry, isopropyl alcohol (IPA) repellency, resultant charge retention, and filtration characteristics. Surface fluorine/carbon (F/C) ratios of ≈0.2 are sufficient to protect filtration performance from vapor discharging methods. Samples with bulk additive loadings of 1.2% or higher are found to achieve the necessary repellency to resist discharging independent of the migration state of the sample, while samples loaded at the 0.6% level required sufficient migration to achieve the requisite F/C ratio of 0.2 in order to be protected. Samples that achieved the necessary surface chemistry to provide significant IPA repellency retained > 80% of electret charge and corresponding filtration performance. These results have special significance in the design of filtration media relevant in global healthcare and other industrial settings.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".