Applying the Berberine-Pretreated Filter for Inactivating Bioaerosols
Bibliographic record
Abstract
This work considers the effects of using the berberine pretreated filters (BPFs) as the antiseptic filters on the bioaerosol penetration. Two concentrations of berberine solutions were used to coat on the polypropylene fibrous filter. The Escherichia coli (E. coli), and Bacillus subtilis (B. subtilis) bioaerosols were generated using a Collison nebulizer, as the challenged bioaerosols. The effects of various factors, including the face velocity and the relative humidity on the bioaerosol collection characteristics were evaluated. Experimental results suggested the pretreatment of berberine did have an antiseptic effect on bacteria bioaerosol and increase the inactivation mechanism. The filter pretreated with a higher concentration of berberine has a stronger antiseptic effect on bioaerosols. The culturable survival of E. coli bioaerosols through the untreated filter, the 0.002 wt%, and 0.02 wt% BPFs are around 68%, 43% and 36%, respectively. In addition, the culturable survival of B. subtilis bioaerosols through the 0.002 wt%, and 0.02 wt% BPFs are around 66%, 51% and 43%, respectively. Moreover, the culturable survival of E. coli bioaerosol through the 0.002 wt% BPFs increased from 43% to 54% as the face velocity increased from 10 to 30 cm/s. These results indicated that the antiseptic of the BPFs decreased with face velocity.
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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) | 0.001 | 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 | 0.001 | 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".