Antibacterial and antiviral activity of a highly efficient electrospun r‐ <scp>PET</scp> nanofiber
Bibliographic record
Abstract
Abstract Since the COVID‐19 pandemic, the emergence of clean air has increased interest in developing antimicrobial air filters, targeting sterilization and public health concerns. Nanofibers are a promising approach due to their high efficiency in air filtration, while nanoparticles adhered to the fibre surfaces can improve safety due to the biocidal effects. In this study, CuNPs were developed using a green method in a redox reaction, with CuSO 4 ∙ 5H 2 O, ascorbic acid, and polyvinyl pyrrolidone (PVP). Membranes were prepared using recycled polyethylene terephthalate (PET) bottles (r‐PET) by electrospinning and the biocidal effect was given by applying CuNP in surface membranes by spraying. The collection time and rotation speed varied between 30 to 90 min and between 176 and 355 rpm, respectively. The permeability (k 1 ) and the particle collection efficiency (%) of the membranes were measured for each combination. Results showed the Darcy's permeability in order of 10 −12 m 2 , and overall efficiency up to 99.81% for particle diameters below 290 nm, with enhanced particle collection for nanoparticles (<100 nm). The membranes coated with copper nanoparticles (CuNP) showed a reduction of 99.99% for E. coli and S. aureus as gram‐negative and gram‐positive bacteria, respectively, even in low concentrations. Membranes coated with CuNP were effective against Yellow Fever and SARS‐CoV‐2 viruses, with viral reduction of 99.13% and 93.00%, respectively. The electrospun membranes developed in this study are versatile and can be utilized in various applications such as indoor air filters, portable air filters, wound dressings, medical equipment, and masks. Their usage enhances safety during material handling and usage.
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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) | 0.000 | 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".