Antibacterial/Antiviral Face Masks: Processing, Characteristics, Challenges, and Sustainability
Bibliographic record
Abstract
The face mask has become a part of our daily life after the emergence of SAR-CoV-2, commonly known as the novel coronavirus 2019 or, COVID-19 all over the world. On a day-to-day basis, previously the face mask has been used to filter airborne particles entering the body and affecting the respiratory system, especially by individuals in pollution-prone areas. But as the pathogens having severe acute respiratory disease-causing abilities emerge with the potential to create a pandemic, the necessity of virus/bacteria killing ability along with the filtration efficiency of the face mask has come into account. Existing ordinary face masks have filtration capacity only. Sometimes it cannot restrict particles and pathogens of nano or even micro-scale. Moreover, when it is disposed of after use, it can be a potential source of pathogen transmission. Therefore, the development of antiviral/antibacterial face masks is the need of the hour. This article focuses on the advancement of face mask processing methods, existing and promising antibacterial/antiviral agents, socio-economic sustainability, and challenges in achieving the goal of a green environment. Besides, various characteristics of the face mask like swelling and degradation properties, morphologies (SEM, FESEM), mechanical strength, antioxidant property, and antimicrobial activity are also revealed. Lastly, some future perspectives and directives are accordingly discussed with the hope that the grim of any future pandemic should not shroud us and make the world stalled again.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".