Antibacterial/Antiviral Face Masks: Processing, Characteristics, Challenges, and Sustainability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 it