Functionalized Masks: Powerful Materials against COVID‐19 and Future Pandemics
Bibliographic record
Abstract
The outbreak of COVID-19 revealed the vulnerability of commercially available face masks. Without having antibacterial/antiviral activities, the current masks act only as filtering materials of the aerosols containing microorganisms. Meanwhile, in surgical masks, the viral and bacterial filtration highly depends on the electrostatic charges of masks. These electrostatic charges disappear after 8 h, which leads to a significant decline in filtration efficiency. Therefore, to enhance the masks' protection performance, fabrication of innovative masks with more advanced functions is in urgent demand. This review summarizes the various functionalizing agents which can endow four important functions in the masks including i) boosting the antimicrobial and self-disinfectant characteristics via incorporating metal nanoparticles or photosensitizers, ii) increasing the self-cleaning by inserting superhydrophobic materials such as graphenes and alkyl silanes, iii) creating photo/electrothermal properties by forming graphene and metal thin films within the masks, and iv) incorporating triboelectric nanogenerators among the friction layers of masks to stabilize the electrostatic charges and facilitating the recharging of masks. The strategies for creating these properties toward the functionalized masks are discussed in detail. The effectiveness and limitation of each method in generating the desired properties are well-explained along with addressing the prospects for the future development of masks.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".