Functionalized Masks: Powerful Materials against COVID‐19 and Future Pandemics
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 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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