Study of Mask Fibers for Protection Against SARS-Cov-2 Via Luminescent Aerosolized Silicon Nanoparticles
Bibliographic record
Abstract
A highly contagious respiratory virus called SARS-CoV-2 began in Wuhan, China in end of 2019. It initiated a world-wide pandemic in 2020. Countries took extreme actions to avoid the transmission of such virus by implying restrictions in wearing personal protective equipment, which includes wearing masks [1-6]. The main reason behind the spread of this viral disease is sneezing and coughing, rendering the wearing of masks highly important for personal protection. Masks are usually used to prevent the transmission of pathogenic microorganisms from symptomatic and asymptomatic carriers to those who are in contact with them, wherein the mask filters the microorganisms from getting into the respiratory secretions. A common type of mask that is used due to its wide availability is the polyethylene (PE) filter mask. In this work, 3 nm silicon nanoparticles (Si-NPs) were used to model the SARS-CoV-2. The specific Si-NPs were chosen due to their chemical activity, ultra-small nature, easy attachment to other materials through chemical bonding, high luminescence and hydrophobicity, which makes clusters of diameters of 100-300 nm, which is similar to the virus size. Both N95 and surgical masks are investigated in this work using the Si-NPs. Si-NPs were used to examine the filtering process of the mask. The developed Si-NPs were dispersed in isopropyl alcohol (IPA) and filled into spraying bottle. The spraying bottle produces a cloud of droplets ranging from 40 to 900 mm, which is used to mimic the sneeze, where the droplets size is between 20 to 900 mm per spray. Testing the spraying was done on a Si wafer under UV radiation to picture the dropping pattern of the particles on the surface. To visualize the filtering process of the mask, we placed the mask between the prepared spraying bottle and 3x3 cm Si wafer that is mounted over a foam surface and above table surface. The testing conditions were carried in constant mode. The N95 mask fibers were studied by optical imaging and scanning electron microscopy (SEM) imaging. The SEM image show that the fibers have diameter of about 25 mm and that the roughness and surface typography is random. The mask fibers trap the Si-NPs creating clusters of fibers around it, which is not detected in the mask without the NPs. To examine the mask under UV, a luminescence image was taken for the mask with NPs and without NPs. The image shows bright red/orange luminescence for the mask with NPs, which indicates nano particles bonded to the fibers as a SARS-CoV-2 would do. A section of the N95 mask was taken and studied under UV light. The mask with the sprayed NPs showed very bright luminescence emitted from the mask fibers, which is not noticed without the NPs. To summarize, aerosolized Si nanoparticles were used visualize how mask are used to filter out SARS-CoV-2. A setup was created to test the mask, where Si-NPs were sprayed on to the mask from certain distance and a UV- induced fluorescence was used to observe the NPs being sprayed and reaching masks. The study showed that mask fibers are good at filtering nano-scale virus that led to infections. This indicates that the mask filter is effective in preventing the SARS-CoV-2 virus infection. References [1] “Timeline: WHO's COVID-19 response,” World Health Organization. [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/interactive-timeline#event-0. [Accessed: 10-Nov-2020]. [2] S. Verma, M. Dhanak, and J. Frankenfield, “Visualizing the effectiveness of face masks in obstructing respiratory jets,” Physics of Fluids, vol. 32, no. 6, p. 061708, 2020. [3] C. R. MacIntyre, S. Cauchemez, D. E. Dwyer, H. Seale, P. Cheung, G. Browne, M. Fasher, J. Wood, Z. Gao, R. Booy, and N. Ferguson, “Face Mask Use and Control of Respiratory Virus Transmission in Households,” Emerging Infectious Diseases, vol. 15, no. 2, pp. 233–241, 2009. [4] J. W. Tang, T. J. Liebner, B. A. Craven, and G. S. Settles, “A schlieren optical study of the human cough with and without wearing masks for aerosol infection control,” Journal of The Royal Society Interface, vol. 6, no. suppl_6, 2009. [5] J. Xiao, E. Y. Shiu, H. Gao, J. Y. Wong, M. W. Fong, S. Ryu, and B. J. Cowling, “Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings—Personal Protective and Environmental Measures,” Emerging Infectious Diseases, vol. 26, no. 5, pp. 967–975, 2020. [6] V. K. Midha and A. Dakuri, “Spun bonding Technology and Fabric Properties: a Review,” Journal of Textile Engineering & Fashion Technology, vol. 1, no. 4, 2017. Figure 1
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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) | 0.001 | 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".