Study of Mask Fibers for Protection Against SARS-Cov-2 Via Luminescent Aerosolized Silicon Nanoparticles
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».