Biopolymer Coating Imparts Sustainable Self-Disinfecting and Antimicrobial Properties to Fabric: Translated to Protective Gears for the Pandemic and Beyond.

The global pandemic of COVID-19 and emerging antimicrobial drug resistance highlights the need for sustainable technology that enables more preparedness and active control measures. It is thus important to have a reliable solution to avert the present situations as well as preserve nature for habitable life in the future. One time use of PPE kits is promoting the accumulation of nondegradable waste, which may pose an unforeseen challenge in the future. We have developed a biocompatible, biodegradable, and nonirritating nanoemulsion coating for textiles. The study focused on coating cotton fabric to functionalize it with broad spectrum antimicrobial, antibiofilm, and anti-SARS-CoV-2 activity. The nanoemulsion comprises spherical particles of chitosan, oleic acid, and eugenol that are cross-linked to fibers. The nanoemulsion caused complete destruction of pathogens even for the most rigid biofilms formed by drug resistant Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans on the surface of the coated fabric. The secondary coat with beeswax imparts super hydrophobicity and 20 wash cycle resistance and leads to enhanced barrier properties with superior particulate filtration, bacterial filtration, and viral penetration efficiency as compared to an N95 respirator. The coated fabric qualifies as per standard parameters like breathability, flammability, splash resistance, and filtration efficiency for submicrometer particles, bacteria, and viruses. The scaleup and bulk manufacturing of the coating technology on fabric masks complied with standards. The consumer feedback rated the coated mask with high scores in breathability and comfortability as compared to an N95. The strategy promises to provide a long-term sustainable model compared to single use masks and PPE that will remain a nondegradable burden on the ecosystem for years to come.

Mask Waste: A Sustainable Mask-Based Epoxy Resin/SiO2 Composite for Efficient Purification of Water-in-Oil Emulsions

Since the emergence of the COVID-19 pandemic, there has been a tremendous increase in the production of masks worldwide, with more than 1.5 billion masks having been disposed of during this time. The damage caused by mask pollution is a global threat;highlighting the need to dispose of discarded masks correctly. Herein, we report a recycling approach that uses discarded masks to fabricate a superhydrophobic epoxy resin/SiO2 membrane for separating emulsions. The composite has a high flux value (2123 L. m(-2).h(-1)) and high separation efficiency (>98%). The filter maintained its excellent superhydrophobic property (WCA > 150 degrees) after tape-peel cycles, clamping cycles with tweezers, abrasion cycles with 800 grit SiC sandpaper, pressure with fingertips, and kneading cycles. This study proposes a renewable, eco-friendly, and low-cost product, which can be used for oil spill cleanup and water purification. The filter not only removes oil from oily wastewater (such as oil spills) but also solves pollution caused by discarded masks. This study provides insights for resource recovery that may contribute to the purification of oily water emulsions.

Can Machine Learning and Nanotechnology Help the Fight Against the Current COVID-19 Crisis?

The entire world is now in a state of caution since the outbreak of the COVID-19 pandemic. The overwhelmingly high spread and mortality rate due to the SARS-CoV-2 virus has not only made the headlines but also raised alarming concerns for the human community. Applications of nano-biotechnology, along with machine learning, have excellent potential in dealing with serious health issues, mainly in medical science. This review article aims to augment the multidimensional use of silver nanoparticles, especially in the fabrication of textiles and face masks, which could represent a new avenue for prevention. Furthermore, the disinfection of COVID-19, along with other pathogens using silver nanoparticles and machine learning could help in the risk assessment.