Design and development of multilayer cotton masks via machine learning.

With the ongoing COVID-19 pandemic, reusable high-performance cloth masks are recommended for the public to minimize virus spread and alleviate the demand for disposable surgical masks. However, the approach to design a high-performance cotton mask is still unclear. In this study, we aimed to find out the relationship between fabric properties and mask performance via experimental design and machine learning. Our work is the first reported work of employing machine learning to develop protective face masks. Here, we analyzed the characteristics of Egyptian cotton (EC) fabrics with different thread counts and measured the efficacy of triple-layered masks with different layer combinations and stacking orders. The filtration efficiencies of the triple-layered masks were related to the cotton properties and the layer combination. Stacking EC fabrics in the order of thread count 100-300-100 provides the best particle filtration efficiency (45.4%) and bacterial filtration efficiency (98.1%). Furthermore, these key performance metrics were correctly predicted using machine-learning models based on the physical characteristics of the constituent EC layers using Lasso and XGBoost machine-learning models. Our work showed that the machine learning-based prediction approach can be generalized to other material design problems to improve the efficiency of product development.

N95 respirator decontamination: a study in reusability.

The coronavirus disease 2019 (COVID-19) pandemic had caused a severe depletion of the worldwide supply of N95 respirators. The development of methods to effectively decontaminate N95 respirators while maintaining their integrity is crucial for respirator regeneration and reuse. In this study, we systematically evaluated five respirator decontamination methods using vaporized hydrogen peroxide (VHP) or ultraviolet (254 nm wavelength, UVC) radiation. Through testing the bioburden, filtration, fluid resistance, and fit (shape) of the decontaminated respirators, we found that the decontamination methods using BioQuell VHP, custom VHP container, Steris VHP, and Sterrad VHP effectively inactivated Cardiovirus (3-log10 reduction) and bacteria (6-log10 reduction) without compromising the respirator integrity after 2-15 cycles. Hope UVC system was capable of inactivating Cardiovirus (3-log10 reduction) but exhibited relatively poorer bactericidal activity. These methods are capable of decontaminating 10-1000 respirators per batch with varied decontamination times (10-200 min). Our findings show that N95 respirators treated by the previously mentioned decontamination methods are safe and effective for reuse by industry, laboratories, and hospitals.

Toward the prevention of coronavirus infection: what role can polymers play?

Severe acute respiratory syndrome-associated coronavirus 2 has caused a global public health crisis with high rates of infection and mortality. Treatment and prevention approaches include vaccine development, the design of small-molecule antiviral drugs, and macromolecular neutralizing antibodies. Polymers have been designed for effective virus inhibition and as antiviral drug delivery carriers. This review summarizes recent progress and provides a perspective on polymer-based approaches for the treatment and prevention of coronavirus infection. These polymer-based partners include polyanion/polycations, dendritic polymers, macromolecular prodrugs, and polymeric drug delivery systems that have the potential to significantly improve the efficacy of antiviral therapeutics.