Performance Evaluation of Commercially Available Masks in Korea for Filtering Airborne Droplets Containing Bacteria

Evaluating Droplet Survivability on Face Masks with X-ray Microtomography

When a person talks, coughs, or sneezes, respiratory droplets are expelled and inevitably land on several surfaces, representing a route for respiratory disease transmission. Here, face masks act as a barrier by obstructing the passage of droplets during exhalation and inhalation. Being constantly exposed to respiratory events and carrying droplet residue, understanding the evaporation and absorption dynamics for tiny droplets on face masks and the fate of viral particle deposition is necessary to analyze the contamination risk. We explore the ideal design for masks from the interaction of mask surfaces with surrogate respiratory droplets by X-ray microscopy and microtomography. We show that the respiratory droplet survivability is significantly reduced in masks with a hydrophilic surface where absorption takes place, leading to a reduction of the postevaporation droplet residue at the mask surface compared with a hydrophobic surface. The results allow us to propose a better mask layer design dependent on wettability, reducing the risk of contamination from respiratory droplets.

Infrastructure in the Age of Pandemics: Utilizing Polypropylene-Based Mask Waste for Durable and Sustainable Road Pavements

When navigating the environmental exigencies precipitated by global pandemics, the escalation of mask waste presents a multifaceted dilemma. In this avant-garde research, we unveil a novel approach: harnessing the sterilized shredded mask residues (SMRs), predominantly composed of 100 wt. % polypropylene, as pioneering modifiers for asphalt. Distinct proportions of SMR (e.g., 3, 6, and 9 wt. %) were judiciously integrated with fresh-virgin base AP-5 asphalt and subjected to an extensive suite of state-of-the-art examinations, encompassing thin-layer chromatography-flame ionization detection (TLC-FID), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and specific rheological metrics. The TLC-FID diagnostic trajectories highlighted the nuanced rejuvenating influence of SMR on the binder, a facet reinforced by a pronounced elevation in the thermodynamic stability index (IC). The FT-IR spectra elucidated SMR's preeminent role as a filler, negating notions of chemical reactivity. The TGA analyses unveiled an elevated thermal onset of degradation, signposting enhanced thermal resilience, whereas the DSC readings illuminated a superior thermal comportment at lower extremities. The SEM evaluations rendered a clearer panorama: there was heightened textural perturbation at escalated SMR incorporations, yet the 3 wt. % concoction showcased an optimal, coherent microtexture symbiosis with asphalt. The rheological scrutinies revealed a systematic trajectory: a diminishing penetration and ductility countered by ascending softening points and viscosity metrics. The coup de maître stemmed from the DSR analyses, unequivocally validating SMR's unparalleled prowess in curtailing rutting distress. This seminal inquiry not only posits a blueprint for refined pavement longevity but also champions a sustainable countermeasure to pandemic-propelled waste, epitomizing the confluence of environmental prudence an d infrastructural fortitude.

Mask-Wearing Behaviors after Two Years of Wearing Masks Due to COVID-19 in Korea: A Cross-Sectional Study

In Korea, wearing masks in public places has become the norm during the prolonged coronavirus disease 2019 (COVID-19) pandemic. This cross-sectional study investigated the mask-wearing behavior of Koreans (n = 433) via online mode living in Seoul and Gwangju after wearing a mask in public spaces for two years due to COVID-19. The respondents selected their face masks based on season, gender, age, occupation, mask-wearing hours, mask filter performance, mask shape, and mask color. The general discomfort caused by wearing a mask was divided into physical and speech discomfort, and it was not correlated with anxiety when not wearing a face mask. Speech discomfort caused by wearing a mask was correlated with general discomfort, clear speech, vocal pain, anxiety, and only-indoor mask-off plans. Anxiety when not wearing a mask appeared to affect both indoor and outdoor mask-off plans. The more uncomfortable and less anxious respondents were when not wearing a mask, the sooner they wanted to discontinue wearing masks indoors and outdoors. It is expected that the use of masks will continue in the future and that there may be differences in the place and time of use of masks in Korea and around the world due to new infectious diseases and fine dust. Facial masks can be worn more comfortably and conveniently if the discomfort and anxiety of wearing a mask are improved by considering various behaviors when wearing a mask in the future.

Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19

The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for identifying people who do not wear a face mask. However, not all face masks or coverings are equally effective in preventing virus transmission or illness caused by viruses and therefore, it appears important for those systems to incorporate the ability to distinguish between the different types of face masks. This paper implements four pre-trained deep transfer learning models (NasNetMobile, MobileNetv2, ResNet101v2, and ResNet152v2) to classify images based on the type of face mask (KN95, N95, surgical and cloth) worn by people. Experimental results indicate that the deep residual networks (ResNet101v2 and ResNet152v2) provide the best performance with the highest accuracy and the lowest loss.