Evaluation of wearing comfort of dust masks

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.

Evaluation of the degradation of the graphene-polypropylene composites of masks in harsh working conditions

The recent COVID-19 outbreak has led health authorities to recommend at least the use of surgical masks, most preferably respirators (FFP2 or KN95), to prevent the spread of the virus. Non-woven fabrics have been chosen as the best option to manufacture the face masks, due to their filtration efficiency, low cost, and versatility. Modifying the mask filters with graphene has been of great interest due to its potential use as antibacterial and virucidal properties. Indeed, some companies have commercialized face masks in which graphene is coated and/or embedded. However, the Canadian sanitary authorities advised against using the Shandong Shengquan New Materials Co. graphene masks because of the possibility of pulmonary damage produced by graphene inhalation. Thus, we have analyzed the stability of the graphene filter of these masks and compared it with two other commercially available graphene mask filters, evaluating the morphological and spectroscopical change of the fibers, as well as the particles released during the endurance tests. Our work introduces the necessary tools and methodology to evaluate the potential degradation of face masks under extreme working conditions. These methods complement the present standard tests ensuring the security of the new filters based on composites or nanomaterials.

Speech Impaired by Half Masks Used for the Respiratory Tract Protection

Filtering half masks belong to the group of personal protective equipment in the work environment. They protect the respiratory tract but may hinder breath and suppress speech. The present work is focused on the attenuation of sound by the half masks known as “filtering facepieces”, FFPs, of various construction and filtration efficiency. Rather than study the perception of speech by humans, we used a generator of white noise and artificial speech to obtain objective characteristics of the attenuation. The generator speaker was either covered by an FFP or remained uncovered while a class 1 meter measured sound pressure levels in 1/3 octave bands with center frequencies 100–20 kHz at distances from 1 to 5 m from the speaker. All five FFPs suppressed acoustic waves from the octave bands with center frequencies of 1 kHz and higher, i.e., in the frequency range responsible for 80% of the perceived speech intelligibility, particularly in the 2 kHz-octave band. FFPs of higher filtration efficiency stronger attenuated the sound. Moreover, the FFPs changed the voice timbre because the attenuation depended on the wave frequency. The two combined factors can impede speech intelligibility.

Electrospun nanofiber-based respiratory face masks-a review

The severe acute respiratory syndrome coronavirus (SARS-CoV-2) pandemic of 2019 forced widespread use of face coverings as a mandatory step towards reducing infection by the virus. The face mask acts as a barrier for transmission of infected aerosols among its user and surrounding people. This has propelled pace of research and development of face masks around the world. This short review is an effort to present advances in materials and designs used for face masks. Details available in scientific literature and company brochures have been accessed and the use of nanomaterials and designs for the new generation of face masks have been discussed. Special attention was given to the face masks based on electrospun nanofiber-based membrane materials due to their nano-sized pores, light weight, and high filtration efficiency; therefore, they are commercially viable and popular among various products available in the market. Incorporation of metal organic framework (MOFs) and graphene have opened avenues to more advanced/multi-functional, reusable, and high capacity adsorption filtration membranes. Rapid prototyping/3-dimensional (3-D) printing techniques have been applied to shorten the time of manufacture of face masks. This review is expected to be very helpful for engineers, scientists, and entrepreneurs working on development of novel face masks required in plenty during this pandemic period.

Development and validation of the discomfort of cloth Masks-12 (DCM-12) scale

During the COVID-19 pandemic, the use of face masks by the public has helped to slow the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the community. Cloth masks have been recommended because of their effectiveness, availability, and reusability. Like other types of face masks, however, user discomfort while wearing cloth masks is thought to engender behaviors that limit the effectiveness of cloth masks as source control (e.g., adjusting or removing one's mask temporarily while in public). To design cloth masks that are more tolerable, a measurement instrument for assessing subjective user discomfort is needed. Across two studies, we identified and confirmed a two-dimensional factor structure underlying the discomfort of cloth masks - discomfort related to the breathability and discomfort related to the tightness of the mask against the face and head. Additionally, we provide replicable evidence that both factor-subscales predict the self-reported frequencies of problematic mask-wearing behaviors.

Quantifying face mask comfort

Face mask usage is one of the most effective ways to limit SARS-CoV-2 transmission, but a mask is only useful if user compliance is high. Through anonymous surveys (n = 679), it was shown that mask discomfort is the primary source of noncompliance in mask wearing. Further, through these surveys, three critical predicting variables that dictate mask comfort were identified: air resistance, water vapor permeability, and face temperature change. To validate these predicting variables in a physiological context, experiments (n = 9) were performed to measure the respiratory rate and change in face temperature while wearing different types of three commonly used masks. Finally, using values of these predicting variables from experiments and the literature, and surveys asking users to rate the comfort of various masks, three machine learning algorithms were trained and tested to generate overall comfort scores for those masks. Although all three models performed with an accuracy of approximately 70%, the multiple linear regression model provides a simple analytical expression to predict the comfort scores for common face masks provided the input predicting variables. As face mask usage is crucial during the COVID-19 pandemic, the goal of this quantitative framework to predict mask comfort is hoped to improve user experience and prevent discomfort-induced noncompliance.