Monitoring multi-respiratory indices via a smart nanofibrous mask filter based on a triboelectric nanogenerator

Abstract

Abstract Respiratory parameters, such as respiratory rate (RR), inhalation time (tin), exhalation time (tex), and their ratio (IER=tin/tex), are of great importance to indicate clinical differences between healthy people and those with respiratory diseases. Herein, we report a respiration monitoring triboelectric nanogenerator (RM-TENG) with nanofibrous membranes, which can be used as a smart, changeable, self-powered mask filter with high filtration efficiency for monitoring multiple respiratory indices (e.g., RR, tin, tex, IER). We created a mathematical model to quantitatively analyze the effects of gap distance between two triboelectric layers on the contact area by recording the nanofibers layer s deformation profile with digital image correlation (DIC) tests. The RM-TENG is more sensitive to smaller gap distances between 1\xa0mm and 5\xa0mm because the high specific area of nanofibers can provide a more effective contact area. An RM-TENG built with optimized structure parameters can accurately and consistently detect the above-mentioned respiratory indices with excellent sensing stability for 40\xa0h. The monitored RR and IER have 100% and 93.53% agreement with the real-time RR and IER set on the ventilator, respectively. Furthermore, it has a filtration efficiency of 99\xa0wt% for particle sizes between 0.3\xa0µm and 5\xa0µm. This study introduces a mask filter fabricated with a simple structure with both filtering and sensing capability, which has excellent potential for self-powered health diagnostics.