Current facemask research focuses on material characterization and efficiency; however, facemasks are often not tested such that aerosol distributions are evaluated from the gaps in the sides, bottom, and nose areas. Poor evaluation methods could lead to misinformation on optimal facemasks use; a high-throughput, reproducible method which illuminates the issue of fit influencing aerosol transmission is needed. To this end, we have created an in vitro model to quantify particle transmission by mimicking exhalation aerosols in a 3D printed face-nose-mouth replica via a nebulizer and quantifying particle counts using a hand-held particle counter. A sewn, sewn with pipe cleaner nose piece, and sewn with a coffee filter facemask were used to evaluate current common homemade sewn facemask designs, benchmarked against industry standard surgical, N95 respirator tightly fit, and N95 respirator loosely fit facemasks. All facemasks have significantly reduced particle counts in front of the facemask, but the side and top of the facemask showed increases in particle counts over the no facemask condition at that same position, suggesting that some proportion of aerosols are being redirected to these gaps. An altered size distribution of aerosols that escape at the vulnerable positions was observed; escaped particles have larger count median diameters, with a decreased ratio of smaller to larger particles, possibly due to hygroscopic growth or aggregation. Of the homemade sewn facemasks, the facemask with a coffee filter insert performed the best at reducing escaped aerosols, with increased efficiency also observed for sewn masks with a pipe cleaner nose piece. Importantly, there were minimal differences between facemasks at increasing distances, which supports that social distance is a critical element in reducing aerosol transmission. This work brings to light the importance of quantifying particle count in positions other than directly in front of the facemask and identifies areas of research to be explored.
Material characterization and selection for 3D-printed spine models
