Abstract

The pandemic revealed significant gaps in our understanding of the antiviral potential of porous textiles used for personal protective equipment and nonporous touch surfaces. What is the fate of a microbe when it encounters an abiotic surface? How can we change the microenvironment of materials to improve antimicrobial properties? Filling these gaps requires increasing data generation throughput. A method to accomplish this leverages the use of the enveloped bacteriophage ϕ6, an adjustable spacing multichannel pipette, and the statistical design opportunities inherent in the ordered array of the 24-well culture plate format, resulting in a semi-automated small drop assay. For 100 mm2 nonporous coupons of Cu and Zn, the reduction in ϕ6 infectivity fits first-order kinetics, resulting in half-lives (T50) of 4.2 ± 0.1 and 29.4 ± 1.6 min, respectively. In contrast, exposure to stainless steel has no significant effect on infectivity. For porous textiles, differences associated with composition, color, and surface treatment of samples are detected within 5 min of exposure. Half-lives for differently dyed Zn-containing fabrics from commercially available masks ranged from 2.1 ± 0.05 to 9.4 ± 0.2 min. A path toward full automation and the application of machine learning techniques to guide combinatorial material engineering is presented.

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