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In Environmental science & technology ; h5-index 132.0

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.

Reiss Rebecca A, Makhnin Oleg, Lowe Terry C


SARS CoV-2, antimicrobial, antiviral, copper, materials testing, virucidal, zinc