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
(T
50) 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.