  Bacteria can swim upstream due to hydrodynamic interactions with the fluid
flow in a narrow tube, and pose a clinical threat of urinary tract infection to
patients implanted with catheters. Coatings and structured surfaces have been
proposed as a way to suppress bacterial contamination in catheters. However,
there is no surface structuring or coating approach to date that thoroughly
addresses the contamination problem. Here, based on the physical mechanism of
upstream swimming, we propose a novel geometric design, optimized by an AI
model predicting in-flow bacterial dynamics. The AI method, based on Fourier
neural operator, offers significant speedups over traditional simulation
methods. Using Escherichia coli, we demonstrate the anti-infection mechanism in
quasi-2D micro-fluidic experiments and evaluate the effectiveness of the design
in 3Dprinted prototype catheters under clinical flow rates. Our catheter design
shows 1-2 orders of magnitude improved suppression of bacterial contamination
at the upstream end of the catheter, potentially prolonging the in-dwelling
time for catheter use and reducing the overall risk of catheter-associated
urinary tract infections.
