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Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset

journal contribution
submitted on 2024-05-08, 04:52 and posted on 2024-05-08, 04:53 authored by Heesoo Park, El Tayeb Bentria, Sami Rtimi, Abdelilah Arredouani, Halima Bensmail, Fedwa El-Mellouhi

Nowadays, most experiments to synthesize and test photocatalytic antimicrobial materials are based on trial and error. More often than not, the mechanism of action of the antimicrobial activity is unknown for a large spectrum of microorganisms. Here, we propose a scheme to speed up the design and optimization of photocatalytic antimicrobial surfaces tailored to give a balanced production of reactive oxygen species (ROS) upon illumination. Using an experiment-to-machine-learning scheme applied to a limited experimental dataset, we built a model that can predict the photocatalytic activity of materials for antimicrobial applications over a wide range of material compositions. This machine-learning-assisted strategy offers the opportunity to reduce the cost, labor, time, and precursors consumed during experiments that are based on trial and error. Our strategy may significantly accelerate the large-scale deployment of photocatalysts as a promising route to mitigate fomite transmission of pathogens (bacteria, viruses, fungi) in hospital settings and public places.

Other Information

Published in: Catalysts
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.3390/catal11081001

History

Language

  • English

Publisher

MDPI

Publication Year

  • 2021

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Health and Life Sciences - HBKU
  • Qatar Environment and Energy Research Institute - HBKU
  • Qatar Computing Research Institute - HBKU
  • Qatar Biomedical Research Institute - HBKU
  • Diabetes Research Center - QBRI

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    Qatar Environment and Energy Research Institute - HBKU

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