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

Abstract

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.

Intelligent Algorithms Enable Photocatalyst Design and Performance Prediction

“…The MLP model has also been used to predict the degradation activity of TiO 2 in degrading Bisphenol A [111]. At present, many models, such as the support vector machine, multilayered feed-forward network, generalized additive model, convolutional neural network, adaptive neuro-fuzzy inference system, big data analysis, and deep learning models, have been used to predict the photocatalytic performance of photocatalysts [112][113][114][115][116][117][118][119]. It is worth noting that it is difficult for a single neural network model to meet the demand of prediction, and multiple models combined with training corresponding experimental data can effectively predict the photocatalytic performance of photocatalysts.…”

“…Catalysts 2024, 14, x FOR PEER REVIEW 15 of and deep learning models, have been used to predict the photocatalytic performance photocatalysts [112][113][114][115][116][117][118][119]. It is worth noting that it is difficult for a single neural netwo model to meet the demand of prediction, and multiple models combined with traini corresponding experimental data can effectively predict the photocatalytic performan of photocatalysts.…”

Adsorption and Inactivation of SARS-CoV-2 on the Surface of Anatase TiO2(101)

“…Oxide nanomaterials are promising candidates for the removal and inactivation of viral/bacterial infections as well as for preventing pathogen dissemination . Titanium dioxide (TiO 2 ), with unique properties such as low toxicity, high photoactivity, and high commercial availability compared to other semiconductors, has received considerable attention for environmental applications such as solid-state antiviral materials, self-cleaning surfaces, and air and water purification systems. , Multiple competing reaction pathways can be responsible for virus inactivation over nanomaterials, such as catalytic oxidation, metal ion release, photothermal effects, and formation of reactive oxygen species (ROS) . Among different advanced oxidation processes, which are used for the elimination of organic and inorganic pollutants and inactivation of microorganisms, photocatalysis is the most desirable method owing to the high ability in inactivation of different types of contaminants .…”

Distinctive Effects of Surface Roughness and Ions Release on the Bacterial Adhesion and Inactivation of Textured Copper Oxide Surfaces

“…Many germs have developed a resistance to a large spectrum of antibiotics and have become stronger than ever. These germs are present in water/wastewater, indoor environments, and fomites [1][2][3][4]. Many approaches have been used to fight infections in the indoor environment such as spraying, detergents, ventilation, etc.…”