Structural characterization and catalytic sterilization performance of a TiO₂ nano-photocatalyst

In view of the food safety and hygiene issues caused by pathogenic microorganisms, tetrabutyl titanate was used as a precursor for the preparation of a TiO₂ nano-semiconductor photocatalyst via the sol-gel process. The plate count method was then adopted to investigate the photocatalytic sterilization performance of the synthesized TiO₂ nanoparticles toward Escherichia coli, Staphylococcus aureus, and Candida albicans. Subsequently, a backpropagation (BP) neural network model was developed to predict the photocatalytic sterilization performance. The photocatalyst was structurally characterized by the Brunauer-Emmett-Teller method for specific surface area determination, transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The results indicated that the prepared TiO₂ nano-photocatalyst was of high purity with a specific surface area of 76.5 m²/g and the particle size range 15-18 nm. The nanoparticles exhibited characteristic peaks corresponding to the oxide component Ti-O, hydroxyl group ˙OH and oxygen chemisorbed and presented an anatase-dominated multiphase structure that enhanced the photocatalytic performance. UV irradiation at 254 nm produced better sterilization effects on E. coli, S. aureus, and C. albicans, with elimination rates after 30 min of reaction of 97.8%, 99.4%, and 93.6%, respectively. These results indicated that the TiO₂ nano-photocatalyst is a promising environmentally friendly catalyst with good sterilization performance. The constructed BP neural network also exhibited high training accuracy and good generalization ability, with correlation coefficients between the network-predicted and experimental target values of 0.9789. These results support research on the intelligent processing of photocatalytic sterilization with TiO₂ nanoparticles.