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Updated: Jan 19, 2026

A Complete Method for Evaluating the Performance of Photocatalysts for the Degradation of Antibiotics in Environmental Remediation
Published on: October 6, 2022
Kinetic study and performance comparison of TiO2-mediated visible-light-responsive photocatalysts for the
Shang-Ming Huang1, Chih-Huang Weng2, Jing-Hua Tzeng3
1Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 40227, Taiwan; Department of Soil and Environmental Science, National Chung Hsing University, Taichung 40227, Taiwan.
Abstract:
Fungi are highly survived with exceptional resistance to environmental stress. Conventional fungicides are quite efficient, but the increase in use raises severe environmental problems. In this study, environmentally friendly TiO2-mediated visible-light-responsive photocatalysts, namely N-TiO2, N-T-TiO2, C-TiO2, and Pd-C-TiO2, were used to compare the performance of disinfecting a mold fungi Aspergillus niger. Key parameters, including photocatalyst dosage, the initial fungal concentration, and visible-light intensity, affecting the disinfecting process, was investigated. A new developed Light-responsive Modified Hom's (LMH) kinetic model incorporating visible-light intensity and photocatalyst light-absorption coefficient was firstly used to predict such photocatalytic process in fungal inactivation. Among the photocatalysts, Pd-C-TiO2 showed the highest inactivation performance against fungi, followed by C-TiO2, N-T-TiO2, and N-TiO2. In general, inactivation increased with increasing photocatalyst dosage and light intensity while decreased with increasing initial fungal concentration. For kinetic modeling, the LMH model supports the hypothesis that photocatalyst performance toward visible-light-driven fungal inactivation primarily depends on the light-absorption capacity of the photocatalyst. In conclusion, mold fungi Aspergillus niger are effectively disinfected by TiO2-mediated visible-light-responsive photocatalysts, and such fungal inactivation process could be predicted by LMH kinetic model.
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