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Photocatalytic Degradation of Antibiotics Using Nanomaterials: Mechanisms, Applications, and Future Perspectives

光催化 纳米技术 纳米材料 材料科学 生物炭 环境修复 人体净化 生化工程 降级(电信) 异质结 太阳能 环境退化 光伏系统 环境科学 地下水修复 过程(计算) 戒毒(替代医学) 可持续设计
作者
Jianwei Liu,Hongwei Ruan,Pengfei Duan,Peng Shao,Yang Zhou,Ying Wang,Yudi Chen,Zhiyong Yan,Yang Liu
出处
期刊:Nanomaterials [Multidisciplinary Digital Publishing Institute]
卷期号:16 (1): 49-49 被引量:25
标识
DOI:10.3390/nano16010049
摘要

Widespread antibiotic residues in aquatic environments pose escalating threats to ecological stability and human health, highlighting the urgent demand for effective remediation strategies. In recent years, photocatalytic technology based on advanced nanomaterials has emerged as a sustainable and efficient strategy for antibiotic degradation, enabling the effective utilization of solar energy for environmental remediation. This review provides an in-depth discussion of six representative categories of photocatalytic nanomaterials that have demonstrated remarkable performance in antibiotic degradation, including metal oxide-based systems with defect engineering and hollow architectures, bismuth-based semiconductors with narrow band gaps and heterojunction designs, silver-based plasmonic composites with enhanced light harvesting, metal–organic frameworks (MOFs) featuring tunable porosity and hybrid interfaces, carbon-based materials such as g-C3N4 and biochar that facilitate charge transfer and adsorption, and emerging MXene–semiconductor hybrids exhibiting exceptional conductivity and interfacial activity. The photocatalytic performance of these nanomaterials is compared in terms of degradation efficiency, recyclability, and visible-light response to evaluate their suitability for antibiotic degradation. Beyond parent compound removal, we emphasize transformation products, mineralization, and post-treatment toxicity evolution as critical metrics for assessing true detoxification and environmental risk. In addition, the incorporation of artificial intelligence into photocatalyst design, mechanistic modeling, and process optimization is highlighted as a promising direction for accelerating material innovation and advancing toward scalable, safe, and sustainable photocatalytic applications.
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