Bej Sourav, Cho Eun-Bum
Energy Convergence Research Center, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811, Republic of Korea.
Energy Convergence Research Center, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811, Republic of Korea; Department of Fine Chemistry, Seoul National University of Science and Technology, 232, Gongneung-ro, Nowon-gu, Seoul, 01811, Republic of Korea; Institute for Applied Chemistry, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811, Republic of Korea.
Environ Res. 2025 Apr 1;270:120946. doi: 10.1016/j.envres.2025.120946. Epub 2025 Jan 28.
Unregulated discharge of antibiotics in waterbodies has posed a significant threat to the aquatic flora and fauna in post-pandemic times. This alarming situation has ascertained the need for suitable sensors to detect persistent antibiotic residues. In this context, functional hybrid materials centralized on reticular metal-organic frameworks (MOFs)/composites have been a research hot spot for photoelectrochemical host-guest recognition events over the past two decades. The unique amalgamation of the robust framework, ease of synthesis, and tunable photophysical properties complemented with in silico approaches render these materials highly promising for recognition events over other contemporaries. The present review provides a critical analysis of the state-of-the-art advancement of MOFs along with their allied composites toward the detection of targeted amino-drug residues (nitrofurazone, norfloxacin, ciprofloxacin, tetracycline, acetaminophen) within the last quinquennial period (approximately 2019-2024). Detection of the targeted drug residues by electrochemical and fluorometric pathways and their host-guest mechanistic pathways have been precisely described. Additionally, different functionalization methods and luminescence strategies with their structural viewpoint have been concisely summarized. Moreover, we delve into the futuristic possibility of integrating artificial intelligence (AI) and machine learning (ML) for better quantification of antibiotics. Finally, the unmet challenges and future research directions of the current research strategies have been outlined for automatic ML (AutoML) assisted next-generation POCT device fabrication.
疫情后时代,水体中抗生素的无节制排放对水生动植物构成了重大威胁。这种令人担忧的情况确定了需要合适的传感器来检测持久性抗生素残留。在此背景下,以网状金属有机框架(MOF)/复合材料为核心的功能杂化材料在过去二十年中一直是光电化学主客体识别事件的研究热点。坚固的框架、易于合成、可调节的光物理性质以及计算机模拟方法的独特结合,使这些材料在识别事件方面比其他同类材料更具前景。本综述对MOF及其相关复合材料在过去五年(约2019 - 2024年)检测目标氨基药物残留(呋喃西林、诺氟沙星、环丙沙星、四环素、对乙酰氨基酚)方面的最新进展进行了批判性分析。精确描述了通过电化学和荧光途径检测目标药物残留及其主客体作用机制途径。此外,还简要总结了不同的功能化方法和发光策略及其结构观点。此外,我们探讨了整合人工智能(AI)和机器学习(ML)以更好地定量抗生素的未来可能性。最后,针对自动ML(AutoML)辅助下一代即时检测(POCT)设备制造,概述了当前研究策略尚未解决的挑战和未来研究方向。