Zhang Yihao, Li Jiaxuan, Zhou Yu, Zhang Xu, Liu Xianhua
School of Environmental Science and Engineering, Tianjin University, Tianjin 300354, China.
Sensors (Basel). 2024 Jul 4;24(13):4350. doi: 10.3390/s24134350.
Water pollution greatly impacts humans and ecosystems, so a series of policies have been enacted to control it. The first step in performing pollution control is to detect contaminants in the water. Various methods have been proposed for water quality testing, such as spectroscopy, chromatography, and electrochemical techniques. However, traditional testing methods require the utilization of laboratory equipment, which is large and not suitable for real-time testing in the field. Microfluidic devices can overcome the limitations of traditional testing instruments and have become an efficient and convenient tool for water quality analysis. At the same time, artificial intelligence is an ideal means of recognizing, classifying, and predicting data obtained from microfluidic systems. Microfluidic devices based on artificial intelligence and machine learning are being developed with great significance for the next generation of water quality monitoring systems. This review begins with a brief introduction to the algorithms involved in artificial intelligence and the materials used in the fabrication and detection techniques of microfluidic platforms. Then, the latest research development of combining the two for pollutant detection in water bodies, including heavy metals, pesticides, micro- and nanoplastics, and microalgae, is mainly introduced. Finally, the challenges encountered and the future directions of detection methods based on industrial intelligence and microfluidic chips are discussed.
水污染对人类和生态系统有重大影响，因此已制定了一系列政策来加以控制。进行污染控制的第一步是检测水中的污染物。已提出了各种水质检测方法，如光谱法、色谱法和电化学技术。然而，传统检测方法需要使用实验室设备，这些设备体积庞大，不适合在现场进行实时检测。微流控装置可以克服传统检测仪器的局限性，已成为水质分析的高效便捷工具。同时，人工智能是识别、分类和预测从微流控系统获得的数据的理想手段。基于人工智能和机器学习的微流控装置正在被开发，这对下一代水质监测系统具有重要意义。本文综述首先简要介绍人工智能所涉及的算法以及微流控平台制造和检测技术中使用的材料。然后，主要介绍了将两者结合用于水体中污染物检测的最新研究进展，包括重金属、农药、微塑料和纳米塑料以及微藻。最后，讨论了基于人工智能和微流控芯片的检测方法所面临的挑战和未来发展方向。
