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Trends in sensor development toward next-generation point-of-care testing for mercury

Mercury(编程语言) 纳米技术 环境科学 工艺工程 环境化学 生化工程 计算机科学 化学 材料科学 工程类 程序设计语言
作者
Ji Won Lim,Tai-Yong Kim,Min‐Ah Woo
出处
期刊:Biosensors and Bioelectronics [Elsevier]
卷期号:183: 113228-113228 被引量:59
标识
DOI:10.1016/j.bios.2021.113228
摘要

Mercury is one of the most common heavy metals and a major environmental pollutant that affects ecosystems. Since mercury and its compounds are toxic to humans, even at low concentrations, it is very important to monitor mercury contamination in water and foods. Although conventional mercury detection methods, including inductively coupled plasma mass spectrometry, atomic absorption spectroscopy, and gas chromatography–mass spectrometry, exhibit excellent sensitivity and accuracy, they require operation by an expert in a sophisticated and fully controlled laboratory environment. To overcome these limitations and realize point-of-care testing, many novel methods for direct sample analysis in the field have recently been developed by improving the speed and simplicity of detection. Commonly, these unconventional sensors rely on colorimetric, fluorescence, or electrochemical mechanisms to transduce signals from mercury. In the case of colorimetric and fluorescent sensors, benchtop methods have gradually evolved through technology convergence to give standalone platforms, such as paper-based assays and lab-on-a-chip systems, and portable measurement devices, such as smartphones. Electrochemical sensors that use screen-printed electrodes with carbon or metal nanomaterials or hybrid materials to improve sensitivity and stability also provide promising detection platforms. This review summarizes the current state of sensor platforms for the on-field detection of mercury with a focus on key features and recent developments. Furthermore, trends for next-generation mercury sensors are suggested based on a paradigm shift to the active integration of cutting-edge technologies, such as drones, systems based on artificial intelligence, machine learning, and three-dimensional printing, and high-quality smartphones.
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