Internet-of-things-integrated molecularly imprinted polymer-based electrochemical nano-sensors for pesticide detection in the environment and food products

分子印迹聚合物 纳米传感器 生物传感器 杀虫剂 农药残留 纳米技术 分析物 化学 色谱法 材料科学 选择性 有机化学 生物 催化作用 农学
作者
Arpana Parihar,Palak Sharma,Nishant Kumar Choudhary,Raju Khan,Ebrahim Mostafavi
出处
期刊:Environmental Pollution [Elsevier BV]
卷期号:351: 124029-124029 被引量:50
标识
DOI:10.1016/j.envpol.2024.124029
摘要

To ensure environmental and health safety, relevant pollutants such as pesticides must be screened thoroughly to set their permissible limit. Various approaches have been used to identify pesticides such as capillary electrophoresis, gas and liquid-liquid chromatography, high-performance liquid chromatography, and enzyme-linked immune-absorbent tests. However, these techniques have some drawbacks, including time-consuming difficult steps, expensive bulky equipment, expert personnel, and a lack of selectivity. Recent advances in the field of biosensing have introduced biosensors for the onsite detection of pesticides which offer several advantages including rapid, simple, selective, sensitive, low-cost operation, and on-site detection. With the advent of molecularly imprinted polymer which substituted the traditional biorecognition elements (BREs) such as enzymes and antibodies, biosensors' sensitivity, selectivity, and reproducibility enhanced many folds. Molecularly imprinted polymers (MIP) are artificial polymer molecules that resemble natural BREs. They are synthesized when functional monomers are polymerized in the presence of a target analyte. Owing to the advantages of MIP, in this paper, the development of MIP-based electrochemical biosensors for pesticide detection is reviewed critically. A brief introduction to pesticides and the use of MIPs-based electrochemical sensors for pesticide detection is presented along with pros and cons. Further, Internet of Things (IoT) integrated MIP-based nanosensors for pesticide detection and information distribution have been discussed. In the end, future perspectives and challenges while implementing MIP-based nanosensors for onsite pesticide recognition have eventually been highlighted.
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