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Electrochemical sensor based on molecularly imprinted polymer for sensitive triclosan detection in wastewater and mineral water

检出限 分子印迹聚合物 微分脉冲伏安法 循环伏安法 三氯生 电化学气体传感器 介电谱 化学 材料科学 傅里叶变换红外光谱 电化学 电极 化学工程 色谱法 有机化学 催化作用 物理化学 病理 工程类 选择性 医学
作者
Soukaina Motia,Ioan Albert Tudor,Paulo A. Ribeiro,Maria Raposo,Benachir Bouchikhi,Nezha El Bari
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:664: 647-658 被引量:58
标识
DOI:10.1016/j.scitotenv.2019.01.331
摘要

Triclosan (TCS) is a topical antiseptic widely used in different cosmetic products. It is also a common additive in many antimicrobial household consumables. Over a certain concentration, it becomes risky for human and environmental health. This work describes the development of an electrochemical sensor based on molecularly imprinted polymer (MIP), assembled on screen-printed gold electrode (Au-SPE), dedicated to the TCS detection in environmental water sources. To achieve this goal, an acrylamide/bisacrylamide solution was polymerized after linking TCS with the carboxylic polyvinyl chloride (PVC-COOH) layer onto the Au-SPE. The sensor device fabrication and its retention capabilities were characterized through cyclic voltammetry (CV), differential pulse voltammetry (DPV), electrochemical impedance spectroscopy (EIS), atomic force microscopy (AFM) and Fourier transform infrared (FTIR) spectroscopy. As control experiment, negligible responses were obtained during the non-imprinted polymer (NIP) test. The sensor could effectively detect TCS avoiding interferences of structural similar substances like 2,4,6-trichlorophenol and catechol. Under optimal conditions, the sensor responses were found logarithmic in the concentration range from 0.1 to 1000 pg mL-1. Indeed, compared with reported works, this sensor exhibits lower detection limit (LOD) and quantification limit (LOQ) of 0.23 and 0.78 pg mL-1, respectively. The developed sensor was effectively applied to wastewater samples for TCS detection and displayed satisfactory performances. Moreover, the different wastewater samples, regarding their TCS contents, were correctly classified by using principal component analysis (PCA) technique. Correspondingly, this work has demonstrated a cheap, simple and effective sensing platform for TCS detection thus making it a promising tool for future evolution of accurate and reliable environmental analysis.
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