Suppr超能文献

基于 CdMoO/g-CN 纳米复合材料的苯菌灵印迹电化学传感器:在果汁样品中的应用。

Carbendazim imprinted electrochemical sensor based on CdMoO/g-CN nanocomposite: Application to fruit juice samples.

机构信息

Hasan Kalyoncu University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Gaziantep, Turkey.

出版信息

Chemosphere. 2022 Aug;301:134766. doi: 10.1016/j.chemosphere.2022.134766. Epub 2022 Apr 28.

Abstract

Carbendazim (CAR) as a fungicide is utilized for fruits and vegetables to provide diseases' control and the degradation of carbendazim having benzimidazole ring is slow. Herein, a molecularly imprinted electrochemical sensor based on CdMoO/g-CN nanocomposite was prepared for CAR determination in fruit juice samples. Firstly, CdMoO/g-CN nanocomposite with high yield was fabricated via one-pot in-situ hydrothermal approach including environmentally friendly method. Formation of CAR imprinted polymers was performed on CdMoO/g-CN nanocomposite modified glassy carbon electrode (GCE) in presence of CAR as template and pyrrole (Py) as a monomer by cyclic voltammetry (CV) technique. Following the morphological, structural, and optical characterization of as-synthesized nanocomposite, the electrochemical techniques were also implemented to evaluate the electrochemical features of fabricated electrodes. The limit of quantification (LOQ) and limit of detection (LOD) values were calculated as 0.1 × 10 M, and 2.5 × 10 M, respectively in addition to satisfactory selectivity, stability, reproducibility and reusability. The findings revealed that the proposed CAR imprinted electrochemical sensor can be successfully employed in food safety.

摘要

多菌灵(CAR)作为一种杀菌剂,用于水果和蔬菜,以提供疾病控制,并缓慢降解具有苯并咪唑环的多菌灵。在此,通过一锅原位水热法制备了基于 CdMoO/g-CN 纳米复合材料的分子印迹电化学传感器,用于果汁样品中 CAR 的测定。首先,通过环境友好的方法,以 CdMoO/g-CN 纳米复合材料高产率合成。在 CAR 作为模板和吡咯(Py)作为单体的存在下,通过循环伏安法(CV)技术在 CdMoO/g-CN 纳米复合材料修饰的玻碳电极(GCE)上进行 CAR 印迹聚合物的形成。在合成的纳米复合材料的形态、结构和光学特性之后,还实施了电化学技术来评估所制备电极的电化学特性。LOQ 和 LOD 值分别计算为 0.1×10^-6M 和 2.5×10^-6M,此外还具有令人满意的选择性、稳定性、重现性和可重复性。研究结果表明,所提出的 CAR 印迹电化学传感器可成功用于食品安全。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验