用于超灵敏葡萄糖传感器的高效NiO基复合材料的研制

G. Naikoo, Mehrai Ud Din Sheikh
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引用次数: 2

摘要

糖尿病的死亡率在世界范围内以惊人的速度增长,引起了人们的极大关注。因此,为了监测血糖水平,防止糖尿病对生命的影响,研究人员一直致力于开发高灵敏度、可靠、快速和经济的电化学血糖检测传感器。在本研究中,我们计划采用绿色化学方法,通过改性溶胶-凝胶法合成NiO基复合材料用于葡萄糖检测。采用粉末x射线衍射(PXRD)、电子显微镜(SEM)和BET表面积分析仪对合成材料的晶体结构和表面形貌进行了表征。采用循环伏安法(CV)和差分脉冲伏安法(DPV)研究了制备的电极材料在碱性介质中对葡萄糖电氧化的催化性能。NiO@Si-NPs开发的非酶传感器具有优异的葡萄糖传感性能,检测限极低(0.08µM),灵敏度高达445µAm−1cm−2,这可能归功于在NiO表面添加了二氧化硅纳米颗粒,为葡萄糖氧化提供了大的表面积。因此,NiO@SiNps复合电极材料将成为一种非常有前途的候选材料,用于开发高效的电化学传感器,用于可靠的葡萄糖检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Highly Efficient NiO based Composite Materials for Ultra-Sensitive Glucose Sensors Non Enzymatic Glucose Sensors
Diabetes is attracting a great attention because of alarming increase in death rate worldwide. Thus, in order to monitor blood glucose level and to prevent life-threatening impact of diabetes, researchers are consistently working on the development of highly sensitive, reliable, rapid and cost-effective electrochemical sensors for glucose detection. In this study, we plan to adopt green chemistry approach to synthesize NiO based composite materials via modified sol gel approach for glucose detection. The crystal structure and surface morphology of the as-synthesized materials were characterized by Powder-X ray diffraction (PXRD), electron microscopy (SEM) and BET surface area analyzer. Cyclic Voltammetry (CV) and Differential Pulse Voltammetry (DPV) methods were employed to investigate the catalytic properties of the fabricated electrode materials for glucose electro-oxidation in alkaline media. The NiO@Si-NPs developed non-enzymatic sensor exhibited excellent performance for glucose sensing with extremely low detection limit (0.08 µM) and an ultrahigh sensitivity of 445 µAm−1cm−2 which could be attributed to the addition of silica nanoparticles on the surface of NiO that offered large surface area for oxidation of glucose. Hence, NiO@SiNps composite electrode materials will serve as a highly promising candidate for the development of efficient electrochemical sensors for reliable glucose detection.
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