Suppr超能文献

一步法制备具有高活性 Ni(OH)的石墨烯和多孔 Ni 杂化材料用于葡萄糖检测。

One-step formation of a hybrid material of graphene and porous Ni with highly active Ni(OH) used for glucose detection.

机构信息

Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, People's Republic of China.

出版信息

Nanotechnology. 2020 May 1;31(18):185501. doi: 10.1088/1361-6528/ab6ab7. Epub 2020 Jan 13.

Abstract

A hybrid material of graphene and porous Ni with highly active Ni(OH) was formed through a one-step electrochemical exfoliation assisted method. The porous Ni with a pore size of 2-10 micrometers obtained by a hydrogen bubble template method was used as the cathode while the graphite foil was used as the anode with only (NH)SO as the electrolyte. Both the high surface areas of porous Ni and the oxygen radicals in graphene favored the formation of the Ni(OH). It is confirmed by energy dispersion spectrum, transmission electron microscope, Raman spectroscopy, x-ray diffraction and x-ray photoelectron spectroscopy analysis. Both the active area and the glucose sensing property of the as-prepared hybrid material were estimated by electrochemical methods of cyclic voltammetry with current-voltage (C-V) curve, chronoamperometry with current-time (I-t) curve and electrochemical impedance spectroscopy analysis, respectively. It shows an extraordinary active area as well as a low charge transfer resistance and absorption resistance. As a result, a high sensitivity of 6504 μA/mM cm within a linear range of 4 μM-1.0 mM was obtained for glucose detection.

摘要

通过一步电化学剥离辅助法形成了石墨烯和具有高活性 Ni(OH)的多孔 Ni 的杂化材料。通过氢气气泡模板法获得的具有 2-10 微米孔径的多孔 Ni 用作阴极,而石墨箔用作阳极,仅使用(NH)SO 作为电解质。多孔 Ni 的高表面积和石墨烯中的氧自由基都有利于 Ni(OH)的形成。通过能量色散谱、透射电子显微镜、拉曼光谱、X 射线衍射和 X 射线光电子能谱分析得到证实。通过循环伏安法、电流-电压(C-V)曲线、计时安培法和电流-时间(I-t)曲线以及电化学阻抗谱分析,分别对所制备的杂化材料的活性面积和葡萄糖传感性能进行了评估。结果表明,该杂化材料具有极高的活性面积以及较低的电荷转移电阻和吸收电阻。因此,在 4 μM-1.0 mM 的线性范围内,葡萄糖检测的灵敏度达到了 6504 μA/mM cm。

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