Two-Dimensional Nickel Porphyrinic Metal–Organic Framework-Modified Electrode for Electrochemical Sensing

纳米片 电化学 电极 循环伏安法 电化学气体传感器 微分脉冲伏安法 检出限 金属有机骨架 化学工程 材料科学 无机化学 化学 纳米技术 冶金 吸附 有机化学 物理化学 色谱法 工程类
作者
Andrews Boakye,Kun Yu,Huining Chai,Tailin Xu,Lystra Sarah Houston,Benjamin Kwablah Asinyo,Xueji Zhang,Guangyao Zhang,Lijun Qu
出处
期刊:Langmuir [American Chemical Society]
卷期号:40 (5): 2708-2718 被引量:4
标识
DOI:10.1021/acs.langmuir.3c03257
摘要

Due to their highly exposed active sites and high aspect ratio caused by their substantial lateral dimension and thin thickness, two-dimensional (2D) metal–organic framework (MOF) nanosheets are currently considered a potential hybrid material for electrochemical sensing. Herein, we present a nickel-based porphyrinic MOF nanosheet as a versatile and robust platform with an enhanced electrochemical detection performance. It is important to note that the nickel porphyrin ligand reacted with Cu(NO3)2·3H2O in a solvothermal process, with polyvinylpyrrolidone (PVP) acting as the surfactant to control the anisotropic development of creating a 2D Cu–TCPP(Ni) MOF nanosheet structure. To realize the exceptional selectivity, sensitivity, and stability of the synthesized 2D Cu–TCPP(Ni) MOF nanosheet, a laser-induced graphene electrode was modified with the MOF nanosheet and employed as a sensor for the detection of p-nitrophenol (p-NP). With a detection range of 0.5–200 μM for differential pulse voltammetry (DPV) and 0.9–300 μM for cyclic voltammetry (CV), the proposed sensor demonstrated enhanced electrochemical performance, with the limit of detection (LOD) for DPV and CV as 0.1 and 0.3 μM, respectively. The outstanding outcome of the sensor is attributed to the 2D Cu–TCPP(Ni) MOF nanosheet's substantial active surface area, innate catalytic activity, and superior adsorption capacity. Furthermore, it is anticipated that the proposed electrode sensor will make it possible to create high-performance electrochemical sensors for environmental point-of-care testing since it successfully detected p-NP in real sample analysis.
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