Two-Dimensional Nickel Porphyrinic Metal-Organic Framework-Modified Electrode for Electrochemical Sensing
Andrews Boakye1, Kun Yu1, Huining Chai2
1Research Center for Intelligent and Wearable Technology, College of Textiles and Clothing, State Key Laboratory of Bio-Fibers and Eco-Textiles, Qingdao University, Qingdao 266071, China.
This study introduces a novel 2D metal-organic framework (MOF) nanosheet for electrochemical sensing. The developed sensor demonstrates high sensitivity and selectivity for detecting p-nitrophenol (p-NP) in real samples.
Area of Science:
- Materials Science
- Electrochemistry
- Nanotechnology
Background:
- Two-dimensional (2D) metal-organic framework (MOF) nanosheets offer potential for electrochemical sensing due to their active sites and high aspect ratio.
- Developing robust platforms with enhanced detection performance is crucial for advanced sensing applications.
Purpose of the Study:
- To synthesize and characterize a novel nickel-based porphyrinic MOF nanosheet (2D Cu-TCPP(Ni)) for electrochemical sensing.
- To develop a highly selective, sensitive, and stable sensor for detecting p-nitrophenol (p-NP).
Main Methods:
- A solvothermal process using a nickel porphyrin ligand, Cu(NO3)2·3H2O, and polyvinylpyrrolidone (PVP) surfactant to create 2D Cu-TCPP(Ni) MOF nanosheets.
- Modification of a laser-induced graphene electrode with the synthesized 2D Cu-TCPP(Ni) MOF nanosheet.
- Electrochemical detection of p-nitrophenol (p-NP) using differential pulse voltammetry (DPV) and cyclic voltammetry (CV).
Main Results:
- The 2D Cu-TCPP(Ni) MOF nanosheet exhibited excellent selectivity, sensitivity, and stability.
- The sensor demonstrated a detection range of 0.5–200 μM (DPV) and 0.9–300 μM (CV) for p-NP.
- Limits of detection (LOD) were as low as 0.1 μM (DPV) and 0.3 μM (CV).
Conclusions:
- The enhanced electrochemical performance is attributed to the MOF nanosheet's large active surface area, catalytic activity, and adsorption capacity.
- The developed sensor successfully detected p-NP in real sample analysis, indicating its potential for environmental point-of-care testing.
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