Facile preparation of cubic metal organic framework sensor: Kinetics, mechanism analysis and sensitive detection of dopamine

[Display omitted] •A Cu-MOF electrochemical sensor was fabricated to detect dopamine.•The sensor was characterized by SEM, TEM, XRD, FTIR and XPS methods.•The electrochemical analysis of Cu-MOF modified GCE electrode was investigated by DPV technique.•The developed sensor was exhibited the linear ranges of 10–100 µM and LOD 0.27 µM for dopamine. Dopamine (DA) is one of the typical neurotransmitters, which plays an important role in early prevention of neurological diseases and clinical research. Rapid and sensitive detection of dopamine is crucial. In this work, a series Cu-metal organic framework material (Cu-MOF) with different raw material ratios were prepared by a one-step simple precipitation method. The as-prepared materials were characterized by scanning electron microscopy (SEM), High Resolution Transmission Electron Microscope (HRTEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and Fourier Transform Infrared Spectroscopy (FT-IR) techniques. The differential pulse voltammetry (DPV), cyclic voltammogram (CV) and electrochemical impedance spectroscopy (EIS) were applied for electrochemical characterization. The Cu-MOF-3 modification on the surface of glassy carbon electrodes (GCE) showed the strongest electrochemical signal and biggest peak current, which was superior to that of bare electrode and other prepared MOFs electrodes. The electrochemical reaction kinetics were investigated in detail, and the possible mechanism was proposed. Under optimized conditions, the enhanced peak currents represented the excellent analytical performance of detection of DA in the range of 10–100 μM, as well as good interference resistance and reproducibility. To further validate its possible application, the present electrochemical sensor was successfully applied to determine trace amount of DA with satisfactory recovery results (>99.5 %) in water sample. This work provides a simple and environment-friendly analysis method in DA detection, suggesting it as a promising low-cost, reliable platform for the DA detection application.