An electrochemical molecularly imprinted sensor based on CuBi2O4/rGO@MoS2 nanocomposite and its utilization for highly selective and sensitive for linagliptin assay

The molecularly imprinted polymers (MIP) is an outstanding electrochemical tool that demonstrates good chemical sensitivity and stability. These main advantages, coupled with the material's vast microfabrication flexibility, make molecularly imprinted sensors an attractive sensing device. Herein, it was aimed to develop a state-of-art molecularly imprinted sensor based on CuBi2O4/rGO@MoS2 nanocomposite to be utilized for the detection of linagliptin (LNG), a novel hypoglycemic drug. The electrochemical characterizations of linagliptin on the surface of the modified electrode was examined via cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). Several characterization methods including transmission electron microscope (TEM), X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), and Energy-dispersive X-ray spectroscopy(EDX), were utilized for electrode characterization. The LNG imprinted voltammetric sensor was developed in 80.0 mM phenol containing 20.0 mM LNG. CuBi2O4/rGO@MoS2 nanocomposite on LNG imprinted screen-printed carbon electrode (SPCE) (MIP/CuBi2O4/rGO@MoS2 nanocomposite/SCPE) exhibited a linear relationship between peak current and LNG concentration in the range 0.07-0.5 nM with a detection limit of 0.057 nM. In the existence of interfering substances, an LNG imprinted electrode was utilized to analyze urine, human plasma, and tablet samples with adequate selectivity. The developed sensor was also illustrated for stability, repeatability, reproducibility, and reusability.

Automated summary

This study developed a molecularly imprinted sensor based on CuBi2O4/rGO@MoS2 nanocomposite for the detection of linagliptin. The sensor exhibited good selectivity and sensitivity, with excellent electrochemical properties under multiple detection methods.