Study and Application of Molecularly Imprinted Electrochemical Sensor Based on AuNPs/N-GR@CS for Highly Selective Recognition of Trace Hyperoside

Abstract

Establishing a high-selectivity and rapid detection technology for trace index components in complex samples is of great significance for real-time and on-site drug quality evaluation. In this study, a molecularly imprinted electrochemical sensor with highly selective recognition and detection of trace hyperoside was prepared using chitosan functionalized Nitrogen-doped graphene composite coated with gold nanoparticles (AuNPs/N-GR@CS) as electrode substrate modification material, and the deposition of AuNPs further improved the conductivity of the modified electrode. With the aid of molecular imprinting technology, polymer films with high selectivity and identification of hyperoside were successfully prepared on glassy carbon electrodes (GCE) by self-assembly using hyperoside as template molecule and acrylamide as functional monomer. Because the acrylamide can accept protons through the olefinic double bond and firmly polymerize with each other, while it binds with hyperoside through hydrogen bonds. Therefore, the hyperoside can be easily dissociated in the eluate, which offers a condition for formating a molecularly imprinted polymer film to highly select hyperoside. The highly conductive N-GR@CS modified at the bottom of the polymer film provides the possibility to electrocatalyze hyperoside, and facilitate electron transfer to amplify the response signal. Under the optimized experimental conditions, the sensor showed a detection limit was 6.42 × 10−8 mol l−1 (S/N = 3) with a good linear relationship in the range of 2.15 × 10−7 to 2.15 × 10−5 mol l−1. Moreover, it displayed good reproducibility and stability, and could realize the direct and highly selective detection of trace hyperoside in complex samples. In consequence, this study is expected to provide a convenient and reliable method for on-site real-time evaluation of traditional Chinese medicine (TCM) quality with reference to the index components.