Using electrochemical oxidation of Rutin in modeling a novel and sensitive immunosensor based on Pt nanoparticle and graphene-ionic liquid-chitosan nanocomposite to detect human chorionic gonadotropin

This study aims at developing an innovative electrochemical immunoassay system for an ultrasensitive detection of human chorionic gonadotropin (hCG). hCG is a secretion of the placenta during pregnancy and gestational trophoblastic diseases. It increases as a consequence of abnormal placental invasion and placental immaturity. Thus, an exact diagnosis of the concentration of hCG in urine or serum plays an important role in monitoring trophoblastic diseases in all modern immunological pregnancy tests. In this work, a novel immunosensor is constructed by covalent immobilization of Pt nanoparticles (PtNPs) onto robust nanocomposite containing graphene (Gr), chitosan (Chit) and 1-methyl-3-octyl imidazolium tetrafluoroborate as ionic liquid (IL) (Gr-IL-Chit). The nanocomposite not only led to increase the electrode surface area and accelerate the electron transfer kinetics, but also it could provide a highly stable matrix to enhance the loading amount of the nanoparticles. PtNPs acts as a linker to immobilize hCG antibody onto the modified electrode. The amine groups of the antibody are covalently attached to PtNPs. Cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) were employed to trace the assembly process of the immunosensor. In this study, we used Rutin (RU) for the first time as the redox probe in the development of the electrochemical immunosensor. Upon biorecognition hCG to its antibody, peak current of RU decreased due to the formation of hCG-anti hCG complex. Through differential pulse voltammetry (DVP) experiments, it was found that proposed immunosensor could detect hCG at two broad linear ranges: 0.00106-2.12 and 2.12-350 mIU/mL and a low detection limit of 0.00035 mIU/mL. The immunosensor was found stable, reproducible, and highly specific for hCG detection. (C) 2015 Elsevier B.V. All rights reserved.