Development of nanostructure-based electrochemical immunosensor for determination of pancreatic cancer antigen 19.9

Nanostructure-based biosensors, when combined with cost-effective fabrication technologies, could offer simple and highly sensitive devices for detecting important cancer biomarkers in clinical settings. Therefore, in this thesis, it was aimed to develop a label-free electrochemical immunosensor to sensitively detect the cancer marker carbohydrate antigen 19-9 (CA19-9). For this purpose, multi-walled carbon nanotubes (MWCNTs), reduced graphene oxide (rGO), and molybdenum disulfide (MoS2)- supported palladium, gold, and silver nanoparticles were prepared. These nanomaterials were prepared using chemical reduction and hydrothermal method. Surface characterization of the nanostructures was performed using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), high-resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS). A new platform for detecting CA19-9 was prepared using surface-imprinted carbon electrode (SPCE). The SPCE surface was modified by immobilizing antibodies and antigens onto the characterized nanomaterial layer. The preparation stages of the immunosensor were characterized by electrochemical methods such as cyclic voltammetry (CV) and square wave voltammetry (SWV) and the sensor properties were investigated. Modification of SPCE surface with MoS2 and AuPd/MoS2-rGo significantly improved the analytical performance of the biosensor, which showed superior results to those prepared with rGO alone. The biosensor exhibited a wide working range of 0.015–108 U/mL and a relatively low detection limit of 0.0042 U/mL. Repetability was successfully examined with 10 replicate measurements, and reproducibility was successfully examined with 6 different electrodes prepared at different times. It is envisaged that the developed sensor can be a part of the disease diagnosis and treatment process thanks to the cost-effective, rapid and label-free detection of the targeted biomarker.