Development of Graphene with Tungsten Disulfide Composite Layer Based SPR Biosensor for Biomedical Application

Surface Plasmon Resonance (SPR) biosensors have been widely used for biomedical applications due to their high sensitivity and label-free detection capabilities. However, their performance can be further enhanced by using advanced materials and signal-processing techniques. The objective of this study is to develop a composite layer-based SPR biosensor using Au, WS2, and Graphene layers and signal processing with MATLAB for enhanced sensitivity and the detection of DNA-DNA Hybridization. The composite layer-based SPR biosensor was fabricated by depositing a thin layer of Au on a glass substrate, followed by the deposition of WS2 and Graphene layers using a Chemical Vapor Deposition (CVD) technique. A self-assembled monolayer of 3-Mercaptopropionic Acid (MPA) was then attached to promote DNA immobilization. The performance of the biosensor was evaluated by detecting the hybridization of a single-stranded DNA (ssDNA) probe with a complementary ssDNA target. The sensor response was analyzed using MATLAB to enhance the sensitivity of the biosensor. The developed composite layer-based SPR biosensor exhibited a high sensitivity of 592 deg./RIU for the detection of DNA-DNA hybridization. 32.74% sensitivity has been increased. The signal processing with MATLAB significantly improved the signal-to-noise ratio and allowed for real-time monitoring of the biomolecular interactions. The composite layer-based SPR biosensor developed in this study demonstrated enhanced sensitivity for the detection of DNA-DNA hybridization in biomedical applications. The use of advanced materials such as Au, WS2, and Graphene layers, coupled with signal processing with MATLAB, can significantly improve the performance of SPR biosensors. This biosensor has great potential for use in various areas, including genetic testing, drug discovery, and disease diagnosis. Detected DNA-DNA hybridization is used in the biomedical field to identify and classify microorganisms by comparing the degree of genetic similarity between their DNA sequences.