Artificial Intelligence and Machine Learning Driven Multiplex Programmable Modelling for Refractive Index Based Surface Plasmon Resonance Sensor

Researchers can employ various optical biosensor technologies in many biomedical diagnostic and analysis procedures because they can assess the conformational changes of biomolecules and their molecular interactions. Surface plasmon resonance biosensors are one of the most popular methods among many optical biosensors because they are utilized for label-free and real-time monitoring with outstanding precision and accuracy. The current study proposes AI and machine learning (ML) programming-based rapid and highly sensitive SPR refractive sensor. The proposed SPR biosensor device consists of a glass prism N-FK51A, silver metal, graphene, nickel, and potassium niobate layers. Attenuated total reflection (ATR) is the basis for the device operation, and the Kretschmann configuration serves as the foundation for the device structure. The performance parameters, such as angular sensitivity, quality factor, detection accuracy, limit of detection and electric filed have been numerically analysed for blood sample. It is possible to identify and examine biomolecules with the proposed surface plasmon resonance biosensor.