Numerical Optimization of a Silica-Defect 1D Superconductor Photonic Crystal Biosensor for Refractive-Index-Based Cancer Cell Discrimination

Cancer is a leading cause of death worldwide, characterized by uncontrolled cell growth affecting various tissues and organs. Early detection is crucial to reduce mortality and treatment costs. This study proposes a highly sensitive perovskite one-dimensional (1D) defective photonic crystal (PC) biosensor designed to detect cancer cells by exploiting shifts in resonant modes caused by differences in refractive indices (RI) between healthy and malignant cells infiltrating the bio-cavity layers. The sensor structure consists of alternating layers of SrTiO 3 and bio-cavities filled with cancerous or healthy cells with a central defect layer. Using the transfer matrix method (TMM), the transmittance spectra and resonant peak shifts are analysed under varying parameters such as defect layer thickness, incidence angle, and number of periods to optimize sensitivity and quality factor. Introducing SiO 2 as the defect layer enhances sensitivity due to improved light-matter interaction. The optimized sensor achieves a sensitivity of up to 930 nm/RIU, a high quality factor of [Formula: see text], and a low detection limit [Formula: see text], demonstrating its potential as a cost-effective, precise, and rapid tool for biomedical refractive-index based cancer discrimination applications.