Design of two cost-effective and efficient glucose biosensors using transfer matrix method and particle swarm optimization in Python

Abstract This paper outlines the design, optimization, and evaluation of two compact and efficient biosensors for detecting glucose concentrations. The first sensor consists of a stack made up of AlAs/ZnS/GaP, while the second, more compact version is formed from AlAs, SiO 2 , and sapphire. The proposed designs are examined using a designed Python script that utilize the transfer matrix method (TMM) to determine the reflectance power coefficients of the sensors at wavelengths of 500 nm and 633 nm, respectively. The performance of the suggested biosensors is assessed by determining the sensitivity (S), full width at half maximum (FWHM), quality factor (Q), and figure of merit (FoM). To maximize sensitivity, the Python script is enhanced by adding particle swarm optimization (PSO) to determine the best materials and their respective thicknesses. The numerical findings reveal that the proposed sensors (AlAs/ZnS/GaP) and (AlAs/SiO 2 /Al 2 O 3 ) demonstrate strong linear behavior, high sensitivities of (393°/RIU and 388.5°/RIU), and impressive FoM values of (65.5 and 85) across the index range of 1.33 to 1.37, respectively. The proposed sensors are a cost-effective, scalable, and efficient solution for a variety of applications.