Numerical Simulation of a High Sensitivity Spr Sensor for Rapid Malaria Diagnosis using BP and Graphene 2D Materials

Using a novel architecture comprising BK7 glass, silver (Ag), black phosphorus (BP), copper-platinum (Cu-Pt), and graphene materials, we offer a numerical simulation of a highly sensitive surface plasmon resonance (SPR) sensor for the detection of malaria. We utilized the transfer matrix method (TMM) to compute the reflectance and maximize the sensor’s performance. These layers work together to maximize the sensor’s performance by strengthening the interaction between light and matter and raising the detection sensitivity. Using a reference and normal sample and distinct refractive indices (RI) corresponding to the schizont, trophozoite, and ring phases of malaria, we investigated reflectance using the TMM. Different SPR angles (θ_SPR), minimum reflectance (R_min), full width at half maximum (FWHM), figure of merit (FoM), detection accuracy (DA), and sensitivity values are found under these settings, according to our data. In particular, the sensor showed a maximum sensitivity of 351.66/RIU and a FoM of 67.5 RIU^-1 for the normal stage of malaria. The reference sample’s SPR angle ranged from 71.254° to 85.975° for the normal stage, demonstrating the sensor’s capacity to distinguish between samples with and without malaria. The outcomes demonstrate the potential of this multi-layered SPR sensor for point-of-care settings by providing a promising platform for early and accurate diagnosis of malaria.