In this paper, we have presented a design and simulation of a graphene-coated surface plasmon resonance (SPR) based biosensor for targeting specific biological components. We have explicitly shown the detection of the hemoglobin level in blood samples and the glucose concentration level in urine samples by using the finite element method (FEM) based numerical simulation. In the blood component, the 0.001 refractive index increment causes a 6.1025 g/l hemoglobin (HB) level increment, which has been detected using this SPR based sensor with 200 deg/RIU angular sensitivity. Moreover, we have also detected the presence or absence of diabetes using the glucose concentration level in urine samples with this SPR sensor. Therefore, the novelty of this paper is detecting the blood hemoglobin level and glucose concentration levels in urine samples more accurately than the previously proposed whispering gallery mode (WGM) and photonic crystal nanocavity based optical sensors.
Inverse method has wide application on medical diagnosis where non-destructive evaluation is the key factor .Back scattered waves or echoes generated from the forward moving waves has information about its geometry, size and location. In this paper we have investigated how well different geometries of the object is determined from the back scattered waves by a high accuracy Non-Standard Finite Difference Time Inverse (NSFD-TI) Maxwell's algorithm and how the refractive index of the object plays a deterministic role on its size.
Whispering gallery modes (WGM) have revolutionized the field of optical sensors. This paper presents a design and simulation of a novel structure called a “multi-core whispering gallery mode (WGM)” based on multiple evanescent waves coupling for detecting cancer cells and diabetes tear cells. This work is totally simulation based, and the simulation is done by a finite element method based simulation tool. From the simulation, it is expected that the proposed sensor exhibits a sensitivity of 650 nm/RIU, 666.67 nm/RIU, and 642.285 nm/RIU, respectively, for the detection of cancerous Basal, HeLa, and MDB-MB-231 cells. In addition, it is also capable of detecting affected diabetes tear cells from healthy tear cells with a sensitivity of 650 nm/RIU. To the best of our knowledge, the resultant sensitivity of the proposed sensor is probably the highest compared to other WGM based bio-sensors till now.
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