Optimization of kretschmann-surface plasmon resonance biosensor based on multi-walled carbon nanotube using taguchi method

A Gani, Nurfarahin (2023) Optimization of kretschmann-surface plasmon resonance biosensor based on multi-walled carbon nanotube using taguchi method. Project Report. Melaka, Malaysia, Universiti Teknikal Malaysia Melaka. (Submitted)

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Abstract

According to the data and challenges, it appears that the ageing population is dealing with diabetes and renal failure issues that affect rising blood sugar, urea, and creatinine levels, which can lead to strokes and heart attacks and frequently result in death. By using the conventional method, glucose monitoring has relied on experiment test such as finger pricks, with indirect approaches using enzymes like glucose oxide and glucose dehydrogenase. In order to overcome these issues, a new approach using Kretschmann - based Surface Plasmon Resonance (K-SPR) sensor is suggested. It is a great platform in biomolecule interactions due to the various options of materials in biosensors. It measured the change in the refractive index of the material being used. In order to optimize this K-SPR biosensor, a few factors must be investigated such as layer materials, thin layer film thicknesses and refractive index of the layer detection. The difference effects of the layer thicknesses on K-SPR are resulting by using WINSPALL software simulator. The research is focused on the impact of varying the thickness of the material layers which are Chromium (Cr), Gold (Au), Multi-Walled Carbon Nanotube (MWCNT) to the sensitivity at three different wavelengths. The four control factors layer thicknesses of this K-SPR sensor were analyzed using MINITAB software and Taguchi's L9 Orthogonal Array (AO) approach. The Kretschmann-based surface plasmon resonance glucose sensor demonstrated improved sensitivity at an optical wavelength of 785 nm in comparison to the near wavelengths of 632.8 nm and 670 nm by 209.80˚ RIU ̄ 1. Therefore, the most effective configuration for the K-SPR sensor has been found to be A3B3C3D1, as a result of optimization using Taguchi Method to achieve the highest sensitivity which is the wavelength at 785 nm, 4 nm of Chromium layer thickness, 60 nm of gold layer thickness and 10 nm of MWCNT layer thickness.

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