Modelling and Numerical Analysis of a Highly-Efficient PCF-based Amino Acid Sensor

Chemical sensing is a critical problem in the food, water, and environment, as well as in the medicinal and chemical fields, among others. Currently available conventional technologies for detecting chemicals in labs are time-consuming, unable to provide a totally correct result, and occasionally waste a significant amount of sample material. The use of Photonic Crystal Fiber (PCF) sensors may alleviate these difficulties. In addition to biosensors and chemical sensors, PCF may be used as liquid sensors, temperature sensors, mechanical sensors, gas sensors, and other types of sensors. During the last decade, numerous researchers developed PCF to detect a variety of liquid chemicals in order to reduce sensing time, provide more accurate results, and eliminate sample waste. The circular-shaped hollow-core photonic crystal fiber-sensor described in this thesis can be utilized for efficient amino acid research utilizing THz waves, and it is described in detail in this manuscript. The identification of amino acids is important in medical research, notably in the field of medication development. In this study, the finite element method (FEM) was used in conjunction with the COMSOL MULTIPHYSICS software package to analyze the performance of five essential amino acids over a wide range of THz frequencies and core dimensions. Important waveguide properties were evaluated in detail for each amino acid over the entire range of THz frequencies and core dimensions. Numerical study of the modelled sensor has attained relative sensitivity which is above 94.76% at 2.7 THz for all the five amino acids. Besides the effective material loss for the sensor is in the order of 10^-3 cm^-1. The sensor also exhibits a very low confinement loss maintaining a higher numerical aperture. Furthermore, fabrication of this sensor is possible via the use of an extrusion process in conjunction with 3D printing, selective filling technique, or sol-gel technique.

• Supervision