RELATIVE PERMITTIVITY MODELS FOR METAL NANOSPHERE STRUCTURE USING FINITE ELEMENT METHOD

• Main Author: Agustiana, Elis
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/63067
• Institution: Institut Teknologi Bandung

Studies related to surface plasmon resonance (SPR) sensors have a fairly large dependence on the frequency response of the plasmon material, which is also explained by the dielectric function or relative permittivity function which is quite complex. Based on its history, many scientists have developed and improved the dielectric function model in order to accurately describe the dielectric function. However, there is very little discussion on the effect of the relative permittivity function model on the SPR on the sensor characteristics, so this research was carried out. The objectives of this study are to simulate metallic nanospheres for skin depth and heat losses, to determine the effect of variations in size and type of metallic nanosphere material on heat losses data, and to find differences in the characterization of data from modeling results by Brendel-Bormann, Drude-Lorentz, and Werner. This research was carried out in a simulation using the finite element method, with the geometric shape of the nanoparticles being simulated in the form of a sphere, then the data obtained was based on data from 3 relative permittivity functions modeled by several scientists, namely using the permittivity function modeling Brendel-Bormann, Drude-Lorentz, and Werner. Then the data that will be displayed includes data on relative permittivity, refractive index, skin depth, and heat losses. The data obtained were generated from the simulation by varying the wavelength from 400-700 nm and also varying the nanoparticle radius starting from 10 nm, 30 nm, 50 nm and 100 nm. Then experiments were carried out for 3 types of materials including copper, gold and silver. Then in this simulation also varied the modeling of the relative permittivity function of the 3 modeling functions. So based on the data obtained from the simulation results, the data that is closest to the actual data is the data from the modeling of the relative permittivity function of Brendel-Bormann.