Multiwall Carbon Nanotube Impedance Matching Section

Abstract

In this work, computer-aided impedance analysis and genetic-based synthesis of a multiwall carbon nanotube impedance matching section (MWCNTIMS) are proposed. Transmission line model (TLM) of a multiwall carbon nanotube is used for the computer-aided impedance analysis. Continuous parameter genetic algorithm (CPGA) is used for the genetic-based synthesis. A simple, fast, and effective impedance analysis and synthesis approach for an MWCNTIMS is presented. The results of the analysis and synthesis for different examples of MWCNTIMS are given and discussed in detail. The results show that the effect of variation of the distance from the ground plane of the outer shell is very small on the values of input resistance and input reactance. The values of input resistance and input reactance decrease while the value of inner radius or the total number of shells increases. Since the diameter increases with the increasing value of inner radius and the total number of shells, the values of input resistance and input reactance decrease with increasing diameter. While the value of nanotube length increases, the values of input resistance and input reactance increase.

An impedance matching section based on composite right/left‐handed transmission line model in viewpoint of phase shift and noise figure

“…22 Since GAs are easy and common purpose algorithms that do not require derivatives, they have extensive applications in electromagnetics and microwave theory. [23][24][25][26][27][28][29][30] An encoding (with binary or floating point numbers) of the parameter of the error (cost or objective) function to be optimized via GA is defined as a gene. In a binary GA (BGA), parameters constituting a chromosome that is a set of genes are coded by binary numbers.…”

“…GAs mimic evolution and genetic recombination in nature 22 . Since GAs are easy and common purpose algorithms that do not require derivatives, they have extensive applications in electromagnetics and microwave theory 23–30 . An encoding (with binary or floating point numbers) of the parameter of the error (cost or objective) function to be optimized via GA is defined as a gene.…”