Planar Sensor for Material Characterization Based on the Sierpinski Fractal Curve

Autor: Ignacio Llamas-Garro, P. H. B. Cavalcanti Filho, L. M. da Silva, Manuelle R. T. de Oliveira, J. A. I. Araujo, M. S. Coutinho, M. T. de Melo
Rok vydání: 2020
Předmět:
Permittivity
Materials science
Article Subject
Microwave sensors
Acoustics
Material characterizations
02 engineering and technology
High permittivity
computer.software_genre
Microwave devices
01 natural sciences
Measure (mathematics)
Resonator
Planar
Sierpinski fractals
0202 electrical engineering
electronic engineering
information engineering
Range (statistics)
T1-995
Computer software
Sensitivity (control systems)
Electrical and Electronic Engineering
Instrumentation
Technology (General)
Planar sensors
010401 analytical chemistry
020206 networking & telecommunications
Microwave resonators
0104 chemical sciences
Sierpinski triangle
Simulation software
NO KEYWORDS
Fractals
Control and Systems Engineering
Simulator software
Poles
Permittivity values
computer
Frequency ranges
Measured results
Zdroj: Journal of Sensors
r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Universitat Oberta de Catalunya (UOC)
Journal of Sensors, Vol 2020 (2020)
ISSN: 1687-7268
1687-725X
DOI: 10.1155/2020/8830596
Popis: This paper presents a planar and compact microwave resonator sensor to characterize materials. The geometry of the resonator is based on the Sierpinski fractal curve and has four poles in the frequency range from 0.5 GHz to 5.5 GHz. Any of the four poles can be used to measure samples with low permittivity values, where the first pole is suitable for samples with high permittivity values. The sensitivity of the poles and return losses of the sensor are presented and obtained using a full-wave 3D simulator software. The device is manufactured and validated through a comparison between simulated and measured results. © 2020 P. H. B. Cavalcanti Filho et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Databáze: OpenAIRE
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