基于哑铃缺陷-地基结构(DB-DGS)谐振器的多用途反射模相位变化微波传感器,用于固体和液体表征

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Nazmia Kurniawati;Paris Vélez;Pau Casacuberta;Lijuan Su;Xavier Canalias;Gerard Sisó;Ferran Martín
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引用次数: 0

摘要

本文介绍了一种用于固体和液体样品表征的单频平面微波传感器。该传感器以反射模式单端口配置工作,利用微带线和一系列感应带,端接横向定向哑铃缺陷-地面结构(DB-DGS)谐振器(蚀刻在地平面上),即传感元件。输出变量是在特定(工作)频率下反射(RX)系数的相位。该器件由并联谐振器(占DB-DGS谐振器)串联组成的集总电路建模,该电路连接到电感器和寄生电容器。这种配置使RX系数在工作频率下产生陡峭的相位斜率,这对于实现高灵敏度至关重要。该传感器可用于检测传感元件周围环境介电常数在参考值附近的微小变化,通过方便地调整工作频率,可以随意选择参考值。为了证明所提出的装置的通用性,考虑了三种不同的场景:1)固体样品的介电常数测量;2)去离子(DI)水中乙醇浓度测定;3)酒中水/洗涤剂的检测。固体介质表征的最大灵敏度为-382.44°/单位介电常数。对于液体传感,该传感器对水中乙醇浓度的灵敏度为9°/%,对葡萄酒中水/洗涤剂混合物的灵敏度为0.88°/%。尽管水/洗涤剂和葡萄酒的电磁特性相似,但所提出的传感器可以分辨体积分数(葡萄酒中水/洗涤剂的百分比)低至2.5%,显示出高灵敏度。对于乙醇和去离子水的混合物,传感器能够检测浓度为1%的乙醇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Versatile Reflective-Mode Phase-Variation Microwave Sensor Based on a Dumbbell Defect-Ground-Structure (DB-DGS) Resonator for Solid and Liquid Characterization
This article presents a single-frequency planar microwave sensor designed for characterizing solid and liquid samples. The sensor operates in reflective-mode one-port configuration utilizing a microstrip line with a series inductive strip terminated with a transversely oriented dumbbell defect-ground-structure (DB-DGS) resonator (etched in the ground plane), the sensing element. The output variable is the phase of the reflection (RX) coefficient at a specific (operating) frequency. The device is modeled by a lumped circuit consisting of a parallel resonator (accounting for the DB-DGS resonator) series connected to an inductor and a parasitic capacitor. This configuration enables the generation of a steep phase slope in the RX coefficient at the operating frequency, which is essential for achieving high sensitivity. The sensor can be applied to the detection of small variations in the permittivity of the environment surrounding the sensing element in the vicinity of a reference (REF) value, where such REF value can be selected at wish by conveniently tuning the operating frequency. To demonstrate the versatility of the proposed device, three different scenarios are considered: 1) dielectric constant of solid samples measurement; 2) ethanol concentration in deionized (DI) water determination; and 3) detection of water/detergent in wine. The maximum sensitivity achieved for the dielectric characterization of solids is –382.44° per unit of dielectric constant. For liquid sensing, the sensor exhibits sensitivities of 9°/% for ethanol concentration in water and 0.88°/% for detecting water/detergent mixtures in wine. Despite the similar electromagnetic characteristics of water/detergent and wine, the proposed sensor can resolve a volume fraction (percentage of water/detergent in wine) as little as 2.5%, demonstrating high sensitivity. For the mixture of ethanol and DI water, the sensor is capable of detecting a concentration of 1% of ethanol.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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