A Microwave Sensor System Based on M-SRRs for Assessing the Complex Permittivity of Liquid Samples

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Wen-Jing Wu;Hao Xie;Wen-Sheng Zhao;Wensong Wang
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Abstract

In this article, a differential modified split-ring resonator (M-SRR)-based microwave sensor system is proposed to extract the complex permittivity of liquid samples. The proposed sensor system is constituted by the differential circuit part and demodulator; meanwhile, the differential circuit part consists of an RF generator, two RF switches, and two M-SRR-based passive sensors. The confinement of electrical field can be enhanced for the SRR-based passive sensor with interdigital capacitor added, and two RF switches are added to realize the capability of differential detection. For the differential circuit part, the upper path would be activated when the RF switches are turned to the upper branch, and this output signal is used as a reference signal. Besides, the lower path would be activated when the RF switches are turned to the lower branch, which is regarded as a test channel. The demodulator contains a power divider, hybrid coupler, transmission line (TL), mixer, and low-pass filter (LPF), and it can convert the power level of RF signal into an output dc voltage. As known, the injected liquid samples with various complex permittivity would induce the different output dc voltages, and then, the mathematical model describing the association between dc voltage and complex permittivity is built. In the test, the RF generator outputs an RF signal at about 1790 MHz with the power level of 10 dBm, and the average sensitivities of about 0.807 and ${1.12}~\text {mV} /{\varepsilon }_{r}^{\prime }$ are obtained, which are enhanced by about 7.6%–49.3% than recently reported ones. The sensitivity of per unit volume is calculated as 0.615/0.229 mV/( $\varepsilon _{r}^{\prime }\cdot \text { VL}$ ) as the volume of liquid sample is about $4.9~\mu \text { L}$ . The proposed sensor system is a strong contender in the field of detecting liquid samples.
基于M-SRRs的液体样品复合介电常数测量微波传感器系统
本文提出了一种基于微分修正裂环谐振器(M-SRR)的微波传感器系统,用于提取液体样品的复介电常数。所提出的传感器系统由差分电路部分和解调器组成;同时,差分电路部分由一个射频发生器、两个射频开关和两个基于m - srr的无源传感器组成。在srr型无源传感器中加入数字间电容,增强了对电场的约束,并增加了两个射频开关,实现了差分检测能力。对于差分电路部分,当射频开关转到上支路时,上路径被激活,该输出信号作为参考信号。另外,当射频开关转到下支路时,下支路将被激活,该支路作为测试通道。该解调器由功率分配器、混合耦合器、传输线(TL)、混频器和低通滤波器(LPF)组成,可将射频信号的功率电平转换为输出直流电压。已知不同复介电常数的注入液样会诱发不同的输出直流电压,进而建立直流电压与复介电常数关系的数学模型。在测试中,射频发生器输出约1790 MHz,功率电平为10 dBm的射频信号,得到平均灵敏度约为0.807和${1.12}~\text {mV} /{\varepsilon }_{r}^{\prime }$,提高约7.6%–49.3% than recently reported ones. The sensitivity of per unit volume is calculated as 0.615/0.229 mV/( $\varepsilon _{r}^{\prime }\cdot \text { VL}$ ) as the volume of liquid sample is about $4.9~\mu \text { L}$ . The proposed sensor system is a strong contender in the field of detecting liquid samples.
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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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