用于流体分析的交错级联对称SRR平面微波传感器

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Luqman Ali;Gaofeng Wang;Kishor Kumar Adhikari;Ijaz Khan;Cong Wang
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引用次数: 0

摘要

本文提出了一种利用交错级联对称劈裂环谐振器(symr - srrs)精确检测水-乙醇和水-甲醇等液体混合物的平面微波传感器,具有较高的灵敏度。该传感器采用高度局部化的分裂环谐振器(SRR)结构,该结构限制强电场,允许通过观察到的频移来精确监测液体混合物样品。Sym-SRRs谐振频率为2.4 GHz和3.32 GHz,可实现双模传感,提高了对流体样品微小变化的检测。此外,该设计还采用了锥形传输线,以优化从微带馈电到谐振器的阻抗转换,从而提高灵敏度并最大限度地减少信号损失。锥形传输线增强了功率传输,最大限度地减少了反射,并与被测材料(MUT)最佳地相互作用,从而产生更清晰的共振峰和更精确的频移。传感器的几何设计经过优化,可以产生均匀的电场,确保与MUT的有效相互作用,以实现准确的检测。通过将各种测试样品装入传感器,由此产生的谐振频率的测量位移对甲醇(4.81 MHz/ $\varepsilon _{r}$)和乙醇(3.96 MHz/ $\varepsilon _{r}$)产生高灵敏度。通过实验验证,制作的裸传感器原型与理论预测具有良好的相关性。研究结果验证了该传感器在准确表征流体介质介电特性方面的性能。通过将传感器装入各种测试样品,我们观察到谐振频率的显著变化,显示出对甲醇和乙醇的高灵敏度。这些发现证实了传感器在准确表征流体介质介电特性方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Planar Microwave Sensor Using Interleaved Cascaded Symmetric SRR for Fluidic Analysis
This article presents a planar microwave sensor utilizing interleaved cascaded symmetric split-ring resonators (Sym-SRRs) to accurately detect liquid mixtures, such as water–ethanol and water–methanol, with high sensitivity. The proposed sensor employs highly localized split-ring resonator (SRR) structures that confine strong electric fields, allowing for the precise monitoring of liquid mixture samples through observed frequency shifts. The Sym-SRRs resonate at frequencies of 2.4 and 3.32 GHz, facilitating dual-mode sensing and improving the detection of minute variations in fluidic samples. In addition, the design incorporates a tapered transmission line to optimize impedance transition from the microstrip feed to the resonator, thereby enhancing sensitivity and minimizing signal losses. The tapered transmission line enhances power transfer, minimizes reflections, and interacts optimally with the material under test (MUT), resulting in sharper resonance peaks and more precise frequency shifts. The geometric design of the sensor is optimized to generate a uniform electric field, ensuring effective interaction with the MUT for accurate detection. By loading the sensor with various test samples, the resulting measured shifts in the resonant frequency yield high sensitivity for methanol (4.81 MHz/ $\varepsilon _{r}$ ) and ethanol (3.96 MHz/ $\varepsilon _{r}$ ). Through experimental validation, a fabricated prototype of the bare sensor has shown a good correlation with theoretical predictions. The findings validate the sensor’s performance in accurately characterizing the dielectric properties of fluidic media. By loading the sensor with various test samples, we observed significant shifts in the resonant frequency, demonstrating high sensitivity for methanol and ethanol. These findings confirm the sensor’s effectiveness in accurately characterizing the dielectric properties of fluidic media.
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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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