Mediator-Free Multiple Solution Identification and Sensing Through Diverse-Structure Microwave Resonator Units

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 10
• Main Authors: Song, Yi-Ran, Liang, Jun-Ge, Wu, Jia-Kang, Lin, Song, Wu, En-Kang, Qiang, Tian, Ding, Xu-Min, Gu, Xiao-Feng, Wang, Cong
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Capacitance; Ethanol; Mediator free; Microfluidics; Microwave circuits; Microwave integrated circuits; microwave resonator array; Microwave sensors; multisolution detection; neural network; Neural networks; Parameter sensitivity; Potassium chloride; Principal component analysis; principal component analysis (PCA); Principal components analysis; Resonant frequency; Resonators; Rings (mathematics); Sensitivity; Sensor arrays; Sensors
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2025.3554885

It is necessary to develop a mediator-free multisolution beyond the single-object detection limitations of traditional methods to fulfill the current complex needs in solution detection. In this work, the concentrations of ionic and molecular solutions were measured using a mediator-free microwave sensor array. The array consists of four types of resonators commonly used for solution detection: complementary interdigital capacitor (CIDC), square complementary split-ring resonator (SCSRR), complementary spiral resonator (CSR), and complementary symmetric split-ring resonator (CSSRR). We analyzed the effect of resonator structures on solution sensitivity across various polarization modes using microwave parameters (amplitude and frequency) analysis. Results indicated a high sensitivity (3.3 MHz/% for ethanol, 6 MHz/% for KCl, and 1 MHz/% for NaCl) and low limits of detection (1.32% for ethanol, 0.02% for KCl, and 0.03% for NaCl), within a broad detection range (20%100% for ethanol and 5%-25% for KCl and NaCl). In addition, the identification for multisolution detection by resonator units was compared based on the principal component analysis (PCA) algorithm. By combining all array units, a neural network algorithm was developed to map the microwave parameters to the concentrations of multisolution. This research offers new insights into sensor structure selection and multisolution sensing, broadening potential applications in microwave-based sensing.