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Planar microwave sensors have become increasing developed in recent decades, especially in material characterization (solid/liquid) as they provide regions highly sensitive to the surrounding medium. However, when it comes to deciphering the content of practical biological analytes or chemical components inside a host medium, even higher sensitivities are required due to their minute concentrations. This review article presents a comprehensive outlook on various methodologies to enhance sensitivity (e.g., coupling resonators, channel embedding, analyte immobilization, resonator pattern recognition, use of phase variation, using coupled line section, and intermodulation products), resolution (active sensors, differential measurements), and robustness (using machine learning) of arbitrary sensors of interest. Some of the most practical approaches are presented with prototype examples, and the main applications of incorporating such procedures are reported. Sensors with which the proposed techniques are implemented exhibit higher performance for high-end and real-life use.

This research explores the application of an artificial intelligence (AI)-assisted approach to enhance the selectivity of microwave sensors used for liquid mixture sensing. We utilized a planar microwave sensor comprising two coupled rectangular complementary split-ring resonators operating at 2.45 GHz to establish a highly sensitive capacitive region. The sensor’s quality factor was markedly improved from 70 to approximately 2700 through the incorporation of a regenerative amplifier to compensate for losses. A deep neural network (DNN) technique is employed to characterize mixtures of methanol, ethanol, and water, using the frequency, amplitude, and quality factor as inputs. However, the DNN approach is found to be effective solely for binary mixtures, with a maximum concentration error of 4.3%. To improve selectivity for ternary mixtures, we employed a more sophisticated machine learning algorithm, the convolutional neural network (CNN), using the entire transmission response as the 1-D input. This resulted in a significant improvement in selectivity, limiting the maximum percentage error to just 0.7% (≈6-fold accuracy enhancement).

This paper presents a novel planar multifunctional sensor that is used to monitor physical variations in the environment regarding distance, angle, and stretch. A double split-ring resonator is designed at 5.2 GHz as the core operating sensor. Another identical resonator is placed on top of the first one. The stacked configuration is theoretically analyzed using an electric circuit model with a detailed parameter extraction discussion. This design is first employed as a displacement sensor, and a compelling high sensitivity of 500 MHz/mm is observed for a wide dynamic range of 0-5 mm. Then, in another configuration, the stacked design is used as a rotation sensor that results in a high sensitivity of 4.5 MHz/ ° for the full range of 0-180 ° . In addition, the stacked resonator is utilized as a strain detector, and a 0–30% stretch is emulated with a linear sensitivity of 12 MHz/%. Measurements are well in congruence with simulated results, which proves the accurate functionality of the sensor in tracking mechanical deformations, all in a single compact contraption.

Encountering with a nonlinear second-order differential equation including ϵ r and μ r spatial distributions, while computing the fields inside inhomogeneous media, persuaded us to find their known distributions that give exact solutions. Similarities between random distributions of electric properties and known functions lead us to estimate them using three mathematical tools of artificial neural networks (ANNs), support vector machines (SVMs) and Fuzzy Logic (FL). Assigning known functions after fitting with minimum error to arbitrary inputs using results of machine learning networks leads to achieve an approximate solution for the field inside materials considering boundary conditions. A comparative study between the methods according to the complexity of the structures as well as the accuracy and the calculation time for testing of unforeseen inputs, including classification, prediction and regression is presented. We examined the extracted pairs of ϵ r and μ r with ANN, SVM networks and FL and got satisfactory outputs with detailed results. The application of the presented method in zero reflection subjects is exemplified.

Detecting the presence of materials in biomedical science using THz sensors, especially thin DNA strands, needs considerably sensitive sensors. Connection of unit cells in new frequency selective surface (FSS) structure, coupled with transmission line is introduced so that not only helps to miniaturize in sensing applications through THz frequency range, but also has steeper flanks in transitions, thus leads us to have higher sensitivities. All of the results are taken out from resonant frequencies related to reflection (s11), a new property used instead of transmission loss (s21), which is measurable using THz spectrometers. The effect of analyte thickness and dielectric properties of load on frequency response is explained. This approach is a new way in recognizing very small amounts of material even with rather low dielectric constants.

Limited sensitivity and sensing range are arguably the greatest challenges in microwave sensor design. Recent attempts to improve these properties have relied on metamaterial- (MTM-) inspired open-loop resonators (OLRs) coupled to transmission lines (TLs). Although the strongly resonant properties of the OLR sensitively reflect small changes in the environment through a shift in its resonance frequency, the resulting sensitivities remain ultimately limited by the level of coupling between the OLR and the TL. This work introduces a novel solution to this problem that employs negative-refractiveindex TL (NRI-TL) MTMs to substantially improve this coupling so as to fully exploit its resonant properties. A MTM-infused planar microwave sensor is designed for operation at 2.5 GHz, and is shown to exhibit a significant improvement in sensitivity and linearity. A rigorous signal-flow analysis (SFA) of the sensor is proposed and shown to provide a fully analytical description of all salient features of both the conventional and MTM-infused sensors. Full-wave simulations confirm the analytical predictions, and all data demonstrate excellent agreement with measurements of a fabricated prototype. The proposed device is shown to be especially useful in the characterization of commonly-available high-permittivity liquids as well as in sensitively distinguishing concentrations of ethanol/methanol in water.