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 coupled to transmission lines (TLs). Although the strongly resonant properties of the resonator 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 resonator and the TL. This paper introduces a novel solution to this problem that employs negative-refractive-index 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 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.

It is shown here that microwave sensors can be used to monitor glucose in serum concentration with minimum detectable as well as resolution of 1 mMol ·L ^-1 ( ≈ 18 mg ·dL ^-1 ). The ultrasensitive detection technique relies on a split ring resonator, operating at the frequency of 1.156 GHz, as the core of the sensor where its loss is compensated to enhance the quality factor from ~ 190 (passive mode) to ~ 3850 (active mode) to enable high resolution (modified frequency detection error from ±12 kHz down to ±2.5 kHz) frequency-shift sensing. Initially, glucose concentrations of 100-1000 mMol ·L ^-1 (1800-18000 mg ·dL ^-1 ) in water were detected within 250 kHz of dynamic range (between two spectrum ends). Selectivity of the sensor to glucose is verified with respect to common interstitial fluid ingredients with biological levels. Finally, to enhance the resolution of the proposed sensor, its loss-compensation is further improved leading to increased accuracy of measuring glucose samples in a 0.9 % NaCl solution containing 10 % horse serum that closely resembles blood plasma and interstitial fluid. This allows exploration of lower concentrations in the physiological range 1-30 mMol ·L ^-1 (18-540 mg ·dL ^-1 ) with improved frequency detection error down to ±0.75 kHz for two cases of with/without serum solutions with dynamic range of 30 kHz/38 kHz. The highly accurate glucose monitoring technique could be utilized for developing noninvasive glucose sensors for biomedical applications in real-time glucose monitoring.

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).

In this paper, a new technique to enhance the sensitivity of microwave resonators is introduced. Double split ring resonators are implemented as the core of a loss-compensated resonator. It is illustrated that regenerative oscillators when mixed together can produce higher order intermodulation products (IMP) at the output. The variations in sensing tone are multiplied and exhibit considerably higher sensitivities at 3 ^rd , 5 ^th , and 7 ^th IMP components compared to the main resonant frequency. The sensor is also integrated into wireless platform with ultra-wideband bowtie antennas. Common fluids such as Toluene, n-Heptane, IPA, Ethanol, Methanol, Acetone, and Water are tested in fluidic channel and demonstrated that the sensitivity for intermodulation products are significantly increased proportional to the order of IMP. The proposed sensor is also examined with glucose concentration sensing for the range of 0- 600 mg/dL and significant variation for 7 ^th IMP sensor is observed as opposed to saturation of conventional sensor. Also, asphaltene concentrations down to 6 ppm are recognizable when precipitated from toluene solution using the proposed sensor. Moreover, a rigorous analytical study is presented for phase noise of the IMP originated from reference signals.