An effective approach for finite-difference time-domain modeling of graphene as a conducting sheet is proposed. First, we present a new technique for implementing a conducting surface boundary condition in the FDTD method; then, the dispersive surface conductivity of graphene is imposed. Numerical examples are presented to show the stability, accuracy, applicability, and advantages of the proposed approach. Validation is achieved by comparison with existing analytic methods.

A dual-band and polarization-independent electromagnetic energy harvester composed of an array of pixelated unit cells is proposed. The pixelated unit cell is basically a dual-band resonator loaded with two resistors which model the input impedance of a power combining circuit in a complete harvesting system. To design the unit cell, a topology optimization approach based on pixelization of the surface of the unit cell and application of a binary optimization algorithm is used. The optimization goal is set to maximize harvesting efficiency at 2.45 GHz and 6 GHz. In our design, full symmetry of the unit cell is considered to achieve insensitivity to the polarization of the incident wave. Once, the unit cell is designed, as a proof of the concept, a metasurface harvester composed of 9 × 9 pixelated cells is designed. The full-wave electromagnetic simulation results demonstrate that the proposed metasurface absorbs the incident electromagnetic wave energy with nearly unity efficiency at both frequencies of interest and irrespective the polarization of the incident field while simultaneously delivering the absorbed power to the loads. To validate the simulations, the metasurface harvester is fabricated and tested in an anechoic chamber. A strong agreement between the simulation results and measurements is observed.

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 article presents a new concept for sensitivity enhancement of dielectric sensors by loading a microstrip line with multiple coupled resonators. The concept is based on the interresonator coupling mechanism, which is represented as a mutual capacitance in an equivalent lumped circuit model. This mutual capacitance increases the sensitivity of the sensor (in terms of a shift in the resonance frequency) to detect the presence of dielectric materials (i.e., detecting changes in the real-relative permittivity of dielectric laminates). Complementary split-ring resonators (CSRRs) are utilized and coupled to a splitter-combiner microstrip section to design a 2CSRR and a 4CSRR sensor. The new sensors provided an appreciable enhancement in the sensitivity when detecting dielectric material. The concept is tested using full-wave numerical simulations to detect variations in the dielectric constant of a slab. Finally, full experimental validation is provided using fabricated sensors.