The traditional design method for terahertz metasurface biosensors is cumbersome and time-consuming, requires expertise, and often leads to significant discrepancies between expected and actual values. This paper presents a novel approach for the fast, efficient, and convenient inverse design of THz metasurface sensors, leveraging convolutional neural network techniques based on deep learning. During the model training process, the magnitude data of the scattering parameters collected from the numerical simulation of the THz metasurface served as features, paired with corresponding surface structure matrices as labels to form the training dataset. During the validation process, the thoroughly trained model precisely predicted the expected surface structure matrix of a THz metasurface. The results demonstrate that the proposed algorithm realizes time-saving, high-efficiency, and high-precision inversion methods without complicated data preprocessing and additional optimization algorithms. Therefore, deep learning algorithms offer a novel approach for swiftly designing and optimizing THz metasurface sensors in biomedical detection, bypassing the complex and specialized design process of electromagnetic devices, and promising extensive prospects for their application in the biomedical field.

This article reports a novel dielectric model developed for estimating the complex permittivity of unconsolidated porous media containing gas hydrate. The complex permittivity spectra were experimentally obtained by using open-ended-coaxial-probes over a frequency range between 1 MHz and 3 GHz, in which the dielectric dispersion of both hydrate and liquid solution are covered. With tetrahydrofuran used as the hydrate former, reflection coefficients were recorded during the hydrate formation and dissociation processes in quartz sands and the complex permittivity spectra were inversed. Volumetric fractions estimated from the X-ray-tomography were used as the referenced values. Experimental data showed that the Maxwell-Wagner effect, surface conductance, and phase configuration can affect the bulk permittivity. The discrepancy was found to be unacceptable when the fitting was conducted with pre-existing models such as complex-refractive-index-method (CRIM) and Maxwell-Wagner-Bruggeman-Hanai (MWBH). In this study, by modifying the nested Wagner's theory, a shell-coated model was applied, and the surface of the solid sphere was assumed to possess surface conductance when it was humidified by the liquid solution. In contrast to CRIM and MWBH, the proposed model allows more accurate estimation of the volumetric fraction. By adopting this model with a range of dielectric measurements with different phase configurations, temperature, particle size, surface conductivity, and frequency, the contents of components and their influences onto the bulk permittivity can be physically estimated. The proposed model provides an essential tool for the interpretation of dielectric dispersion curves and the prediction of the volumetric fractions, which can be useful for both the field and laboratory applications.

As oil and gas reservoirs progress into the mid-to-late stages of development, produced fluids increasingly exhibit high water-cut and complex flow regimes. Conventional water-cut measurement techniques based on capacitance, conductance, and resistance often face challenges in terms of accuracy, stability, and adaptability. In this study, a novel non-contact broadband microwave system, based on a ridged-horn antenna microwave transmission sensor (RHAMTS), is proposed to achieve highly sensitive full-range (0–100%) water-cut monitoring. The RHAMTS consists of two identical ridged-horn antennas, whose geometries are optimized through analytical design calculations and full-wave finite-element simulations. Numerical simulations are first performed to elucidate the sensing mechanism. Subsequently, static and dynamic experiments are conducted under two representative conditions: emulsified oil-water mixtures and stratified oil-water layers. The results indicate that the broadband spectral signatures of the RHAMTS can effectively characterize water-cut in both emulsified mixtures and stratified oil-water layers. For emulsified mixtures, both amplitude attenuation and phase shift vary systematically with water-cut, and the RHAMTS can still effectively characterize water-cut under saline conditions. For stratified oil-water flow, results from both static and dynamic experiments demonstrate that amplitude attenuation provides more robust features for practical water-cut discrimination. Compared with conventional methods, the proposed RHAMTS offers non-contact operation, rich spectral information, and compatibility with various flow regimes, providing a feasible and efficient approach for water-cut monitoring under complex field conditions.

A method based on a unidirectional monopole ultrawideband antenna was proposed to evaluate the fluid saturation around borehole. The spectrum span of the excitation signal covered 750 MHz to 2.5 GHz and the slot antenna was optimized to transmit lateral wave more efficiently. The pulsed evaluation method offers more superiority when compared with the electromagnetic propagation tool and dielectric tools based on discrete frequencies. Measurement of complex permittivity in a wideband spectrum provides more accurate fluid saturation evaluation around the borehole. This method has been proven to operate effectively in a borehole filled with oil-based mud and to launch enough wave energy into the formation directionally. In addition, the proposed scheme is verified by using finite-difference time-domain method, and the simulation results show that this design adapts to evaluation of the fluid saturation and the detection of formation boundary. This scheme may be used as an alternative tool for logging while drilling the imaging application.