Biaxially oriented silica-polypropylene nanocomposites for HVDC film capacitors: Morphology-dielectric property relationships, and critical evaluation of the current progress and limitations

Dielectric polymer nanocomposites are considered as one of the most promising insulation material candidates for future capacitive energy storage applications, providing tailorability of charge trapping and transport properties at the...

Phase Structure and Electrical Properties of Sm-Doped BiFe0.98Mn0.02O3 Thin Films

Bi1−xSmxFe0.98Mn0.02O3 (x = 0, 0.02, 0.04, 0.06; named BSFMx) (BSFM) films were prepared by the sol-gel method on indium tin oxide (ITO)/glass substrate. The effects of different Sm content on the crystal structure, phase composition, oxygen vacancy content, ferroelectric property, dielectric property, leakage property, leakage mechanism, and aging property of the BSFM films were systematically analyzed. X-ray diffraction (XRD) and Raman spectral analyses revealed that the sample had both R3c and Pnma phases. Through additional XRD fitting of the films, the content of the two phases of the sample was analyzed in detail, and it was found that the Pnma phase in the BSFMx = 0 film had the lowest abundance. X-ray photoelectron spectroscopy (XPS) analysis showed that the BSFMx = 0.04 film had the lowest oxygen vacancy content, which was conducive to a decrease in leakage current density and an improvement in dielectric properties. The diffraction peak of (110) exhibited the maximum intensity when the doping amount was 4 mol%, and the minimum leakage current density and a large remanent polarization intensity were also observed at room temperature (2Pr = 91.859 μC/cm2). By doping Sm at an appropriate amount, the leakage property of the BSFM films was reduced, the dielectric property was improved, and the aging process was delayed. The performance changes in the BSFM films were further explained from different perspectives, such as phase composition and oxygen vacancy content.

Development of a Microwave Sensor for Solid and Liquid Substances Based on Closed Loop Resonator

In this work, a compact dielectric sensor for the detection of adulteration in solid and liquid samples using planar resonators is presented. Six types of filter prototypes operating at 2.4 GHz are presented, optimized, numerically assessed, fabricated and experimentally validated. The obtained experimental results provided an error less than 6% with respect to the simulated results. Moreover, a size reduction of about 69% was achieved for the band stop filter and a 75% reduction for band pass filter compared to standard sensors realized using open/short circuited stub microstrip lines. From the designed filters, the miniaturised filter with Q of 95 at 2.4 GHz and size of 35 mm × 35 mm is formulated as a sensor and is validated theoretically and experimentally. The designed sensor shows better sensitivity, and it depends upon the dielectric property of the sample to be tested. Simulation and experimental validation of the designed sensor is carried out by loading different samples onto the sensor. The adulteration detection of various food samples using the designed sensor is experimentally validated and shows excellent sensing on adding adulterants to the original sample. The sensitivity of the sensor is analyzed by studying the variations in resonant frequency, scattering parameters, phase and Q factor with variation in the dielectric property of the sample loaded onto the sensor.

Microwave Dielectric Property Retrieval from Open-Ended Coaxial Probe Response with Deep Learning

In the manuscript, we propose a new technique for determination of Debye parameters, representing the dielectric properties of materials, from the reflection coefficient response of open-ended coaxial probes. The method retrieves the Debye parameters using a deep learning model designed through utilization of numerically generated data. Unlike real data, using synthetically generated input and output data for training purposes provides representation of a wide variety of materials with rapid data generation. Furthermore, the proposed method provides design flexibility and can be applied to any desired probe with intended dimensions and material. Next, we experimentally verified the designed deep learning model using measured reflection coefficients when the probe was terminated with five different standard liquids, four mixtures,and a gel-like material.and compared the results with the literature. Obtained mean percent relative error was ranging from 1.21±0.06 to 10.89±0.08. Our work also presents a large-scale statistical verification of the proposed dielectric property retrieval technique.

Microwave Dielectric Property Retrieval from Open-Ended Coaxial Probe Response with Deep Learning

In the manuscript, we propose a new technique for determination of Debye parameters, representing the dielectric properties of materials, from the reflection coefficient response of open-ended coaxial probes. The method retrieves the Debye parameters using a deep learning model designed through utilization of numerically generated data. Unlike real data, using synthetically generated input and output data for training purposes provides representation of a wide variety of materials with rapid data generation. Furthermore, the proposed method provides design flexibility and can be applied to any desired probe with intended dimensions and material. Next, we experimentally verified the designed deep learning model using measured reflection coefficients when the probe was terminated with five different standard liquids, four mixtures,and a gel-like material.and compared the results with the literature. Obtained mean percent relative error was ranging from 1.21±0.06 to 10.89±0.08. Our work also presents a large-scale statistical verification of the proposed dielectric property retrieval technique.