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This study investigates the application of an artificial neural network to predict the complex dielectric properties of granular catalysts commonly used in microwave reaction chemistry. The study utilizes finite element electromagnetic simulations and two-dimensional convolutional neural networks to solve for a large solution space of varying dielectrics. This convolutional neural network was trained using a supervised learning approach and a common backpropagation. The frequency range of interest was between 0.1 to 13.5 GHz with the real part of the dielectric constants ranging from 1 to 100 and the imaginary part ranging from 0.0 to 0.2. The network was double validated using experimental data collected from a coaxial airline. The model was demonstrated to convert either experimental or computational derived scattering parameter to complex permittivities. Moreover, the model eliminates the need for iterative solutions that often have difficulty with the piecewise continuous nature of frequency dependent scattering parameters.

This study experimentally determines the dielectric properties of a powdery titanium (II) oxide (TiO) material within the microwave range (0.1-13.5 GHz). The properties were determined using a coaxial airline method using a TiO/paraffin mixture at several loading fractions. A permittivity of 60 for volume fraction below 30% and 100 for volume fraction above were measured.

The following study investigates the use of a coaxial transmission line for determining the properties of powdery dielectric materials (1-10GHz). Four powdery materials with dielectric constants ranging from 3.5 to 70 (SiO$_2$, Al$_2$O$_3$, CeO$_2$, and TiO$_2$) were experimentally investigated at varying volume loading fractions. Powder particles were mixed with a paraffin matrix and properties of the powder were analyzed using ten mixing equations to verify their accuracy. These powder-paraffin composites were also modeled at varying volume loadings for comparison with experimental data to gain a better understanding of the interactions between the different phases. The optimal volume loading fraction was determined to be 10% for all powders tested. A metric for selecting the most well-suited mixing equation was proposed that involved taking the ratio of the particle dielectric to that of the matrix. This study ultimately provides guidance for experimentally measuring the dielectric properties of unknown powdery materials that have application for new devices that utilized powder-based dielectric materials.

The dielectric enhancement and modulation of a cuprous oxide (Cu2O) microwave-active catalyst material is investigated from an experimental and computational point of view. Experimental synthesis of two particle morphologies that included a cube and spike were carried out using an EDTA hydrothermal synthesis method. The permittivity for the spiked particles at low volume fraction in a paraffin composite exhibited a 20% increase when compared to the cube-shaped particles at the same volume fraction. Using a finite difference time domain (FDTD) simulation, the improvement in permittivity was attributed to the enhanced electric field near the tip of the spike particles and the neighboring interaction at higher volume fractions. The increased electric field at the tips of the particles induces a change in polarizability (dipole density) within the matrix material, which increases the effective dielectric properties of the composite. Furthermore, it was determined that an electrically conductive particle within a high permittivity matrix material is advantageous for generating high localized electric fields that can be utilized for microwave-assisted catalytic reactions.