(205a) Computationally Guided Design of Ferrite Nanoparticles for Magnetic Inductive Heating

Identifying alternative methods to efficiently heat catalysts is of interest for both operational and economic reasons. Current technology relies on heating via convection with reactant and product fluids. An alternative promising method is Magnetic Inductive Heating (MIH), where magnetic potential energy is converted into thermal energy to increase the temperature of the system. MIH provides rapid localized heating, efficient heat delivery, and tunable rate of heating, which could be beneficial for catalysts. This form of heating is observed in Magnetically Inductive Materials (MIM), a subset of ferromagnetic materials, where the material’s magnetic properties under an alternating external magnetic field create an energy hysteresis to generate heat. A key intrinsic magnetic property of MIMs is Magnetic Anisotropy (MA), which is the MIM's dependence of magnetic moment on the direction of an external magnetic field. In this study, we aim to learn how to control MA by tuning MIM composition. We are specifically interested in ferrites, i.e., 3d metal oxides based on magnetite (which has the chemical formula Fe₃O₄). Ferrites are promising materials as MIM due to their lower cost, durability, and magnetic properties that can be tuned by changing composition through doping. However, at present, the relationship between composition and inductive properties is unknown due to the large composition space. Experimentally, testing the various doped ferrites is time consuming and not tractable; alternatively, it can be accelerated using computational screening. Therefore, in this work, we aim to identify the relationship between ferrite composition and MA using Density Functional Theory (DFT). Specifically, we consider materials with the formulae M_xFe_(3-x)O₄ (0≤x≤1), where M can be any combination of Mn, Ni, Co, Cu, and Zn. Substitution into octahedral vs. tetrahedral sites and number of substitutions are considered using a high-throughput screening approach. Our results suggest that MA can be tuned by varying ferrite composition. We discuss how these materials can be used as catalysts for the Reverse Water-Gas shift reaction.