Magnetic nanoparticles have an important role as heat generators in magnetic fluid hyperthermia, a type of nextgeneration cancer treatment. Despite various trials to improve the heat generation capability of magnetic nanoparticles, iron oxide nanoparticles are the only approved heat generators for clinical applications, which require a large injection dose due to their low hyperthermia efficiency. In this study, iron oxide nanoclusters (NCs) with a highly enhanced hyperthermia effect and adjustable size were synthesized through a facile and simple solvothermal method. Among the samples, the NCs with a size of 25 nm showed the highest hyperthermia efficiency. Differently sized NCs exhibit inconsistent interparticle crystalline alignments, which affect their magnetic properties (e.g., coercivity and saturation magnetization). As a result, the optimal NCs exhibited a significantly enhanced heat generation efficiency compared with that of isolated iron oxide nanoparticles (ca. 7 nm), and their hyperthermia effect on skin cancer cells was confirmed.
Anode-supported solid oxide fuel cells (SOFCs) model based on artificial neural network (ANN) and optimized design variables were modeled. The input parameters of the anode-supported SOFC model developed in this study are as follows: current density, temperature, electrolyte thickness, anode thickness, anode porosity, and cathode thickness. Voltage was estimated from the SOFC model with the input parameters. Numerical results show that the SOFC model constructed in this study can represent the actual SOFC characteristics very well. There are four design parameters to be optimized: electrolyte, anode, cathode thickness, and anode porosity. To derive the optimal combination of the design parameters, we have used a multi-armed bandit algorithm (MAB), and developed a methodology for deriving near-optimal parameter set without searching for all possible parameter sets.
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