A nanostructured electrode for use in low-temperature solid oxide fuel cells has drawn attention due to its high activity. Among various nanostructure fabrication processes, sputtering has shown superior characteristics for nanostructured electrode fabrication and does not require high temperatures. However, the limited performance of sputtered Ni-based anodes and the current lack of a fundamental understanding of nanostructural control remain significant issues. Here, the fabrication process for a high-performance nanostructured Ni anode that works by tailoring a nano-column structure is presented. Controlling the sputtering deposition angle and rotation speed of the substrate significantly improves the in-plane connectivity of the nanostructured Ni anode, resulting in a 50% enhancement in the peak power density of the cell. The maximum performance of the cell with the tailored anode nanostructure was 304 and 477 mW cm⁻² at 450 and 500 °C, respectively, which represents the best recorded performance of an anodic aluminum oxide (AAO)-supported Ni-based thin-film SOFC. Further investigation via 1-dimensional simulation showed that the enhancement in the in-plane continuity of the columnar anode structure dominantly affects the performance of TF-SOFCs, which means that the novel process is promising for practical applications of nano-energy devices fabricated by sputtering.

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.

In this study, 3D-printed polymer bipolar plates were fabricated and applied to the polymer electrolyte membrane fuel cells (PEMFCs). In order to overcome the poor electronic conductivity of polymer bipolar plates, silver current collecting layers were deposited using a sputter process. Influences of the thickness of the current collecting layer were carefully investigated to optimize the thickness. Using a sputter process, current collecting layers were successfully deposited without clear evidence of defects. Additionally, the increment in the total weight of bipolar plates was minimized. The average thickness of the silver current collecting layer was varied from 216 nm to 1.46 um. The results showed the fuel cell with 1.46 um thick Ag current collecting layer coated 3D printed bipolar plates achieved 0.96 V of the open circuit voltage and 308.35 mW/cm² of performance at 25 °C.