Abstract:The goal of the current work was to identify an air-side-optimized contact material for La0.3Ca0.7Fe0.7Cr0.3O3−δ (LCFCr) electrodes and a Crofer22APU interconnect for use in reversible solid oxide fuel cells (RSOFCs). LaNi0.6Co0.4−xFexO3 (x = 0–0.3) perovskite-type oxides were investigated in this work. The partial substitution of Co by Fe decreased the thermal expansion coefficient values (TEC) closer to the values of the LCFCr and Crofer 22 APU interconnects. The oxides were synthesized using the glycine–nit… Show more
“…The perovskite structure is very versatile, which enables them to perform excellently in several high-interest applications, including high temperature technologies such as solid oxide fuel cells (SOFCs), and solid oxide electrolysis cells (SOECs). [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] In these high temperature applications one of the properties that is essential to performance is the thermal expansion coefficients (TECs) of the materials used for certain device components. Thermal expansion coefficients are proportionality constants of how a material expands or contracts with a change in temperature.…”
The support vector machine model produced the best results with a root mean square error of 1.54 × 10−6 K−1. The model was applied to 3 593 726 possible AA′BB′O3 compositions, resulting in 150 451 predictions in the model confidence region.
“…The perovskite structure is very versatile, which enables them to perform excellently in several high-interest applications, including high temperature technologies such as solid oxide fuel cells (SOFCs), and solid oxide electrolysis cells (SOECs). [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] In these high temperature applications one of the properties that is essential to performance is the thermal expansion coefficients (TECs) of the materials used for certain device components. Thermal expansion coefficients are proportionality constants of how a material expands or contracts with a change in temperature.…”
The support vector machine model produced the best results with a root mean square error of 1.54 × 10−6 K−1. The model was applied to 3 593 726 possible AA′BB′O3 compositions, resulting in 150 451 predictions in the model confidence region.
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