高的矿的性能预测La0.8Sr0.2MnxCoyFezO3与组合图书馆和机器学习的自动高通量表征
Carlota Bozal-Ginesta1,2, Juande Sirvent1, Giulio Cordaro3
1Nanoionics and Fuel Cells group, Catalonia Institute for Energy Research, Jardins de Les Dones de Negre 1, Sant Adrià de Besòs, Barcelona, 08930, Spain.
Advanced materials (Deerfield Beach, Fla.)
|November 5, 2024
概括
高的矿氧化物显示出对固体氧化物电池氧气电极的希望. 富含铁的组合物表现出最好的性能,与氧子网格扭曲有关.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 固氧化物燃料电池 固氧化物燃料电池
背景情况:
- 矿氧化物为各种应用提供可调节的性能.
- 探索广的组成空间需要先进的方法,如高通量实验和机器学习.
研究的目的:
- 研究高 La0.8Sr0.2Mnx C o y F e z O3±δ 矿氧化物中的组成-结构-性能关系.
- 在固体氧化物细胞 (SOC) 中确定氧气电极的最佳组成.
主要方法:
- 使用薄膜组合脉冲激光沉积制造连续组成图的制造.
- 使用六种绘图技术对构成性,结构性和电化学性质进行表征.
- 机器学习 (随机森林) 来建模电化学性能.
主要成果:
- 富含铁的矿氧化物被确定为最佳的氧气电极材料.
- 最低的区域特异性电阻值是在700°C以富含铁的组合物实现的.
- 在氧子网格扭曲和增强电极性能之间发现了相关性.
结论:
- 高的矿氧化物,特别是富含铁的矿氧化物,对于SOC氧气电极来说是有前途的.
- 机器学习有效地预测性能,并确定关键的结构-属性关系.
- 氧子晶格扭曲是高性能氧电极的关键因素.
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