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启发式方法来预测固体氧化物燃料电池阴极的性能降解

Muhammad Zubair Khan¹, Muhammad Taqi Mehran², Amjad Hussain^3,4

¹Department of Materials Science & Engineering, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Mang, Haripur22621, Khyber Pakhunkhwa, Pakistan.

ACS applied materials & interfaces

|September 13, 2023

概括

固体氧化物燃料电池 (SOFC) 阴极的性能降解是通过量化介质迁移和绝缘相形成来预测的. 这项研究提供了对阴极降解的系统理解,以改善寿命预测.

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
化学工程是化学工程的重要组成部分.

背景情况:

固体氧化物燃料电池 (SOFC) 对于清洁能源转换至关重要.
阴极降解,特别是离子间扩散和表面分离,限制了SOFC的性能和寿命.
了解这些降解机制对于开发持久的SOFC至关重要.

研究的目的:

为了预测固体氧化物燃料电池 (SOFC) 阴极的性能退化.
调查 (La_0.60Sr_0.40) _0.95Co_0.20Fe_0.80O_3- (LSCF) -Gd_0.10Ce_0.90O_1.95 (GDC) 复合阴极中的离子迁移和表面分离.
量化GDC中间层中形成的绝缘相及其对正极性能的影响.

主要方法:

扫描传输电子显微镜 (STEM) 与能量分散式X射线光谱学 (EDXS) 结合,分析1000小时以上的离子迁移.
二维 (2D) 图像分析以量化GDC间层中绝缘相的体积分数.
电化学阻抗光谱 (EIS) 用于表征欧姆电阻和极化电阻.
数学建模以建立降解估计的关系.

主要成果:

观察和量化了GDC中间层内的C迁移和绝缘阶段的形成.
该研究首次成功量化了绝缘阶段,将其与性能降低相关联.
预测的性能降低与实验电化学阻抗光谱 (EIS) 结果有很好的一致性.

结论:

这项研究提供了对LSCF-GDC阴极的依赖时间的化迁移和分离的定量分析.
已建立的数学关系能够准确预测阴极退化和寿命.
这项工作提供了一种系统的方法来理解和减轻SOFC阴极降解,以提高耐用性.

关键词:

阴极降解降解的阴极降解.电离间扩散的扩散.终身预测预测的时间分离隔离的分离.固体氧化物燃料电池燃料电池的使用情况

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