Adaptive kinetic Monte Carlo simulation of solid oxide fuel cell components
Adaptive kinetic Monte Carlo simulation of solid oxide fuel cell components
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DOI：
10.1039/c4ta01504e
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发表时间：
2014-07
影响因子：
6.4
通讯作者：
D. Gunn;N. Allan;J. Purton
中科院分区：
文献类型：
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作者：
D. Gunn;N. Allan;J. Purton
固体氧化物燃料电池（SOFC）电解质钇稳定氧化锆（YSZ）、钙稳定氧化锆（CSZ）、掺钆氧化铈（GDC）和掺钐氧化铈（SDC）以及正极材料镧锶钴氧化物（LSCO）的离子电导率直接使用DL_AKMC计算。DL_AKMC是一种自适应动力学蒙特卡罗（aKMC）程序，它假设系统的动力学知识有限。这些材料的模拟时间为几毫秒，并且在实验中最相关的温度和掺杂浓度范围内（掺杂氧化锆为2-18 mol%，掺杂氧化铈为5-25 mol%， LSCO为5-80 mol%）。电解质在1000 K时的离子电导率与观测值吻合良好：根据掺杂剂浓度的不同，CSZ在3 × 10−3 ~ 1 × 10−2 S cm−1之间，YSZ在4 × 10−3 ~ 3 × 10−2 S cm−1之间，GDC在1 × 10−2 ~ 5 × 10−2 S cm−1之间，SDC在1 × 10−2 ~ 7 × 10−2 S cm−1之间。根据Sr含量的不同，LSCO的离子电导率为10−2 ~ 10−1 S cm−1。所有迁移过程的平均活化能与实验结果一致，稳定氧化锆为0.4-0.5 eV，掺杂氧化铈为0.2-0.3 eV， LSCO为0.3 eV。与传统的KMC方法相比，aKMC提供了一个明显的优势，在传统的KMC方法中，必须提供系统状态转换的列表。在这里，所有的状态转换都是动态生成的，随着系统的发展，可以更准确地模拟动力学。
Ionic conductivities in the solid oxide fuel cell (SOFC) electrolytes yttria-stabilised zirconia (YSZ), calcia-stabilised zirconia (CSZ), gadolinium-doped ceria (GDC) and samarium-doped ceria (SDC) and the cathode material lanthanum strontium cobalt oxide (LSCO) are directly calculated using DL_AKMC, an adaptive kinetic Monte Carlo (aKMC) program which assumes limited a priori knowledge of the kinetics of systems. The materials were simulated over several milliseconds and over the range of experimentally most relevant temperatures and dopant concentrations (2–18 mol% for doped zirconia, 5–25 mol% for doped ceria and 5–80 mol% for LSCO). Ionic conductivities of the electrolytes at 1000 K are in good agreement with the observed values: CSZ in the range 3 × 10−3 to 1 × 10−2 S cm−1 depending on dopant concentration, YSZ 4 × 10−3 to 3 × 10−2 S cm−1, GDC 1 × 10−2 to 5 × 10−2 S cm−1, SDC 1 × 10−2 to 7 × 10−2 S cm−1. LSCO is predicted to have an ionic conductivity of the order of 10−2 to 10−1 S cm−1 depending on Sr content. Average activation energies over all migration processes are 0.4–0.5 eV for the stabilised zirconias and 0.2–0.3 eV for the doped cerias and 0.3 eV for LSCO, in agreement with experiment. aKMC provides a distinct advantage over traditional KMC methods, in which one has to provide a list of system state transitions. Here, all of the state transitions are dynamically generated, leading to a more accurate simulation of the kinetics as the system evolves.