Decoding and tuning the surface stability of perovskite oxides at the atomic level for faster oxygen exchange kinetics in energy conversion devices

掺杂钙钛矿氧化物作为固体氧化物燃料电池（SOFC）中的功能电催化剂层，因为它们可以获得高的电子和氧离子电导率以及与普通电解质材料的良好相容性。最近已经发现，在操作条件下，掺杂钙钛矿的钙钛矿（例如La 1-xSrxMnO 3（LSM））形成覆盖电极表面的偏析层，并且该过程对于电极表面活性是有害的，因此随着时间的推移使材料和SOFC性能劣化。由于阳离子尺寸不匹配以及与近表面区域空间电荷区的静电相互作用而导致的应变能降低已被认为是导致阳离子分离的关键机制。偏析受外部参数如p（O2）、T和外加电位的影响。由于外部条件和材料性质（即阳离子尺寸失配和氧空位浓度）之间的相互作用没有在原子水平上定量理解，因此不可能使用基于知识的方法来稳定这些表面。本项目旨在定量地了解表面结构与环境条件和材料性能的关系，并预测提高表面偏析稳定性和电化学活性的条件。建议的计算框架统一的材料特性和热力学因素到一个单一的蒙特-卡罗模型预测的近地表区域的演变。该模型的预期结果是能够预测掺杂剂和宿主阳离子在钙钛矿氧化物的近表面区域的分布，通过明确占氧空位的分布（引起提出的空间电荷区）。作为一种具有重要技术意义的钙钛矿型电催化剂体系，本研究以La0.8Sr0.2MnO3为出发点。接近表面的氧空位和SrLa'缺陷的形成能将通过密度泛函理论计算来计算，并根据团簇展开进行分析，以获得晶格哈密顿量。近表面区域的蒙特卡罗模拟将产生氧空位的分布，以及作为T和p（O2）的函数的阳离子偏析分布。第二相的形成将通过从头算热力学研究，考虑可能的候选物的形成和界面能（例如，SrO，Ruddlesden-Popper相）。通过用过渡金属阳离子改性的表面的稳定化将通过考虑由Hf对Sr偏析分布的Mn的表面取代的效果来研究。该模型将使人们有可能准确地了解导致阳离子偏析的因素，从而提供了原子尺度上的表面性质的优化的第一性原理的基础。

Doped perovskite oxides serve as functional electrocatalyst layers in solid oxide fuel cells (SOFCs) because they can attain high electronic and oxide ion conductivity as well as good compatibility with common electrolyte materials. It has recently been identified that aliovalently doped perovskites, such as La1-xSrxMnO3 (LSM), form segregation layers capping the electrode surface under operating conditions, and this process is detrimental for electrode surface activity, thus degrading the material and SOFC performance over time. Reduction of strain energy due to cation size mismatch and electrostatic interaction with a space charge zone in the near-surface region have been proposed as the key mechanisms leading to cation segregation. The segregation is affected by external parameters, such as p(O2), T and applied potential. Because the interplay between the external conditions and material properties (i.e. cation size mismatch, and oxygen vacancy concentration) are not quantitatively understood on the atomistic level, it has not been possible to stabilize these surfaces using knowledge-based approaches. This project aims to quantitatively understand the relations of surface structure with environmental conditions, and material properties, and to predict conditions where stability against surface segregation and electrochemical activity are improved. The proposed computational framework unites the material properties and thermodynamic factors into a single Monte-Carlo model for predicting the evolution of the near-surface region. The expected outcome of the model is the ability to predict the distribution of dopant and host cations at the near-surface region of perovskite oxides, by accounting explicitly for the distribution of oxygen vacancies (giving rise to the proposed space charge zone). As a technologically important model perovskite electrocatalyst system, this research takes La0.8Sr0.2MnO3 as a starting point. The formation energies of oxygen vacancies and SrLa' defects close to the surface will be computed by density functional theory calculations and analyzed in terms of a cluster expansion in order to obtain a lattice Hamiltonian. Monte Carlo simulations of the near-surface region will yield the distribution of oxygen vacancies, as well as the cation segregation profile as a function of T and p(O2). Secondary phase formation will be studied by ab-initio thermodynamics considering formation and interface energies of likely candidates (e.g., SrO, Ruddlesden-Popper phases). Stabilization of the surface by modification with transition metal cations will be studied by considering the effect of surface substitution of Mn by Hf on the Sr segregation profile. This model will make it possible to understand accurately the factors leading to cation segregation and thus provide a first-principles basis for the optimization of surface properties on the atomic scale.