Wisesa Pandu, Li Meng, Curnan Matthew T, Gu Geun Ho, Han Jeong Woo, Yang Judith C, Saidi Wissam A
Department of Mechanical Engineering & Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Nano Lett. 2025 Jan 29;25(4):1329-1335. doi: 10.1021/acs.nanolett.4c04648. Epub 2025 Jan 14.
The development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach predicts that oxygen-induced Ni segregation in CuNi alloys favors Cu(100)-O c(2 × 2) reconstruction and destabilizes the Cu(100)-O (2√2 × √2)45° missing row reconstruction (MRR). ETEM experiments validate these predictions and show Ni segregation followed by NiO nucleation and growth in regions without MRR, with secondary nucleation and growth of CuO in MRR regions. Our approach based on combining disparate computational components and ETEM provides a holistic description of the oxidation mechanism in CuNi, which applies to other alloy systems.
开发精确方法以确定合金表面在反应性和腐蚀性环境下如何自发重构，是材料科学中一个关键的、长期存在的重大挑战。我们使用机器学习加速的密度泛函理论和稀有事件方法，并结合环境透射电子显微镜（ETEM），研究了氧化条件下CuNi(100)表面的表面重构与优先偏析倾向之间的相互作用。我们的建模方法预测，CuNi合金中氧诱导的Ni偏析有利于Cu(100)-O c(2×2)重构，并使Cu(100)-O (2√2×√2)45°缺失行重构（MRR）不稳定。ETEM实验验证了这些预测，并表明在没有MRR的区域中，Ni偏析后接着是NiO的成核和生长，而在MRR区域中则是CuO的二次成核和生长。我们基于结合不同计算组件和ETEM的方法，对CuNi中的氧化机制提供了全面描述，该方法也适用于其他合金体系。
