Atomic scale understanding of initial Cu-Ni oxidation from machine-learning accelerated first-principles simulations and in situ TEM experiments
Pandu Wisesa, Meng Li, Matthew T. Curnan, Jeong Woo Han, Judith C., Yang, and Wissam A. Saidi

TL;DR
This study combines machine learning-accelerated simulations and in situ TEM experiments to understand atomic-scale surface reconstructions and oxidation behaviors of Cu-Ni alloys, revealing Ni segregation and oxide formation mechanisms.
Contribution
It introduces a novel integrated approach using advanced simulations and experiments to elucidate atomic-level oxidation processes in alloy surfaces.
Findings
Ni segregation promotes Cu(100)-O c(2x2) reconstruction
NiO nucleation occurs in regions without MRR
Simulated behaviors are validated by in situ TEM observations
Abstract
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. Current oxidation models, such as Cabrera-Mott, are based on macroscopic empirical knowledge that lacks fundamental insight at the atomic level. Using machine learning-accelerated density functional theory with in situ 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 based on molecular dynamics and grand canonical Monte Carlo simulations shows that oxygen-induced Ni segregation in CuNi alloy favors Cu(100)-O c(2x2) reconstruction and destabilizes the Cu(100)-O missing row reconstruction. The underpinnings of these…
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Taxonomy
TopicsCopper-based nanomaterials and applications · Nanoporous metals and alloys · Electron and X-Ray Spectroscopy Techniques
