Cu-Ni Oxidation Mechanism Unveiled: A Machine Learning-Accelerated First-Principles and <i>in Situ</i> TEM Study.
basic_science · Level V
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- Record sourced from PubMed, PMID 39808182.
- Also identified by DOI 10.1021/acs.nanolett.4c04648.
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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. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with <i>in situ</i> 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)<i>R</i>45° missing row reconstruction (MRR). <i>In situ</i> 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 Cu<sub>2</sub>O in MRR regions. Our approach based on combining disparate computational components and <i>in situ</i> ETEM provides a holistic description of the oxidation mechanism in CuNi, which applies to other alloy systems.