Cu-Ni Oxidation Mechanism Unveiled: A Machine Learning-Accelerated First-Principles and in Situ TEM Study.

Area of Science:

• Materials Science
• Surface Science
• Computational Materials Science

Background:

• Understanding alloy surface restructuring under reactive conditions is a major challenge.
• Alloy behavior in corrosive environments is critical for material performance and longevity.
• Predicting surface changes requires sophisticated modeling and experimental validation.

Purpose of the Study:

• To investigate the interplay between surface reconstruction and segregation in CuNi(100) alloys during oxidation.
• To develop and validate a predictive model for alloy surface behavior under reactive conditions.
• To elucidate the oxidation mechanism of CuNi alloys.

Main Methods:

• Utilized machine learning-accelerated density functional theory (DFT) and rare-event methods for modeling.
• Employed *in situ* environmental transmission electron microscopy (ETEM) for experimental observation.
• Combined computational predictions with *in situ* experimental validation.

Main Results:

• Computational modeling predicted that oxygen-induced nickel segregation favors Cu(100)-O c(2 × 2) reconstruction.
• The modeling also predicted destabilization of the Cu(100)-O (2√2 × √2)R45° missing row reconstruction (MRR).
• *In situ* ETEM confirmed Ni segregation, NiO nucleation, and Cu2O growth, validating the model's predictions.

Conclusions:

• The combined computational and experimental approach provides a holistic description of CuNi oxidation.
• The findings highlight the importance of considering segregation effects on surface reconstruction.
• This methodology is applicable to understanding oxidation mechanisms in other alloy systems.