Ultralow reaction barriers for CO oxidation in Cu-Au nanoclusters.
Anastasiia A MikhailovaSergey V LepeshkinVladimir S BaturinAlexey P MaltsevYurii A UspenskiiArtem R OganovPublished in: Nanoscale (2023)
Systematic structure prediction of Cu n Au m nanoclusters was carried out for a wide compositional area ( n + m ≤ 15) using the evolutionary algorithm USPEX and DFT calculations. The obtained structural data allowed us to assess the local stability of clusters and their suitability for catalysis of CO oxidation. Using these two criteria, we selected several most promising clusters for an accurate study of their catalytic properties. The adsorption energies of reagents, reaction paths, and activation energies were calculated. We found several cases with low activation energies and explained these cases using the patterns of structural change at the moment of CO 2 desorption. The unique case is the Cu 7 Au 6 cluster, which has extremely low activation energies for all transition states (below 0.05 eV). We thus showed that higher flexibility due to the binary nature of nanoclusters makes it possible to achieve the maximum catalytic activity. Considering the lower price of copper, Cu-Au nanoparticles are a promising new family of catalysts.
Keyphrases
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- density functional theory
- aqueous solution
- molecular dynamics
- quantum dots
- visible light
- metal organic framework
- reduced graphene oxide
- hydrogen peroxide
- machine learning
- fluorescent probe
- electron transfer
- nitric oxide
- big data
- deep learning
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- molecular dynamics simulations
- gold nanoparticles
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- crystal structure
- neural network
- data analysis