(607g) Lattice Oxygen Kinetics in Nanostructured Ceria: Combining Graph Neural Network Multi-Scale Simulations and in-Situ DRIFT Characterization

Ceria serves as a versatile catalyst in a wide range of reactions including automotive three-way catalysts and CO oxidation. The catalytic activity of ceria stems from its ability to readily release lattice oxygen, making ceria's reducibility crucial for understanding interactions with reaction intermediates on its surface. Density functional theory (DFT) studies have been conducted on well-controlled ceria surface systems at the atomic scale (~100 atoms). However, DFT calculations often rely on heuristic searches for active sites, overlooking geometric effects at the nanometer scale, limiting their applicability to recently proposed nanostructured ceria in the few-nanometer regime.

Here, we study the reduction process of lattice oxygen on the nanostructure-modified ceria systems via large-scale molecular dynamics using a pretrained Graph Neural Network (GNN). The pretrained GNN is successfully applied to the nanostructured ceria system, enabling simulations at larger time and size scales while maintaining DFT-level accuracy.

The lattice oxygen kinetics of the ceria surface is elucidated for the newly proposed 'Mace'-shaped ceria nanoparticles, where the morphology and size are precisely controlled in experiments during the hydrothermal ceria synthesis. Large-scale MD simulations reveal that facile lattice oxygen donation from ceria occurs under CO environment at the interface sites between rod (110) and cube (100) facets. Molecular dynamics simulations track the lattice oxygen donation process, akin to isotope tracing experiments, allowing the identification of the oxygen source. Furthermore, high-resolution characterizations using synchrotron and in-situ DRIFT techniques corroborate the facile oxygen donation at interfacial sites on ceria. By combining multi-scale simulations using a pretrained GNN and high-resolution experimental characterizations, this work provides a deep understanding of the oxygen kinetics in ceria and insights for rationally designing other metal oxide nanostructures.