Neural network-assisted atomic electron tomography

Yongsoo Yang
Korea Advanced Institute of Science and Technology (KAIST)

Functional properties of nanomaterials strongly depend on their surface and interface atomic structures, which often become largely different from their bulk structures, exhibiting surface reconstructions and relaxations. However, most of the surface/interface characterization methods are either limited to 2D measurements or not reaching to true 3D atomic-scale resolution. In this talk, I will demonstrate the measurements of 3D atomic structures at about 15 pm precision using Pt nanoparticles as a model system. Aided by a deep learning-based missing data retrieval combined with atomic electron tomography, the surface/interface atomic structures were reliably measured. From the structures, we found anisotropic strain distribution as well as compressive support boundary effect. A full 3D strain tensor was clearly mapped, which allows direct calculation of the oxygen reduction reaction activity at the surface. The capability of single-atom level surface characterization will not only deepen our understanding of the functional properties of nanomaterials but also open a new door for fine tailoring of their performance.

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