Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron...

Area of Science:

• Materials Science
• Nanotechnology
• Surface Science

Background:

• Nanomaterial properties are dictated by surface atomic structures, which often differ significantly from bulk structures due to reconstructions and relaxations.
• Current surface characterization techniques lack true 3D atomic-scale resolution or are limited to 2D measurements, leaving 3D surface atomic structures of nanomaterials largely undetermined.
• Precisely determining the 3D surface atomic structure at the single-atom level is crucial for understanding and engineering nanomaterial functionality.

Purpose of the Study:

• To demonstrate a novel method for measuring the 3D atomic structure of nanomaterial surfaces with picometer precision.
• To investigate the surface atomic structure and strain distribution of platinum nanoparticles.
• To establish a foundation for advanced nanomaterial characterization and design.

Main Methods:

• Utilized atomic electron tomography combined with deep learning-based missing data retrieval.
• Applied the developed method to a platinum nanoparticle as a model system.
• Achieved a measurement precision of 15 picometers for the 3D atomic structure.

Main Results:

• Successfully measured the 3D atomic surface structure of a platinum nanoparticle at 15 pm precision.
• Identified distinct contributions of <[Formula: see text]> and <[Formula: see text]> facets to surface strain.
• Observed anisotropic strain distribution and a compressive support boundary effect.

Conclusions:

• The developed technique enables single-atom level surface characterization in three dimensions for general nanomaterials.
• Understanding anisotropic strain and boundary effects is key to elucidating nanomaterial functionality.
• This capability will significantly advance the fundamental understanding and tailored design of nanomaterials.