Electron microscopy dataset for the recognition of nanoscale ordering

A unique ordering effect has been observed in functional catalytic nanoscale materials. Instead of randomly arranged binding to the catalyst surface, metal nanoparticles show spatially ordered behavior resulting in formation of geometrical patterns. Understanding of such nanoscale materials and analysis of corresponding microscopy images will never be comprehensive without appropriate reference datasets. Here we describe the first dataset of electron microscopy images comprising individual nanoparticles which undergo ordering on a surface towards the formation of geometrical patterns. The dataset developed in this study spans three levels of nanoscale organization: (i) individual nanoparticles (1–5 nm) and arrays of nanoparticles (5–20 nm), (ii) ordering effects (20–200 nm) and (iii) complex patterns (from nm to ?m scales). The described dataset for the first time provides a possibility for the development of machine learning algorithms to study the unique phenomena of nanoparticles ordering and hierarchical organization.

Reference: Sci. Data, 2020, 7, 101

DOI: 10.1038/s41597-020-0439-1

On-line version: https://www.nature.com/articles/s41597-020-0439-1