Developments in scanning transmission electron microscopy (STEM) have opened
up new possibilities for time-resolved imaging at the atomic scale. However, rapid
imaging of single atom dynamics brings with it a new set of challenges, particularly
regarding noise and the interaction between the electron beam and the specimen. This
thesis develops a set of analytical tools for capturing atomic motion and analyzing the
dynamic behaviour of materials at the atomic scale.
Machine learning is increasingly playing an important role in the analysis of electron
microscopy data. In this light, new unsupervised learning tools are developed here for
noise removal under low-dose imaging conditions and for identifying the motion of
surface atoms. The scope for real-time processing and analysis is also explored, which is
of rising importance as electron microscopy datasets grow in size and complexity.
These advances in image processing and analysis are combined with computational
modelling to uncover new chemical and physical insights into the motion of atoms
adsorbed onto surfaces. Of particular interest are systems for heterogeneous catalysis,
where the catalytic activity can depend intimately on the atomic environment. The
study of Cu atoms on a graphene oxide support reveals that the atoms undergo
anomalous diffusion as a result of spatial and energetic disorder present in the substrate.
The investigation is extended to examine the structure and stability of small Cu clusters
on graphene oxide, with atomistic modelling used to understand the significant role
played by the substrate. Finally, the analytical methods are used to study the surface
reconstruction of silicon alongside the electron beam-induced motion of adatoms on
the surface.
Taken together, these studies demonstrate the materials insights that can be obtained
with time-resolved STEM imaging, and highlight the importance of combining state-ofthe-
art imaging with computational analysis and atomistic modelling to quantitatively
characterize the behaviour of materials with atomic resolution.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement 291522–3DIMAGE, as well as from the European Union Seventh Framework Programme under Grant Agreement 312483-ESTEEM2 (Integrated Infrastructure Initiative -I3)