Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 1991. / Spine title: Application of morphological pseudoconvolutions to STM images. Includes bibliographical references.

Thesis (M.S. in Combat Systems Science and Technology)--Naval Postgraduate School, December 2008. / Thesis Advisor(s): Haegel, Nancy M. ; Luscombe, James. "December 2008." Description based on title screen as viewed on January 29, 2009. Sponsoring/Monitoring Agency Report Number: "DMR-0526330." Includes bibliographical references (p. 59-61). Also available in print.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [140]-152).

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 203-215). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 177-184). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--Brigham Young University. Dept. of Chemistry and Biochemistry, 2007. / Includes bibliographical references.

Thesis (Ph. D.)--Duke University, 2007. / Includes bibliographical references.

Thesis (Ph. D.)--Mechanical Engineering, Georgia Institute of Technology, 2006. / Levent Degertekin, Committee Member ; William P. King, Committee Member ; Boris Mizaikoff, Committee Member ; Mark G. Allen, Committee Member ; Peter J. Hesketh, Committee Chair.

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.