Show simple item record

dc.contributor.authorFurnival, Thomas
dc.date.accessioned2018-01-02T13:08:13Z
dc.date.available2018-01-02T13:08:13Z
dc.date.issued2017-12-20
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/270314
dc.description.abstractDevelopments 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.
dc.description.sponsorshipThe 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).
dc.language.isoen
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectelectron microscopy
dc.subjectmaterials science
dc.subjectmachine learning
dc.subjectscanning transmission electron microscopy
dc.titleSingle atom imaging with time-resolved electron microscopy
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.publisher.departmentDepartment of Materials Science & Metallurgy
dc.date.updated2017-12-29T18:32:29Z
dc.identifier.doi10.17863/CAM.17164
dc.contributor.orcidFurnival, Thomas [0000-0003-1878-5662]
dc.publisher.collegeEmmanuel
dc.type.qualificationtitlePhD in Materials Science
cam.supervisorMidgley, Paul
cam.supervisorBristowe, Paul
rioxxterms.freetoread.startdate2019-01-02


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record