The aim of this thesis was to improve the understanding of microstructure in single crystal ferroelectrics. This was achieved through macroscopic testing of Lead Magnesium Niobate – Lead Titanate (PMN-PT) and microscopic observations of Barium Titanate (BT) single crystals. Multi-axial polarization rotation tests on PMN-PT showed a gradual increase in the change in dielectric displacement due to ferroelectric switching as the electric field is applied at increasing angles to the initial polarization direction. A relatively high remnant polarization for loading angle near to 90° suggested that PMN-PT is more polarizable in certain directions. Strains measured in two directions, parallel to the electric field and perpendicular to the electric field, showed a noticeable variation on two opposite faces of the specimen suggesting an effect of local domain configurations on macroscopic behaviour. A micromechanical model gave an insight into the switching systems operating in the crystal during the polarization rotation test. Domain structure in BT was mapped using synchrotron X-ray reflection topography. By making use of the angular separation of the diffracted reflections and specimen rocking, different domain types could be unambiguously identified, along with the relative tilts between adjacent domains. Fine needle domains (width ≈ 10μm) were successfully mapped providing a composite topograph directly comparable with optical micrograph. The domain structure was confirmed using other techniques such as piezoresponse force microscopy and atomic force microscopy/scanning electron microscopy and optical observations on the etched crystal. Results show that combined use of multiple techniques is necessary to gain a consistent interpretation of the microstructure. Finally, domain evolution in BT under compressive mechanical loading was observed in-situ using optical and X-ray diffraction techniques providing a series of images that show ferroelastic transition. The domain configurations influence the switching behaviour and constitutive models that can account for such effects need to be developed. Quantitative and qualitative data presented in this thesis can assist model development and validation.

Phylogenetic inference relies heavily on statistical models that have been extended and refined over the past years into complex hierarchical models to capture the intricacies of evolutionary processes. The wealth of information in the form of fully sequenced genomes has led to the development of methods that are used to reconstruct the gene and species evolutionary histories in greater and more accurate detail. However, genes are composed of evolutionary conserved sequence segments called domains, and domains can also be affected by duplications, losses, and bifurcations implied by gene or species evolution. This thesis proposes an extension of evolutionary models, such as duplication-loss, rate, and substitution, that have previously been used to model gene evolution, to model the domain evolution. In this thesis, I am proposing DomainDLRS: a comprehensive, hierarchical Bayesian method, based on the DLRS model by Åkerborg et al., 2009, that models domain evolution as occurring inside the gene and species tree. The method incorporates a birth-death process to model the domain duplications and losses along with a domain sequence evolution model with a relaxed molecular clock assumption. The method employs a variant of Markov Chain Monte Carlo technique called, Grouped Independence Metropolis-Hastings for the estimation of posterior distribution over domain and gene trees. By using this method, we performed analyses of Zinc-Finger and PRDM9 gene families, which provides an interesting insight of domain evolution. Finally, a synteny-aware approach for gene homology inference, called GenFamClust, is proposed that uses similarity and gene neighbourhood conservation to improve the homology inference. We evaluated the accuracy of our method on synthetic and two biological datasets consisting of Eukaryotes and Fungal species. Our results show that the use of synteny with similarity is providing a significant improvement in homology inference. / <p>QC 20160904</p>