Publication Date


Date of Final Oral Examination (Defense)


Type of Culminating Activity


Degree Title

Doctor of Philosophy in Materials Science and Engineering


Materials Science and Engineering

Major Advisor

Rick Ubic, Ph.D.


Darryl Butt, Ph.D.


Dmitri Tenne, Ph.D.


Geoff Brennecka, Ph.D.


Composition-structure-property relationships are essential keys to unlocking the strength of predictive crystal chemistry. Awkwardly, the electroceramics industry largely relies on various time-consuming and expensive trial-and-error experiments to address new questions which often could otherwise be interpolated from published data. Indeed, predictive models, which can be derived from empirical evidence, can greatly aid the direction and support of future development in a meaningful, apt, and cost-effective way. Theory suggests that intrinsic properties on the scale of a unit cell may be estimated from the sizes and charges of the chemical constituents alone. Ultimately, researchers could be provided a compositional recipe for any desired structure/property; or the resulting structure/property could be readily calculated based on stoichiometry. Empirical models also lend themselves to the exploration of structure/property trends which would otherwise be virtually impossible to discover via computationally expensive first-principles methods. In order to make useful empirical models, high-quality, self-consistent data are needed; however, such composition-structure-property data pools are scarce.

Chapter one describes a material classification called perovskites which have the general formula ABX3 and may constitute the largest range of functional materials of any other material classification, including: high-temperature insulators/capacitors, superconductors, dielectric resonators, pyroelectrics, piezoelectrics, ionic conductors, photovoltaics, etc. Building general processing-structure-properties models for perovskites, involving the effects of numerous structural anomalies and extrinsic effects, is far beyond the scope of this work, which focuses primarily on the effective size of A-site vacancies and the structural effects of A-site point defect chemistry, A- and B-site cation ordering, and tetragonal distortions.

Point defects, such as vacancies, can have profound effects on crystal structure and properties. Leading up to 2013, the structural effect of extrinsic vacancies has largely been ignored in electroceramics. Chapter two explores previous system-specific studies of extrinsic vacancies in perovskites in terms of the effective sizes of vacancies. The structural effects of aliovalent doping in five typical oxide perovskite systems (CaTiO3, SrTiO3, BaTiO3, PbTiO3, and Pb(Zr0.6Ti0.4)O3) were studied. Samples synthesized via solid-state reactions were checked for phase purity using X-ray diffraction, scanning electron microscopy, and either energy dispersive spectroscopy or wavelength dispersive spectroscopy. Structural parameters were characterized using X-ray, electron, and/or time-of-flight neutron diffraction data. Quantitative diffraction data were collected and refined in order to develop empirical descriptions of the effects of A-site point defect concentrations. A predictive model for pseudocubic lattice constants was developed which relates stoichiometry to the effective sizes of ionic species and vacancies. The same model can be used to calculate values of tolerance factor which better predicts perovskite stability and structure in terms of octahedral tilting than does the traditional definition.

Chapter three examines complex perovskites which contain cation ordering. Perovskites with multiple cation species sharing the A or B sites may form superstructures in which the A- and/or B-site is non-randomly shared by more than one ionic species. Ordering generally involves a lowering of symmetry; however, such ordering is not always complete or spatially extensive. It can exist in varying degrees from perfectly ordered to near completely random (i.e., disordered) and can extend uniformly throughout an entire crystal or exist only in short-range nanodomains. The model developed in chapter two is used to predict cation ordering. Several well-known examples of B-site ordered perovskites were examined and shown to result in volume shrinkage upon ordering. Furthermore, four samples in the Na(1‑3x)/2La(1+x)/2TiO3 system (x = 0.0, 0.0533, 0.1733 and 0.225) were synthesized using a conventional solid-state mixed-oxide method. The structure of the x = 0 end-member (Na0.5La0.5TiO3) has been reported in various space groups, but always with a random distribution of Na+ and La3+ on the A site; however, empirical modeling suggests that it is not only ordered but also that an unexpected volume increase accompanies the ordering process.

Chapter four proposes a predictive model for tetragonality. Samples of [(PbyBa1‑y)(1‑3x)La(2x)]TiO3 were synthesized and X-ray diffraction refinements were conducted in order to measure lattice constants. Further data from open literature were used in order to reveal tetragonality trends. These trends were combined with a model for unit‑cell volume in order to derive a generalized empirical predictive model for tetragonal lattice constants of perovskites in either P4mm or P4/mmm, based solely on published ionic radii data. This model was extended to include the resultant unit-cell dipole moment.