Publication Date
5-2025
Date of Final Oral Examination (Defense)
2-24-2025
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Materials Science and Engineering
Department
Materials Science and Engineering
Supervisory Committee Chair
Rick Ubic, Ph.D.
Supervisory Committee Member
Lan Li, Ph.D.
Supervisory Committee Member
Kevin Tolman, Ph.D.
Abstract
Perovskites are a class of materials famous for their many functional properties and are envisioned as a material that will pave the way for scientific advancement in the fields of renewable energy, high tech sensors, lasers, catalysis, computer chips, and many others. This wide variety of applications is not the result of a single fantastic property of perovskite materials but rather stems from the perovskite structure’s ability to display a host of functional properties. One area where there is room for significant improvement is in the rate of perovskite discovery. The high stability of the perovskite structure is responsible for the many properties that it possesses, but it also greatly increases the compositional space that these properties can exist within which can make finding the best composition for a specific application both an exciting challenge and opportunity for further innovation. It is for this reason that a comprehensive understanding of the structure-processing-property relationship within perovskite materials could be a transformative breakthrough for a litany of high tech fields.
Chapter One provides an overview of the key properties held by perovskites, the atomic structures that produce those properties, the processing techniques that are used to create those structures, as well as some promising methods for accelerating the discovery of new perovskite materials. Although other methods are briefly discussed, the primary focus of this work is on the development of empirical modeling tools that can predict the properties of a perovskite composition using widely known or easy to measure inputs. Historically, the research on new perovskite compositions has heavily focused on optimizing a few compositional ranges where properties are well explored and generally understood while completely new compositions are avoided because the risks of spending limited resources on a “blue skies” research area are generally too high. Predictive tools such as the empirical modeling tools developed in this work have the potential to lower the barriers associated with researching unexplored compositions by providing clues as to which ones are likely to hold the desired properties.
Chapter Two is focused on the development of an empirical model that accounts for the effects of 1:2 B-site ordering in perovskites and can both predict the pseudocubic lattice constant of these materials and assist in determining the degree of ordering present in experimental samples. This model uses only the Shannon ionic radii values as inputs, which in turn means that this model can be used knowing only the charge and the coordination of the ions in any perovskite composition. Additionally, to supplement the limited data for this ordering type, three compositions in the BSMT system were synthesized using conventional solid-state mixed-oxide synthesis techniques.
Chapter Three refines previously developed models by correcting several errors that have persisted through the development of several models and then compares the efficacy of these models on a data set in which several minor errors have been corrected. Like most scientific progress, the development of empirical modeling techniques has been gradual and has been built upon the work that came before it. In this case, several minor errors and inconsistencies in the data set used in one of the founding empirical models for perovskites have been propagated through many models that have built upon that work. The correction of these errors has not only improved the predictive accuracy of existing models but also created a platform for the comparison and analysis of these models.
DOI
10.18122/td.2387.boisestate
Recommended Citation
Wright, Bryan, "Empirical Models for the Prediction of Lattice Constants in Perovskite Materials" (2025). Boise State University Theses and Dissertations. 2387.
10.18122/td.2387.boisestate