Local structure studies in functional materials and self-regulated learning interventions in general chemistry courses
Date
2020
Authors
Paecklar, Arnold A., author
Neilson, James R., advisor
Reynolds, Melissa M., advisor
Rhodes, Matthew G., committee member
Finke, Richard G., committee member
Menoni, Carmen S., committee member
Journal Title
Journal ISSN
Volume Title
Abstract
The first part of this dissertation is dedicated to understanding how the origin of the chemical and physical properties of functional materials is correlated to their structure. The standard approach to determining the structure of a crystalline material is to measure the average structure of regular, repeating units. However, this approach is not sufficient for more complex compounds including disorder. Hence, to fully understand the structure-property relationships of these advanced materials, identifying the local structure is crucial. This work focuses on designing approaches for optimizing the measurement of local structure data based on X-ray and neutron total scattering techniques as well as computational approaches for analyzing and understanding these data sets. The main focus lies in designing a novel system for collecting neutron total scattering data involving the controlled exposure of gasses to solid samples. Combining this setup with a Steady-State Isotopic Transient Kinetic Analysis system further enables the collection of kinetics data simultaneously with the structural data. This system was successfully used for studying and identifying the adsorption and reaction sites in porous materials such as zeolites and metal-organic frameworks. The disorder in these systems is based on the adsorbate which is a major contributor to the structure. However, there are also materials in which a single solid phase itself contains all the disorder. Some examples for disordered materials, covered in this work, are semiconducting perovskite materials with the general formula A2BX6. Computational approaches ranging from single to high-throughput Reverse Monte Carlo modeling were developed to gain more insight into anharmonicity and the interplay of the local structural features. Understanding how these specific local structural features influence desired physical properties will help guide the design of new materials covering a wide range of applications ranging from photovoltaics to biomedical devices. While the creation of such new knowledge in material science is important, we must also ensure that this knowledge is understood and transferred effectively. This effort does not only contain educating the general public but also fostering their curiosity and providing them the tools needed to learn that content knowledge. Succeeding in these endeavors is especially important during the first exposure to science courses. The second part of this dissertation focuses on the aspect of learning by looking at educational interventions in two different introductory general chemistry courses. The effectiveness of these interventions was evaluated based on data collected with paper-based, in-class surveys over the course of the semester. A multitude of self-regulated learning (SRL) measures were assessed and range from extrinsic motivation over self-efficacy to help seeking. Statistical analyses were used to identify differences between entire courses and individual sections exposed to the interventions. Additionally, the students' combined grades were also compared. Identifying the effective tools for helping students in chemistry courses is expected to have a major impact on changing the rate of failing students in such courses. This is the step needed for students to decide to become the next researchers contributing to the field with new scientific discoveries themselves.