Files in this item

FilesDescriptionFormat

application/pdf

application/pdfKIM-DISSERTATION-2020.pdf (10MB)
(no description provided)PDF

Description

Title:Atomistic modeling of mixed ion/electron conductors exhibiting configurational disorder: Application to iron-substituted strontium titanate
Author(s):Kim, Namhoon
Director of Research:Ertekin, Elif
Doctoral Committee Chair(s):Sofronis, Petros
Doctoral Committee Member(s):Aluru, Narayana R; Perry, Nicola H
Department / Program:Mechanical Sci & Engineering
Discipline:Mechanical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):MIEC
perovskite
STF
strontium titanate
DFT
cluster expansion
diffusivity
conductivity
Abstract:In instances where materials exhibit substantial disorder, obtaining accurate, quantitative descriptions of functional properties from modeling and simulation remains a longstanding challenge. While computational methods are more well-established for ordered, crystalline materials, it is not generally straightforward to apply these approaches in the presence of configurational disorder. Nevertheless, many emerging materials across various application areas spanning energy conversion, sensing, and separation exhibit disorder. Historically, the challenges in accounting for disorder explicitly have limited the utility of computational modeling and simulation in optimizing target properties and functionality of the materials. A technologically important class of disordered material systems are the perovskite-derived mixed ion-electron conductors (MIECs). The perovskite-derivative MIECs are of interest in a wide range of applications due to their ability to conduct both ions and electrons. A classic example is iron-substituted strontium titanate (SrTi1-xFexO3-d, STF) which is a promising cathode material in solid oxide fuel cells. The introduction of large degrees of elemental substitution is a material design strategy for tuning electrochemical characteristics of host materials to optimize target properties such as conductivity. Accelerated computational development of MIECs requires accounting for how properties vary with composition and configurational disorder. However, traditional methods for modeling the properties of such materials often neglect the configurational complexity. Instead, they rely on simple estimates of diffusion barriers and prefactors and rarely account for atomic-scale configuration. Such approaches may not capture effects such as disorder, short-ranged order, and their influence on transport, necessary to optimize properties over an expanded composition space. The work presented in this thesis is concerned with advancing the state-of-the-art in the computational modeling and simulation of disordered materials, as it pertains to MIECs. The focus is on the STF material system. In this work, a bottom-up, successive scheme is demonstrated that links first-principles density functional theory (DFT), cluster expansion (CE), and direct solution approaches to the master diffusion equations to obtain quantitative predictions of electronic structures, ordering tendencies, and ion transport of STF across the whole composition space. DFT is used to elucidate the electronic structure of the STF solid solution, which gives insights into electronic and optical properties. A novel approach to the multisublattice CE is demonstrated to accurately capture the ordering tendencies and configurational energies of STF in arbitrary configurations, providing insights into thermochemistry and short-range order, particularly amongst Fe species and oxygen vacancies. The improved CE methodology accounts for the chemical identity of species distributed among multiple sublattices, including cation and anion lattices. This formalism enables one to distinguish physically meaningful interactions that would otherwise be rendered indistinguishable in the conventional CE because the conventional approach compresses chemical identity via a product of site occupations. Using the model developed, it is possible to extract thermochemical properties of STF, such as its stability, and identify tendencies for superlattice formation at certain compositions. Towards the goal of quantitative prediction of experimentally measurable properties, the CE is used in conjunction with Monte Carlo simulations to generate ensembles, and an additional local CE evaluates kinetically resolved activation barriers for oxygen hopping. The oxygen diffusivity is obtained from the steady-state solution to the master diffusion equations, which efficiently handles correlations due to a big difference in hopping rates in disordered materials. The use of the direct solution approach reveals many intriguing aspects of oxygen ion diffusivity in STF. For instance, the diffusion of oxygen ions emerges from many competing factors, including oxygen vacancy concentration, short-range order and other configurational tendencies, blocking sites, and percolation of diffusion networks. Short-range order creates traps for oxygen ions and hinders its mobility. On the other hand, with large Fe incorporation, fast ion diffusion is enabled results from the percolation of the diffusion network through the orderings. The competition between the trapping and the percolation gives rises to intriguing non-monotonic trends in how the diffusion coefficient depends on STF composition. The method introduced here is shown to reproduce the measured oxygen ionic conductivity of STF, obtaining diffusion coefficients within an order of magnitude of experiments across all compositions. The work presented here may be useful both as a specific methodology to determine the ion transport properties of complex oxide mixtures, as well as for opening pathways to higher fidelity models of complex, disordered structures that can be used in computational materials design and optimization.
Issue Date:2020-12-03
Type:Thesis
URI:http://hdl.handle.net/2142/109414
Rights Information:Copyright 2020 Namhoon Kim
Date Available in IDEALS:2021-03-05
Date Deposited:2020-12


This item appears in the following Collection(s)

Item Statistics