Fuel Cells: Optimizing Grain Boundaries in BaZrO3 and Modeling Proton Conduction Using Kinetic Monte Carlo
MetadataShow full item record
Perovskite oxides have attracted much interest due to their possible use in proton conducting fuel cells. Today fuel cells have the potential to solve some of our nations most pressing problems, such as dependence on foreign oil, poor air quality, and greenhouse gas emissions that contribute to global warming. One of the critical limitations to proton conduction in fuel cells are defects in the conducting material. My research objective is to lay the foundation for proton conduction studies through grain boundaries by characterizing the structure of certain grain boundaries and developing a method that can model proton conduction through large systems containing grain boundaries. I focus on two types of defects, namely the O terminated (311) and Zr terminated (310) tilt grain boundaries in BaZrO3. The specific grain boundary choices are motivated by earlier work which found the O terminated (311) surface to be of lower energy than others and one study on (310) tilt grain boundaries in BaZrO3. The latter is the only calculation to date, which considered a specific grain boundary in BaZrO3. Characterization of grain boundaries experimentally is very difficult and no studies have shown a successful characterization for grain boundaries in BaZrO3. Molecular dynamics on two approximate potential energy surfaces was used to sample a range of possible O terminated (311) and Zr terminated (310) tilt grain boundary configurations. Each sample point was optimized and the lowest energy optimized structure was used to represent the relaxed grain boundary. The lowest energy grain boundary will be the basis for future work on proton conduction pathways through grain boundaries. Since realistic grain boundary model systems are larger than model sys- tems containing defects, I have also developed a new method to accelerate the simulation of proton conduction paths. The method uses graph theory path- ways for small regions of the system to make multi-step moves in a traditional kinetic Monte Carlo (kMC) method. The net result is a longer-range mo- tion for each step in the kMC dynamics. This method is tested on a BaZrO3 perovskite system without grain boundaries where we have already found the proton conduction pathways.