Understanding the ionic conductivity maximum in doped ceria: trapping and blocking
Increasing utilization of renewable energy sources like wind and solar power highlights the importance of energy conversion and storage. Solid oxide electrolysis cells (SOEC) - converting electricity from renewable energy sources to hydrogen - and solid oxide fuel cells (SOFC) - converting stored hydrogen or hydrogen containing compounds again to electricity - provide high energy conversion efficiency and excellent fuel flexibility and are therefore a promising candidate for future energy applications. For a good performance of SOECs as well as SOFCs, an electrolyte is required, which has a high oxygen ion conductivity and a low electronic conductivity. Potential candidates are fluorite-structured oxides such as doped zirconia (ZrO2) and doped ceria (CeO2). Doped ceria allows a reduction in the operating temperature from about 900 C to about 600 C, and is, therefore, the main focus of the research of the research group headed by JARA-ENERGY member Prof. Manfred Martin. In doing so, these researches provide a contribution to the study of sustainable and efficient energy storages.
In our high-profile PCCP Perspective article “Understanding the ionic conductivity maximum in doped ceria: trapping and blocking” (https://dx.doi.org/10.1039/C7CP08535D), we show how defects in solids influence the ionic conductivity. Our PCCP front cover picture in Fig. 1 shows schematically how doping ceria with lower valent dopant ions (blue spheres) creates oxygen vacancies (red cubes) that increase the ionic conductivity.
Figure 1 also shows that diffusing oxygen vacancies may be caught by dopants. Further jumps of the oxygen vacancies are hindered and the ionic conductivity is reduced. This two-step trapping process (catch-and-hold) varies with each dopant. The more frequently dopants catch and hold oxygen vacancies, the lower the ionic conductivity. The catch-and-hold principle of trapping thus determines which material has the highest ionic conductivity. Additionally, oxygen vacancies cannot escape through the gaps between tentacles of the dopants. Jumps of oxygen vacancies through these gaps are blocked by the tentacles. If two dopants are next to each other, oxygen vacancies that are caught rarely escape. The creation of oxygen vacancies by doping competes with the blocking of the tentacles of the dopants. Thus, blocking determines the concentration of dopants, leading to the highest ionic conductivity. In summary, our article not only explains which fraction of dopant ions is the best. We also demonstrate which dopant leads to the highest possible ionic conductivity. This knowledge is crucial for the development of new materials for catalysis and energy conversion.
A quantitative prediction of the oxygen ion conductivity as a function of the doping fraction is possible by combining ab initio density functional theory (DFT) with Kinetic Monte Carlo (KMC) simulations. Migration barriers are analyzed for energy contributions, which are caused by the interactions of dopants and vacancies with the migrating oxygen vacancy. We clearly distinguish between energy contributions that are either uniform for forward and backward jumps or favor one migration direction over the reverse direction. If the presence of a dopant changes the migration energy identically for forward and backward jumps, the resulting energy contribution results in blocking as shown schematically in Fig. 1. If the change in migration energy due to doping is different for forward and backward jumps of a specific ionic configuration, the resulting energy contributions corresponds to trapping as shown schematically in Fig. 1. The influence of both effects on the ionic conductivity is analyzed: blocking determines the dopant fraction where the ionic conductivity exhibits the maximum. Trapping limits the maximum ionic conductivity value. In this way, a deeper understanding of the underlying mechanisms determining the influence of dopants on the ionic conductivity is obtained and the ionic conductivity is predicted more accurately, as shown in Fig. 2. The detailed results and insights obtained here for doped ceria can be generalized and applied to other ion conductors that are important for SOFCs and SOECs as well as solid state batteries.
For more information:
J. Koettgen, S. Grieshammer, P. Hein, B.O.H. Grope, M. Nakayama, and M. Martin, Understanding the ionic conductivity maximum in doped ceria: trapping and blocking, Physical Chemistry Chemical Physics, 2018, DOI: 10.1039/C7CP08535D, https://dx.doi.org/10.1039/C7CP08535D