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Diffusivity of the Oxide Ion in Perovskite Oxides

  • J. A. Kilner
  • A. Berenov
  • J. Rossiny
Chapter
Part of the Fuel Cells and Hydrogen Energy book series (FCHY)

There are a large number of complex metal oxides, with the general formula ABO3, that form into the many and closely related perovskite-type structures. These materials show a very wide range of valuable physicochemical properties including ferromagnetism, catalytic activity, ferroelectricity, giant magneto-resistive effect, and ionic and mixed conductivity. As just mentioned, there are a number of structure types that fall under the collective name of perovskite, including the ideal cubic structure, rhombohedral, tetragonal, and orthorhombic distortions, and the hexagonal GdFeO3 types. In this chapter, we present a review of the literature on the subject of oxygen ion diffusion in these materials, as this is fundamental to an understanding of the other physical properties. This is a limited review, as the literature on this subject is extensive, and the aim here is to present the trends that will aid in understanding the changes in oxygen transport between different compositions and materials under different conditions. To this end, the structural differences in the materials will be ignored to a first approximation, and this must be remembered when the comparisons of data are being made. One of the fundamental difficulties in following the trends that occur in the diffusivity of oxygen is the inherent multidimensional nature of the data for the materials concerned. For example, the diffusivity is dependent upon temperature, oxygen partial pressure, identity of the A and B cations, the degree of substitution on either the A or B sites, and finally any deviations from the ideal A to B 1:1 occupancy ratio.

Keywords

Vacancy Concentration Vacancy Formation Tracer Diffusion Oxygen Vacancy Concentration Perovskite Material 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of MaterialsImperial CollegeLondonUK

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