Journal of Materials Science

, Volume 47, Issue 21, pp 7530–7541 | Cite as

A blend of first-principles and kinetic lattice Monte Carlo computation to optimize samarium-doped ceria

First Principles Computations

Abstract

Solid oxide fuel cells (SOFCs) have been acknowledged as a possible future source for clean and efficient electric power generation. One of the most important goals in the SOFCs research is to decrease the operating temperature, which in turn will improve the stability and decrease the cost of various components enabling its widespread utilization. For realizing the aforementioned goal, it is imperative to identify suitable electrolyte materials that show enhanced conductivity in the intermediate temperature range (773–1,073 K). Sm-doped ceria (SDC) is considered a promising candidate for use as an electrolyte material for SOFC operation in intermediate temperature range due to the high oxygen ion conductivity. In this article, we present a theoretical investigation using first-principles and kinetic lattice Monte Carlo (KLMC) computations to highlight the trends in oxygen ion conductivity as a function of dopant content and temperature in SDC. Using first-principles calculations, oxygen vacancy formation and migration were examined at first, second, and third nearest neighbor positions to a Sm ion. The activation energies for oxygen vacancy migration along various pathways in SDC computed using first-principles were used as input to the KLMC model to study vacancy mediated diffusion. SDC with 20 % mole fraction of dopant content yields the maximum conductivity, which is in very good agreement with experimentally identified compositions. Rationale for increase in conductivity as a function of increase in dopant content and subsequent decrease in conductivity at higher dopant fractions in SDC is presented. This combined methodology of first-principles and KLMC computations is a useful tool for the design and identification of various ceria-based electrolyte materials used in SOFCs.

Notes

Acknowledgements

This article is based upon the work supported by the Department of Energy under the Grant No. DE-PS02-06ER06-17. The authors gratefully acknowledge the Fulton High Performance Computing Initiative (HPCI) at the Arizona State University for the computational resources. P.P.D thanks Shahriar Anwar, Peter A. Crozier and Renu Sharma for stimulating discussions.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Materials Science & Engineering, School for Engineering of Matter, Transport & EnergyArizona State UniversityTempeUSA
  2. 2.Department of ChemistryBrookhaven National LaboratoryUptonUSA

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