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TECHNICAL PAPERS

# Effects of Mass Transport on the Performance of Solid Oxide Fuel Cells Composite Electrodes

[+] Author and Article Information
Marco Cannarozzo, Simone Grosso, Adriana Del Borghi, Paola Costamagna

DICHEP, University of Genoa, Italy

Gerry Agnew

Rolls-Royce Fuel Cell Systems Ltd., Derby, UK

J. Fuel Cell Sci. Technol 4(1), 99-106 (Apr 03, 2006) (8 pages) doi:10.1115/1.2393311 History: Received December 23, 2005; Revised April 03, 2006

## Abstract

Composite electrodes are of great interest in the field of solid oxide fuel cells because their use can improve the performance of these cells. However, an important correlation exists between composition, microstructure, and thickness of an electrode and its performance. This correlation has been investigated in this work using a theoretical model. The model, in order to consider all the losses occurring in an electrode, includes Ohm’s law for ionic and electronic charge transport, and the Butler-Volmer equation to evaluate the activation polarizations, and mass transport equations, taking into account diffusion through porous media, to evaluate the concentration losses. The model shows that the best electrode performance is a trade-off between activation and concentration losses. This is because a decrease in the dimensions of the particles or an increase in its thickness result, on the one hand, in a reduction of the activation polarizations, because of a larger active area for the electrochemical reaction, and, on the other hand, in an increase in the concentration losses due to a more difficult gas diffusion. In particular, in order to understand the impact of concentration losses on the performance of composite electrodes, the simulations have been run with two models, one including and the other one neglecting the mass transport equations. The results show that concentration losses play a role only with thick electrodes composed of small particles, operating at high fuel utilization.

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## Figures

Figure 1

Outline of a composite SOFC anode

Figure 2

Comparison of the two models for the currents along x coordinate (φ=0.5, P=1, T=1173K, p=1atm, pH2O0=0.6atm, I=5000A∕m2, rel=0.05μm, t=150μm)

Figure 3

Comparison of the two models when varying the particles dimension (from above: rel=0.2, 0.1 and 0.05μm). Other data: φ=0.5, P=1, T=1173K, p=1atm, pH2O0=0.6atm, I=5000A∕m2, t=150μm

Figure 4

Diffusivity when varying the particles dimension (T=1173K, p=1atm)

Figure 5

Currents through the electrode for a composition near the electronic percolation limit (φ=0.2945, P=1, T=1173K, p=1atm, pH2O0=0.6atm, I=5000A∕m2, rel=0.05μm, t=150μm)

Figure 6

Overpotential throughout the electrode, for a composition near the electronic percolation limit (φ=0.2945, P=1, T=1173K, p=1atm, pH2O0=0.6atm, I=5000A∕m2, rel=0.05μm, t=150μm)

Figure 7

Comparison of the two models for the total overpotential trend when varying the particles dimension (φ=0.4, P=1, T=1173K, p=1atm, pH2O0=0.7atm, I=5000A∕m2, t=150μm)

Figure 8

Comparison of the two models for the total overpotential trend when varying the thickness (φ=0.4, P=1, T=1173K, p=1atm, pH2O0=0.7atm, I=5000A∕m2, rel=0.05μm)

Figure 9

Overpotential when varying thickness and particles dimension (φ=0.4, P=1, T=1173K, p=1atmpH2O0=0.7atm, I=5000A∕m2)

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