A Model to Predict Percolation Threshold and Effective Conductivity of Infiltrated Electrodes for Solid Oxide Fuel Cells

Ryan Snyder, Bucknell University
Michael D. Gross, Bucknell University
Christopher L. Porter, Bucknell University
Michael J. Synodis, Bucknell University
Andrew J.L. Reszka, Bucknell University
Nguyen M. Vo, Bucknell University


We present a mechanistic modeling methodology to predict both the percolation threshold and effective conductivity of infiltrated Solid Oxide Fuel Cell (SOFC) electrodes. The model has been developed to mirror each step of the experimental fabrication process. The primary model output is the infiltrated electrode effective conductivity which provides results over a range of infiltrate loadings that are independent of the chosen electronically conducting material. The percolation threshold is utilized as a valuable output data point directly related to the effective conductivity to compare a wide range of input value choices. The predictive capability of the model is demonstrated by favorable comparison to two separate published experimental studies, one using strontium molybdate and one using La0.8Sr0.2FeO3-δ as infiltrate materials. Effective conductivities and percolation thresholds are shown for varied infiltrate particle size, pore size, and porosity with the infiltrate particle size having the largest impact on the results.