Pore-scale modeling has become a quite popular tool for evaluating the impact of material structure on fuel cell performance. However, the computational complexity of these models often limits simulations to analyze only a small volume of material, which is typically selected randomly from a much larger microstructure dataset. When considering the heterogeneous internal structure of fuel cell materials, it is highly unlikely that such a randomly selected volume (i.e., model domain) would adequately reflect the salient features of the material structure. The objective of this work is to utilize the recent advances in microstructure quantification to select small representative volume elements (RVEs) that accurately reflect the overall microstructure and transport properties of fuel cell materials. The microporous layer (MPL) in polymer electrolyte fuel cells is chosen for initial demonstration of the approach. Dual-beam focused ion beam scanning electron microscopy is utilized to obtain a 3-D structural dataset of the selected MPL sample. The RVEs are selected using the new approach of weighted sets of optimally selected statistical volume elements, and the key structure and transport metrics are evaluated using advanced microstructure algorithms developed in-house. Metric comparisons between the RVEs and the full dataset indicate that the RVEs selected by this approach offer a very good representation of the full dataset, albeit in a volume that is significantly smaller in spatial extent, therefore providing a computationally efficient and reliable model domain for pore-scale analyses. (C) 2011 Elsevier B.V. All rights reserved.