Capturing the Morphology of the Micro-Porous Layer Using a Stochastic Approach

The performance of low-temperature fuel cell technologies is strongly dependent on the effective transport properties of its porous sub-component materials. Properties such as gas diffusivity and thermal conductivity play an important yet complex role in determining whether the component will have a positive or a negative impact on the fuel cell performance. The micro-porous layer (MPL) is an intermediate component between the macro-porous gas diffusion layer (GDL) substrate and the catalyst layer. It is mainly made from carbon particles and hydrophobic agents such as PTFE. Various studies show that the MPL can mitigate catalyst layer flooding at high current densities and ensure a smooth transition between the large pores in the GDL substrate and the small ones in the catalyst layer. However, there is very limited information about MPL properties in the literature due to the complexity of measuring MPL-specific properties experimentally, considering that its delicate stricture always requires a supporting material.

Numerical simulation is one of the most promising alternatives to systematically characterize the MPL and its effective transport properties. We have previously established and validated a numerical framework for the GDL¹. In this work, we propose a stochastic method to generate the physical structure of the MPL material. The model utilizes material specifications such as porosity, size of the particles and PTFE loading as input for structure generation. The model also incorporates parameters to recreate the morphology of an actual structure, such as the clustering and the agglomeration of the particles and the location of the PTFE. This novel technique allows generating a realistic porous media that is validated against experimental data. The results show very good agreement with the measured pore size distribution of a standard MPL material sample. The validated 3D structure is then used to compute the effective transport properties² that are also in good agreement with the limited data available in the literature³. The model is subsequently applied to investigate the effect of certain parameters on the structure and thus on the effective properties. The results of the study provide some insight on how the MPL manufacturing process and particle size can be tuned for specific target properties. Overall, the stochastic modeling framework is intended to become a useful design tool for simulation and design of next generation MPL materials.

Acknowledgments:

This research was supported by Ballard Power Systems and the Natural Sciences and Engineering Research Council of Canada through an Automotive Partnership Canada (APC) grant. We highly appreciate the support form Professor Ned Djilali’s group at the University of Victoria. This research made use of computing resources provided by WestGrid and Compute/Calcul Canada.  

References:

1. M. El Hannach, and E. Kjeang, J. Electrochem. Soc., under review

2. K. J. Lange, P.-C. Sui, and N. Djilali, Commun. Comput. Phys., 14, 537–573 (2013).

3. A. Nanjundappa, A. S. Alavijeh, M. El Hannach, D. Harvey, and E. Kjeang, Electrochim. Acta 110 (2013) 349-357.