A Pore Network Modelling Study of Fuel Cell Gas Diffusion Layers with Patterned Wettability

Sunday, 1 October 2017: 15:00
Maryland A (Gaylord National Resort and Convention Center)
A. Forner-Cuenca (Electrochemistry Laboratory, Paul Scherrer Institute), H. Brandvold (Electrochemistry Laboratory, Paul Scherrer Institut), M. A. Hoeh (Forschungszentrum Jülich GmbH), V. Manzi-Orezzoli (Paul Scherrer Institute), T. J. Schmidt (Laboratory of Physical Chemistry, ETH Zürich), and P. Boillat (LEC, Paul Scherrer Institute)
Further improvements in power density of polymer electrolyte fuel cells (PEFCs) are required to lower the cost. At high current densities, mass transport related resistances limit the performance due to, mainly, a poor water management within the cell. It is therefore paramount to develop advanced water management strategies to maximize power output. In this context, diffusion layers play a notable role. They must ensure efficient gas transport toward the catalyst layer while removing the electrochemically produced liquid water in the opposite flow direction. A substantial amount of work has been carried out to improve gas diffusion layers (GDLs) and microporous layer over the last years, mainly controlling microstructural features, such as pore sizes, fiber arrangements and porosities, and hydrophobic coating load and distribution [1].

Our group has recently developed an approach consisting of artificially creating hydrophilic patterns throughout the entire GDL thickness using the radiation grafting method. The hydrophilic areas act as low pressure liquid water removal pathways, therefore liberating the remaining areas for the gas to flow throughout less tortuous domains [2]. The performance of PEFCs was significantly increased thanks to the use of the modified GDLs [3]. However, there is still plenty of room for improvement. Capillary pressure is the driving force for liquid water transport and it is influenced by the microstructure and liquid-solid interaction [4]. It therefore stands to reason that, for a given application, further optimization can be achieved when selecting the appropriate diffusion layer microstructure and wettability ratio. While the first experimental data investigating the effect of GDL morphology, contact angle and coating load has been recently published, these experiments require notable infrastructure and time efforts [5]. For that reason, modelling capillary pressure experiments using pore network models can be a valid approach to theoretically assess optimal material parameters. The open-source OpenPNM framework, which has been extensively used in recent fuel cell literature [6], was the software of choice for this investigation.

In this talk we will start by presenting the model physics in which a modification of the Washburn equation has been used as required in the intermediate wettability range where this model fails to predict accurately water imbibition and the validation strategy. The definition of coating load and contact angle will be discussed. A good agreement was obtained between model and experiments, specially investigating the influence of coating load, contact angle and pore size distributions. In the second part of the talk, we will provide some material design guidelines based on model output information to maximize the quality of water segregation in GDLs with patterned wettability.


[1] S. Park, J.-W. Lee, B. N. Popov, Int. J. Hydrogen Energy 37(7), 5850 (2012).

[2] A. Forner-Cuenca, J. Biesdorf, L. Gubler, P. M. Kristiansen, T. J. Schmidt, P. Boillat, Adv. Mater. 27, 6317 (2015).

[3] A. Forner-Cuenca, J. Biesdorf, V. Manzi-Orezzoli, L. Gubler, T. J. Schmidt, P. Boillat, J. Electrochem. Soc. 163(13), F1389 (2016).

[4] U. Pasaogullari, C. Y. Wang, J. Electrochem. Soc. 153(3) A399 (2004).

[5] A. Forner-Cuenca, J. Biesdorf, A. Lamibrac, V. Manzi-Orezzoli, F. N. Büchi L. Gubler, T. J. Schmidt, P. Boillat, J. Electrochem. Soc. 163(9), F1038 (2016).

[6] J. Gostick, M. Aghighi, J. Hinebaugh, T. Transter, M. A. Hoeh, H. Day, B. Spellacy, M. Eisharqawy, A. Bazylak, A. Burns, W. Lehnert, A. Putz, Comput. Sci. Eng. 18(4), 60 (2016).