(92e) Understanding Anion Exchange Membrane Performance in Fuel Cells and Their Shortfalls from Practical Standpoint
AIChE Annual Meeting
2016
2016 AIChE Annual Meeting
Transport and Energy Processes
Membrane and Direct Methanol Fuel Cells
Monday, November 14, 2016 - 9:30am to 9:45am
We have explored block and random copolymer morphologies for maximizing hydroxide conductivities and mechanical strength in this work. Polyethylene and Poly(phenylene oxide) based block copolymer AEMs were characterized for their ion transport properties and presence of different water environments. PPO based block copolymers have shown > 100 mS/cm hydroxide conductivity and increased stability which is very promising for AEMFC applications. SAXS, FTIR and diffusion NMR were used to understand membrane morphology and correlate different types of water environments present in the AEM.
Scale up of these synthetic polymers has been an issue faced by many AEMFC researchers for fuel cell performance testing. In this work we have taken an approach of synthesizing commercially available polymers to make 10â??s of grams of polymer batches which will be sufficient for studying fuel cell performance. We have decided to work with commercially available polyphenelyne based polymer namely poly(2,6-dimethyl-1,4-phenylene oxide). We have functionalized this polymer with quaternary ammonium cations by adopting a chlorination chemistry followed by solvent casting and melt pressing to form a strong AEM. This AEM was used for fuel cell performance testing. Fuel cell performance testing was done on this AEM using H2/O2 with Pt/C catalyst at 60°C and 80°C and currently we are exploring various AEM casting and post synthesis modification techniques for maximizing the fuel cell performance. We think that these post synthesis modifications are critical for optimizing the physical and chemical microenvironments in the AEMs.
Overall this talk will focus on understanding ion transport in novel AEMs and their characterization, followed by fuel cell performance testing and optimization.
Acknowledgements:
We would like to thank the US Army Research Office for funding this Multidisciplinary University Research Initiative under contract #W911NF-10-1-0520