Predicting Electrospun Anion Exchange Membrane Conductivity in the Presence of Carbon Dioxide
Authors: Wrubel Jacob A. , Peracchio Aldo A. , Cassenti Brice N. , Grew Kyle N. , Chiu Wilson K. S. 
Published: February 2020

Abstract

Electrospinning is a promising technique for polymeric ion exchange membrane (IEM) fabrication because it enables good control of microstructural and morphological properties. Recent work1 developed a fiber network model to predict carbonate-free electrospun IEM hydroxide conductivity based on a simulation of randomly oriented conducting fibers and a subsequent application of Kirchoff’s circuit laws to solve the equivalent resistor network. A promising application for electrospun membranes is in anion exchange membrane (AEM) fuel cells. By controlling the microstructure and morphology via electrospinning, electrospun AEMs could exhibit improved transport properties compared to AEMs fabricated using other techniques. The transport properties of these other classes of AEMs, referred to as “bulk membranes”, have also been investigated2, including the effects of carbon dioxide absorption3–5 which is a problem in AEM fuel cell operation. The missing link therefore, is to characterize the effects of carbon dioxide on electrospun AEMs.
The bulk membrane models2–5 describe ion transport in a water-saturated bulk material. On the other hand, the fiber network model describes transport in layers of randomly oriented saturated (and therefore conducting) fibers imbedded in a non-conducting hydrophobic matrix. The mapping procedure is a means of converting the layered fiber configuration into an equivalent bulk configuration based on maintaining the volume fraction of water in the process. Scaling laws are given for mapping the fiber network parameters to those of an equivalent bulk membrane. The equivalent bulk membrane yields the same conductivity as the fibrous membrane for the carbonate-free case, and is then used to assess the effects of CO­2 (i.e. carbonates) following the established procedure5.

Acknowledgements

Financial support from the Army Research Office (award number W911NF-14-1-0298) is gratefully acknowledged. The authors would also like to acknowledge the support of and discussions with Dr. Cynthia Lundgren and Dr. Deryn Chu of the U.S. Army Research Laboratory.

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