Effect of Membrane Properties on the Carbonation of Anion Exchange Membrane Fuel Cells

Abstract

Anion exchange membrane fuel cells (AEMFC) are potentially very low-cost replacements for proton exchange membrane fuel cells. However, AEMFCs suffer from one very serious drawback: significant performance loss when CO2 is present in the reacting oxidant gas (e.g., air) due to carbonation. Although the chemical mechanisms for how carbonation leads to voltage loss in operating AEMFCs are known, the way those mechanisms are affected by the properties of the anion exchange membrane (AEM) has not been elucidated. Therefore, this work studies AEMFC carbonation using numerous high-functioning AEMs from the literature and it was found that the ionic conductivity of the AEM plays the most critical role in the CO2-related voltage loss from carbonation, with the degree of AEM crystallinity playing a minor role. In short, higher conductivity—resulting either from a reduction in the membrane thickness or a change in the polymer chemistry—results in faster CO2 migration and emission from the anode side. Although this does lead to a lower overall degree of carbonation in the polymer, it also increases CO2-related voltage loss. Additionally, an operando neutron imaging cell is used to show that as AEMFCs become increasingly carbonated their water content is reduced, which further drives down cell performance.

Series-Connected Tetracation Partially Cross-Linked Anion Exchange Membranes: Insight towards Consequences of Alkyl Spacer Length

“…The thickness of membrane is of great importance in performance determination. It is well-established that membranes with less thickness showed good performance by reducing the ohmic resistance and increasing the water diffusivity in electromembrane processes compared to thick membranes . In particular, AEM-Q1 and AEM-Q5-C10 membranes have a marginally higher thickness (≤8%) than the rest four membranes; however, water content and fixed QAC concentration per cubic centimeter of membrane are the performance determining parameters, and values calculations use either thickness or weight of the membrane sample, which normalizes the thickness difference in the results.…”

The impact of carbonation on hydroxide diffusion in nano-confined anion exchange membranes

“…13–15 A second major challenge, which has received less attention thus far, concerns the carbonation process that occurs when AEMFCs operate with ambient (CO 2 containing) air. 16 It has recently been shown that when the system is exposed to ambient air, the hydroxide ion conductivity in AEMs decreases significantly because of the carbonation reactions between OH − and CO 2 : 17–23 OH − + CO 2 ⇆ HCO 3 − OH − + HCO 3 − ⇆ CO 3 2− + H 2 O…”

Effect of the Agglomerate Geometry on the Effective Electrical Conductivity of a Porous Electrode

“…The surface fraction is the ratio between ones and zeros in the matrix. There are different techniques for the generation of synthetic images based on mathematical descriptors [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. The technique used in this work is based on the union of points called vertices, for the formation of the PSA.…”

“…Among the applications using synthetic images are the development of renewable energy such as synthesis of materials and prediction of behaviors for fuel cells [ 9 ], devices and apps for medicine (magnetic resonance imaging) [ 11 ], neural networks mainly with the use of Deep Learning [ 12 ], materials for ultra-fast devices in the telecommunications area (ultra-fast devices) [ 13 , 14 ], military applications such as radars and ship detection simulators [ 15 ], and topographical images of polymer solar cells [ 16 ]. There are other works involved in the improvement of microstructures related to comparison of different morphologies on 3D reconstructions [ 17 ], the behavior of their geometry to conversion of triangular to hexagonal models [ 18 ], synthesis of palladium nanoparticles in triangular form [ 19 ], Finite Volume Method (FVM) for morphology studies of microstructures with mechanoluminescent particles [ 20 ], heat and humidity transfer in clothing sets, using the finite volume method for the nonlinear parabolic equations system [ 21 ], computational thermal conductivity and membrane pore geometry simulation in porous materials [ 22 , 23 ], tortuosity, permeability and threshold percolation studies from membrane SEM images and transport pore structure [ 24 , 25 , 26 ], images generation from mathematical descriptors for 3D shapes analysis using formal segmentation [ 27 ], structural detail analysis of woven fabric based on synthetic images [ 28 ], thermal expansion coefficients calculation for one and two phases from SEM models and three-dimensional synthetic images of polycrystals [ 29 ], geometric and topological characterizations to establish a relationship of the structure owned by two phases using the Voronoi diagram in geometry of synthetic images [ 30 , 31 ], neutron imaging in fuel cells research [ 32 ], and a systematic classification implemented by its geometric and topological properties focus on imitating morphology through mathematical tools, such as digital image correlation, tessellation, random field generation, and differential equation solvers [ 33 ]. Finally, synthetic anisotropic training is performed to reconstruct anisotropic media [ 34 ] and multiscale model-based on synthetic structures, using isotropic filtering [ 35 ].…”