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Ion-exchange equilibria of ammonium between an aqueous phase and Nafion 117 were measured at 10, 25, 40, and 60°C by equilibrating the membrane in 0.1 M chloride electrolytes of known cation composition. The water content in the membrane phase decreased linearly with increasing cation fraction of ammonium in the membrane phase (y NH 4 ϩ) from H 2 O ϭ 21.2 ͑moles of water per mole-sulfonic acid groups͒ in proton form Nafion in pure water to H 2 O ϭ 13.2 in ammonium form Nafion in a 0.1 M chloride solution. The conductivity was measured by ac impedance in a two-electrode setup using a stack of membranes. The conductivity also decreased linearly with increasing y NH 4 ϩ from 97 to 25 mS/cm at 25.0°C. Our results indicated that the conductivity of Nafion was isotropic, however, available literature is not conclusive on this matter. The temperature dependence of the conductivity was measured, and the fitted activation energy in an Arrhenius-type equation was found to depend on membrane composition and hence water content.

We describe first measurement in a novel thin-layer channel flow cell designed for the investigation of heterogeneous electrocatalysis on porous catalysts. For the interpretation of the measurements, a macroscopic model for coupled species transport and reaction, which can be solved numerically, is feasible. In this paper, we focus on the limiting current. We compare numerical solutions of a macroscopic model to a generalization of a Leveque-type asymptotic estimate for circular electrodes, and to measurements obtained in the aforementioned flow cell. We establish that on properly aligned meshes, the numerical method reproduces the asymptotic estimate. Furthermore, we demonstrate that the measurements are partially performed in the sub-asymptotic regime, in which the boundary layer thickness exceeds the cell height. Using the inlet concentration and the diffusion coefficient from literature, we overestimate the limiting current. On the other hand, the use of fitted parameters leads to perfect agreement between model and experiment.

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