Abstract
Conductive electrochemical AFM images demonstrating the complex nature and structure of Nafion surface conductivity are presented. Nanoscale regions with high currents determining the overall total membrane current can be distinguished from majority domains with lower currents and non-conductive areas. The different conductive domains form ordered structures and show a specific dynamic behaviour. These observations were compared to the structural and electrical models in the literature. None of the models is able to explain all aspects of the current images. The existence of inverted micelles seems to be quite probable since the formation of agglomerates like chains and larger ordered clusters is clearly visible. This aspect is best described by the model of Schmidt-Rohr and Chen. In addition, the highly dynamic behaviour and distribution of conductive channels of Nafion leading to the formation of new current pathways also indicates the formation of different meso-phases with a high local fluctuation rate. The other discussed models also predict structural features which are in agreement with our observations like the formation of super-structures and agglomeration of fibers. The structural characterisation reflects the situation at or near the membrane surface and might differ from the bulk structure since the surface energy may have a large influence on the formation of structures during the membrane solidification process. The quite large dynamics of conductivity changes of Nafion reflected in the formation of new current pathways even at room temperature leads to the assumption that the internal structure of Nafion is subject to significant changes due to humidity and temperature variations. The local variation of individual structures may reflect the variation of concentration of hydrophilic and hydrophobic groups during membrane solidification. The minimization of surface free energy during a self-assembling process is essential for the formation of different phases and subsequent structures like chains, etc. as well as higher order clustering.
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