Prediction techniques research background

In order to develop efficient MXC applications, it is imperative that we understand and predict overpotentials in these systems. The complexity of MXC processes requires that researchers use many techniques to achieve this goal. The use of EIS has been extremely helpful for other fuel cell applications to measure and characterize overpotentials as such, EIS is also helpful in MXC research. Nevertheless, one must be aware that the interpretation of EIS data requires previous knowledge of the processes involved in order to build an adequate equivalent circuit. The validation of such equivalent circuits can in part occur through multiple EIS experiments in which external parameters are varied and impedances are measured as a function of these parameters. The use of alternate analytical techniques can be used also alongside EIS to build a better understanding of the electron transport processes. We are hopeful that this chapter will provide a background and guidelines suitable for MXC researchers to use EIS as part of their analytical toolbox. 

The treatment of polymer bmshes as outlined above rests on the mean-field assumption, that is, the interactions between segments of the polymer chain are described by a constant background potential. While the mean-field approach was shown to be suitable to rationalize experimental observations on the dependence of H on N for dense polymer bmshes, it fails to predict more detailed information about the internal stmcture of the bmsh, such as the segment density 4>(z) (where z denotes the spatial coordinate normal to the interface direction) that is relevant to understand properties of polymer bmshes such as chain interpenetration or interactions between the bmsh and its environment. These limitations have motivated research in more accurate analytical and simulation (often self-consistent field-based) techniques to describe the interactions and density profile within polymer bmshes. 

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