Impact of distributions and mixtures on the charge transfer properties of graphene nanoflakes

Many of the promising new applications of graphene nanoflakes are moderated by charge transfer reactions occurring between defects, such as edges, and the surrounding environment. In this context the sign and value of properties such as the ionization potential, electron affinity, electronegativity and chemical hardness can be useful indicators of the efficiency of graphene nanoflakes for different reactions, and can help identify new application areas. However, as samples of graphene nanoflakes cannot necessarily be perfectly monodispersed, it is necessary to predict these properties for polydispersed ensembles of flakes, and provide a statistical solution. In this study we use some simple statistical methods, in combination with electronic structure simulations, to predict the charge transfer properties of different types of ensembles where restrictions have been placed on the diversity of the structures. By predicting quality factors for a variety of cases, we find that there is a clear motivation for restricting the sizes and suppressing certain morphologies to increase the selectivity and efficiency of charge transfer reactions; even if samples cannot be completely purified.