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Development of new materials capable of conducting protons in the absence of water is crucial for improving the performance, reducing the cost, and extending the operating conditions for proton exchange membrane fuel cells. We present detailed atomistic simulations showing that graphanol (hydroxylated graphane) will conduct protons anhydrously with very low diffusion barriers. We developed a deep learning potential (DP) for graphanol that has near-density functional theory accuracy but requires a very small fraction of the computational cost. We used our DP to calculate proton self-diffusion coefficients as a function of temperature, to estimate the overall barrier to proton diffusion, and to characterize the impact of thermal fluctuations as a function of system size. We propose and test a detailed mechanism for proton conduction on the surface of graphanol. We show that protons can rapidly hop along Grotthuss chains containing several hydroxyl groups aligned such that hydrogen bonds allow for conduction of protons forward and backward along the chain without hydroxyl group rotation. Long-range proton transport only takes place as new Grotthuss chains are formed by rotation of one or more hydroxyl groups in the chain. Thus, the overall diffusion barrier consists of a convolution of the intrinsic proton hopping barrier and the intrinsic hydroxyl rotation barrier. Our results provide a set of design rules for developing new anhydrous proton conducting membranes with even lower diffusion barriers.

In the current study, we have investigated the micellization of pure gemini surfactants and a mixture of gemini and conventional surfactants using a 3D lattice Monte Carlo simulation method. For the pure gemini surfactant system, the effects of tail length on CMC and aggregation number were studied, and the simulation results were found to be in excellent agreement with the experimental results. For a mixture of gemini and conventional surfactants, variations in the mixed CMC, interaction parameter β, and excess Gibbs free energy G(E) with composition revealed synergism in micelle formation. Simulation results were compared to estimations made using regular solution theory to determine the applicability of this theory for non-ideal mixed surfactant systems. A large discrepancy was observed between the behavior of parameters such as the activity coefficients fi and the excess Gibbs free energy G(E) and the expected behavior of these parameters as predicted by regular solution theory. Therefore, we have used the modified version of regular solution theory. This three parameter model contains two parameters in addition to the interaction parameters: the size parameter, ρ, which reflects differences in the size of components, and the packing parameter, P*, which reflects nonrandom mixing in mixed micelles. The proposed model provides a good description of the behavior of gemini and conventional surfactant mixtures. The results indicated that as the chain length of gemini surfactants in mixture is increased, the size parameter remains constant while the interaction and packing parameters increase., (Copyright © 2014 Elsevier Inc. All rights reserved.)