(167e) Study of Proton Transport Using Reactive Molecular Dynamics | AIChE

(167e) Study of Proton Transport Using Reactive Molecular Dynamics


Esai Selvan, M. - Presenter, University of Tennessee
Keffer, D. J. - Presenter, University of Tennessee, Knoxville
Cui, S. - Presenter, University of Tennessee

The proton transport occurs through a combination of vehicular diffusion - movement of the center of mass of hydronium ions and structural diffusion - transfer of protons among water molecules. We have developed a coarse-grained reactive molecular dynamics (RMD) algorithm to model the structural diffusion of proton using the classical molecular dynamics simulation. The major advantage of this approach is that we use computationally efficient, well established non-reactive potentials that already exists and has been optimized. The algorithm implements the structural diffusion via three steps (i) satisfaction of the triggers, (ii) instantaneous reaction and (iii) local equilibration. A set of triggers were chosen based on the transition state obtained from ab initio calculation and activation energy to check for the favorable configuration for the structural diffusion of proton to take place. These trigger values were parameterized to fit the experimental values of the rate constant in order to capture the essential molecular and macroscopic features of structural diffusion. The final step is to satisfy the heat of reaction and ensure a correct ending configuration after the proton hopping.

We validated this algorithm by modeling the structural diffusion of proton in extensively studied system like bulk water. A single hydrated proton was studied in a system of 650 water molecules as a function of temperature from 280 K to 320 K. The triggers were chosen based on the structure of the dominant hydrated models (Zundel and Eigen cations) necessary for the structural diffusion of protons and were parameterized to reproduce the experimental rate constant and activation energy. Using this procedure, an in depth study of the correlation and contribution of the two components of the diffusion to the charge diffusion was conducted. The algorithm is generalized by the environment sensitive geometric and energetic trigger. The RMD endeavors to have a valid starting and ending configuration for the structural diffusion. Information regarding the transition state is embedded in the triggers, but the algorithm makes no attempt to dynamically describe the structure of the transition state. Therefore, functional extrapolation of structural diffusion in one environment (bulk water) to another environment (hydrated proton exchange membrane (PEM)) is enabled by the additional degree of coarse graining. Recognition and adaptability of the environment by the algorithm can be proved by studying proton transport in hydrochloric acid at different concentrations with the trigger values parameterized for infinite dilution.

The next step is to model proton transport in hydrated PEM. The structural diffusion of proton in hydrated membrane can be explained by the following three reactions.




Equation 1 is similar to proton transport in bulk water and will take place along the center of the water channels and equation 2 and 3 will occur at the interface of the hydrophobic and hydrophilic regions. RMD algorithm can accommodate all the above reactions and the triggers can register the presence of the sulfonic acid groups and other environmental factors like confinement. Thus the algorithm will allow us to measure the diffusivities in the hydrated membranes and provide a molecular level understanding of how features of the membrane such as enhanced confinement and hydration impact proton mobility.


The work is supported by a grant from the U. S. Department of Energy BES under the contract number DE-FG02-05ER15723. This research used resources of the Center for Computational Sciences at Oak Ridge National Laboratory, which is supported by the Office of Science of the DOE under Contract DE-AC05-00OR22725.  This work also used resources of the National Institute for Computational Sciences (NICS), supported by NSF with agreement number: OCI 07-11134.