Network analysis of proton transfer in liquid water.
نویسندگان
چکیده
Proton transfer in macromolecular systems is a fascinating yet elusive process. In the last ten years, molecular simulations have shown to be a useful tool to unveil the atomistic mechanism. Notwithstanding, the large number of degrees of freedom involved make the accurate description of the process very hard even for the case of proton diffusion in bulk water. Here, multi-state empirical valence bond molecular dynamics simulations in conjunction with complex network analysis are applied to study proton transfer in liquid water. Making use of a transition network formalism, this approach takes into account the time evolution of several coordinates simultaneously. Our results provide evidence for a strong dependence of proton transfer on the length of the hydrogen bond solvating the Zundel complex, with proton transfer enhancement as shorter bonds are formed at the acceptor site. We identify six major states (nodes) on the network leading from the "special pair" to a more symmetric Zundel complex required for transferring the proton. Moreover, the second solvation shell specifically rearranges to promote the transfer, reiterating the idea that solvation beyond the first shell of the Zundel complex plays a crucial role in the process.
منابع مشابه
The Effect of Hydrogen Bonding and π–π Stacking to Stabilization of 3D Networks of a New Proton Compound, (a-6-mpyH)(Hpyzd) H2O
A new proton transfer compound, formulated as (Hamp-6-pic)(Hpyzd) ∙H2O (1), has been synthesized from the reaction of pyrazine-2,3-dicarboxylic acid (H2pyzd) and 2-amino-6-methyl pyridine (amp-6-pic), in 1:1 molar ratio. Extensive O−H×××O, N−H×××N and O−H×××O hydrogen bonds involving (Hamp-6-pic)+ cation, (Hpyzd)- anion and co-crystal water molecule٫ static electronic٫ and π…π stacking interac...
متن کاملAn Artificial Neural Network Model for Predicting the Pressure Gradient in Horizontal Oil–Water Separated Flow
In this study, a three–layer artificial neural network (ANN) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. A total of 455 data points were collected from 13 data sources to develop the ANN model. Superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of ...
متن کاملAn Artificial Neural Network Model for Predicting the Pressure Gradient in Horizontal Oil–Water Separated Flow
In this study, a three–layer artificial neural network (ANN) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. A total of 455 data points were collected from 13 data sources to develop the ANN model. Superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of ...
متن کاملSynthesis of Sulfonated Polystyrene/acrylate–ionic Liquid (Si-SPS/A–IL) Hybrid Membranes for Methanol Fuel Cells
In this paper, the silicon-containing sulfonated polystyrene/acrylate–ionic liquid (Si-SPS/A–IL)hybrid membranes was prepared to obtain the proton exchange membrane (PEM) materials withhigh methanol barrier and good selectivity. The Si-SPS/A–IL hybrid membranes characterized asthe function of IL to evaluate their potential as PEMs in direct methanol fuel cells (DMFCs).Fourdifferent Hybrid mater...
متن کاملThe Impact of Wettability on Effective Properties of Cathode Catalyst Layer in a Proton Exchange Membrane Fuel Cell
The produced liquid water in cathode catalyst layer (CCL) has significant effect on the operation of proton exchange membrane fuel cell (PEMFC). To investigate this effect, the transport of oxygen in CCL in the presence of immiscible liquid water is studied applying a two-dimensional pore scale model. The CCL was reconstructed as an agglomerated system. To explore the wettability effects, diffe...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- The Journal of chemical physics
دوره 140 24 شماره
صفحات -
تاریخ انتشار 2014