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Updated: May 7, 2025

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Published on: July 19, 2019
Water-Mediated Proton Hopping Mechanisms at the SnO2(110)/H2O Interface from Ab Initio Deep Potential Molecular
Mei Jia1,2, Yong-Bin Zhuang2, Feng Wang2
1Henan Key Laboratory of Biomolecular Recognition and Sensing, Henan Joint International Research Laboratory of Chemo/Biosensing and Early Diagnosis of Major Diseases, College of Chemistry and Chemical Engineering, Shangqiu Normal University, Shangqiu 476000, China.
Abstract:
The interfacial proton transfer (PT) reaction on the metal oxide surface is an important step in many chemical processes including photoelectrocatalytic water splitting, dehydrogenation, and hydrogen storage. The investigation of the PT process, in terms of thermodynamics and kinetics, has received considerable attention, but the individual free energy barriers and solvent effects for different PT pathways on rutile oxide are still lacking. Here, by applying a combination of ab initio and deep potential molecular dynamics methods, we have studied interfacial PT mechanisms by selecting the rutile SnO2(110)/H2O interface as an example of an oxide with the characteristic of frequently interfacial PT processes. Three types of PT pathways among the interfacial groups are found, i.e., proton transfer from terminal adsorbed water to bridge oxygen directly (surface-PT) or via a solvent water (mediated-PT), and proton hopping between two terminal groups (adlayer PT). Our simulations reveal that the terminal water in mediated-PT prefers to point toward the solution and forms a shorter H-bond with the assisted solvent water, leading to the lowest energy barrier and the fastest relative PT rate. In particular, it is found that the full solvation environment plays a crucial role in water-mediated proton conduction, while having little effect on direct PT reactions. The PT mechanisms on aqueous rutile oxide interfaces are also discussed by comparing an oxide series composed of SnO2, TiO2, and IrO2. Consequently, this work provides valuable insights into the ability of a deep neural network to reproduce the ab initio potential energy surface, as well as the PT mechanisms at such oxide/liquid interfaces, which can help understand the important chemical processes in electrochemistry, photoelectrocatalysis, colloid science, and geochemistry.
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