The dissociation of water is an important step in many chemical processes at solid surfaces. In particular, water often spontaneously dissociates near metal oxide surfaces, resulting in a mixture of H2O, H+, and OH- at the interface. Ubiquitous proton-transfer (PT) reactions cause these species to dynamically interconvert, but the underlying mechanisms are poorly understood. Here, we develop and use a reactive high-dimensional neural-network potential based on density functional theory data to elucidate the structural and dynamical properties of the interfacial species at the liquid-water-metal-oxide interface, using the nonpolar ZnO(101̅0) surface as a prototypical case. Molecular dynamics simulations reveal that water dissociation and recombination proceed via two types of PT reactions: (i) to and from surface oxide and hydroxide anions ("surface-PT") and (ii) to and from neighboring adsorbed hydroxide ions and water molecules ("adlayer-PT"). We find that the adlayer-PT rate is significantly higher than the surface-PT rate. Water dissociation is, for both types of PT, governed by a predominant presolvation mechanism, i.e., thermal fluctuations that cause the adsorbed water molecules to occasionally accept a hydrogen bond, resulting in a decreased PT barrier and an increased dissociation rate as compared to when no hydrogen bond is present. Consequently, we are able to show that hydrogen bond fluctuations govern PT events at the water-metal-oxide interface in a way similar to that in acidic and basic aqueous bulk solutions.
水的解离是固体表面许多化学过程中的一个重要步骤。特别是,水经常在金属氧化物表面附近自发解离,导致界面处存在H₂O、H⁺和OH⁻的混合物。普遍存在的质子转移(PT)反应使这些物质动态地相互转化,但潜在的机制却鲜为人知。在此,我们基于密度泛函理论数据开发并使用了一种反应性高维神经网络势,以非极性的ZnO(101̅0)表面作为典型案例,来阐明液 - 水 - 金属 - 氧化物界面处界面物质的结构和动力学性质。分子动力学模拟显示,水的解离和复合通过两种类型的PT反应进行:(i)与表面氧化物和氢氧化物阴离子之间的转移(“表面 - PT”)以及(ii)与相邻吸附的氢氧化物离子和水分子之间的转移(“吸附层 - PT”)。我们发现吸附层 - PT速率明显高于表面 - PT速率。对于这两种类型的PT,水的解离都由一种主要的预溶剂化机制控制,即热涨落导致吸附的水分子偶尔接受一个氢键,与不存在氢键时相比,这使得PT势垒降低,解离速率提高。因此,我们能够表明氢键涨落以一种类似于酸性和碱性水本体溶液中的方式控制着水 - 金属 - 氧化物界面处的PT事件。