揭示商业阴离子交换膜中氢氧根离子传输机制的纳米尺度机制：基于机器学习的原子间势能模拟
Revealing Hydroxide Ion Transport Mechanisms in Commercial Anion-Exchange Membranes at Nano-Scale from Machine-learned Interatomic Potential Simulations
AI总结：
研究通过机器学习势能模拟揭示了商业阴离子交换膜中氢氧根离子传输机制，发现水含量影响氢键网络形成，进而影响质子迁移和扩散系数，为膜材料优化提供理论依据。
摘要：
氢氧根离子传输在碱性水裂解中限制了绿色氢气生产效率，但因建模离子动力学的计算挑战，原子尺度传输机制仍不明确。本文表明，利用精细调参的机器学习原子间势能进行大规模分子动力学模拟，能提供商业膜中氢氧根在数十纳秒和十纳米尺度的原子级洞察。研究发现，增加水含量将孤立水簇转化为连通的氢键网络，促进长程质子转移。在干燥条件下，氢氧根离子被正电基团捕获，传输受阻；而充分水合膜表现出扩展的质子迁移和接近稀释水溶液的扩散系数。模拟再现了实验中扩散和活化能的趋势。研究结果建立了纳米尺度结构与宏观传输的直接联系。除机理洞察外，所提模拟框架还支持预测性、模拟指导的膜化学与架构优化，为高效阴离子交换膜的理性设计铺平道路。
英文摘要：
Hydroxide ion transport in anion-exchange membranes fundamentally limits the efficiency of alkaline water electrolysis for green hydrogen production, yet the atomic-scale transport mechanisms remain poorly understood due to the computational challenges associated with modeling ion dynamics. Given that anion-exchange membranes enable alkaline electrolysis with abundant catalysts while avoiding perfluoroalkyl and polyfluoroalkyl materials, a deeper mechanistic understanding of hydroxide transport in these systems is essential for advancing sustainable hydrogen production. Here, we show that large-scale molecular dynamics simulations with fine-tuned machine-learned interatomic potentials provide atomistic insight into hydroxide mobility in a commercial membrane over tens of nanoseconds and over ten nanometer. We find that increasing water content transforms isolated water clusters into a connected hydrogen-bond network that enables long-range proton transfer. Under dry conditions hydroxide ions are trapped near positively charged groups and transport is strongly hindered, whereas well-hydrated membranes exhibit extended proton migration and diffusion coefficients approaching those of dilute aqueous solutions. The simulations reproduce experimental trends in diffusion and activation energies. Our results establish a direct link between nano-scale structure and macroscopic transport. Beyond mechanistic insight, the presented simulation framework enables predictive, simulation-guided optimization of membrane chemistry and architecture, opening a pathway toward the rational design of more efficient anion-exchange membranes for green hydrogen technologies.
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