纳米孔中离子输运的多尺度建模：将隐式水径向扩散剖面拟合到显式水分子动力学
Multiscale Modeling of Ion Transport in Nanopores: Fitting Implicit-Water Radial Diffusion Profiles to Explicit-Water Molecular Dynamics
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- Center for Natural Sciences, University of Pannonia（佩奇大学自然科学中心）
- Department of Engineering, Reykjavik University（雷克雅未克大学工程系）
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中文总结 AI 辅助
开发多尺度方法，将显式水MD的径向扩散剖面拟合到NP+LEMC框架，用于纳米孔离子输运建模，再现电流分布并揭示NaCl与CaCl2的输运差异及混合物的非线性电导变化。
中文摘要 AI 辅助
我们开发了一种多尺度方法，用于将分子尺度的输运信息纳入计算高效的纳米孔离子输运模型中。我们将径向变化的有效扩散系数剖面拟合到从显式水分子动力学（MD）模拟中获得的径向电导率剖面。该拟合在NP+LEMC框架内进行，该框架将Nernst-Planck方程与局部平衡蒙特卡罗方法相结合，以考虑超越平均场近似的离子关联。我们将该方法应用于带负电荷的二氧化硅纳米孔中的NaCl、CaCl2及其混合物。所得的扩散系数剖面再现了MD模拟的径向电流分布，包括孔壁附近离子迁移率的强烈抑制，这是孔内空间恒定扩散系数无法捕捉的。NaCl和CaCl2表现出定性不同的输运行为：前者由于近壁Na+传导增强而具有阳离子选择性，而后者由于强结合的Ca2+离子在表面附近迁移率受到强烈抑制而表现出弱阴离子选择性。对于NaCl-CaCl2混合物，Ca2+的优先结合导致离子电导的非线性变化。该方法在实验相关的器件行为与计算高效简化模型之间建立了桥梁，显式水MD提供了可纳入有效输运系数的分子尺度信息。
英文摘要
We develop a multiscale approach for incorporating molecular-scale transport information into computationally efficient models of ion transport through nanopores. A radially varying effective diffusion coefficient profile is fitted to radial conductivity profiles obtained from explicit-water molecular dynamics (MD) simulations. The fitting is performed within the NP+LEMC framework, which combines the Nernst--Planck equation with Local Equilibrium Monte Carlo to account for ion correlations beyond mean-field approximation. We apply the approach to NaCl, CaCl$_2$, and their mixtures in a negatively charged silica nanopore. The resulting diffusion coefficient profiles reproduce the radial current distributions of the MD simulations, including the strong suppression of ionic mobility near the pore wall that cannot be captured by a spatially constant diffusion coefficient inside the pore. NaCl and CaCl$_2$ exhibit qualitatively different transport behavior: the former is cation selective due to enhanced near-wall Na$^+$ conduction, whereas the latter shows weak anion selectivity because strongly bound Ca$^{2+}$ ions have strongly suppressed mobility near the surface. For NaCl--CaCl$_2$ mixtures, preferential Ca$^{2+}$ binding leads to nonlinear changes in ionic conductance. The approach establishes a bridge between experimentally relevant device behavior and computationally efficient reduced models, with explicit-water MD providing the molecular-scale information that can be incorporated into the effective transport coefficients.
