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The importance of specifically adsorbed ions for electrokinetic phenomena: Bridging the gap between experiments and MD simulations

电动现象 流动电流 分子动力学 化学 离子 化学物理 水模型 水溶液 桥接(联网) 吸附 材料科学 计算化学 物理化学 计算机科学 有机化学 计算机网络
作者
Max F. Döpke,Remco Hartkamp
出处
期刊:Journal of Chemical Physics [American Institute of Physics]
卷期号:154 (9): 094701-094701 被引量:21
标识
DOI:10.1063/5.0038161
摘要

Molecular Dynamics (MD) simulations are uniquely suitable for providing molecular-level insights into the Electric Double Layer (EDL) that forms when a charged surface is in contact with an aqueous solution. However, simulations are only as accurate in predicting EDL properties as permitted by the atomic interaction models. Experimental ζ-potential values and surface charges could provide a potentially suitable reference to validate and tune the interaction models, if not for the fact that they themselves are a product of imperfect models used to interpret the raw measurement data. Here, we present an approach to tune an interaction model by comparing Electro-Osmotic Flow (EOF) MD simulations against experimental Streaming Current (SC) measurements while minimizing potential modeling errors arising from both approaches. The point that is least susceptible to interpretation and modeling errors is argued to be at the concentration for which zero flow velocity is observed in EOF simulations and a net zero electric current is measured in SC experiments. At this concentration, the ζ-potential is also zero. We were able to match the experimental concentration at which ζ = 0 in MD simulations for a CaCl2 solution at pH 7.5 in contact with fused silica by tuning the ion-surface Lennard-Jones cross interactions. These interactions were found to greatly affect the ion distribution within the EDL and particularly the formation of inner-sphere surface-complexes, which, in turn, affects the electrokinetic flow. With the ion distribution determined explicitly, a series of properties can be calculated unambiguously, such as the capacitance needed for surface complexation models.

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