建立限制原始模型电解质静电筛选长度的有效方法

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL Physical Chemistry Chemical Physics Pub Date : 2024-07-03 DOI:10.1039/D4CP00546E
Jan Forsman, David Ribar and Clifford E. Woodward
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引用次数: 0

摘要

我们提出了一种新颖的、计算成本低廉的方法,用于从限制原始模型(RPM)电解质的模拟中估算静电屏蔽长度。通过与模拟的电荷密度长程部分进行比较,我们证明了该方法在不同的比尤鲁姆长度、盐浓度和离子直径条件下的准确性。我们发现在低介电常数溶剂中存在大量屏蔽不足的现象,但在 "水 "溶剂中则存在屏蔽过度的现象,其程度随离子大小而增加。我们的方法还为离子液体(未来)更精确的经典密度泛函处理提供了可能的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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An efficient method to establish electrostatic screening lengths of restricted primitive model electrolytes†

We present a novel, and computationally cheap, way to estimate electrostatic screening lengths from simulations of restricted primitive model (RPM) electrolytes. We demonstrate that the method is accurate by comparisons with simulated long-ranged parts of the charge density, at various Bjerrum lengths, salt concentrations and ion diameters. We find substantial underscreening in low dielectric solvent, but with an “aqueous” solvent, there is instead overscreening, the degree of which increases with ion size. Our method also offers a possible path to (future) more accurate classical density functional treatments of ionic fluids.

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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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