致密溶液的化学模型

IF 3.3 3区 化学 Q2 CHEMISTRY, PHYSICAL Faraday Discussions Pub Date : 2024-05-22 DOI:10.1039/d4fd00084f
Jean-Francois Dufreche, Bertrand Siboulet, Magali Duvail
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引用次数: 0

摘要

在此,我们将探讨广泛用于描述稠密溶液(尤其是离子溶液)的化学模型问题。首先,一个简单的宏观分析表明,在弱相互作用的情况下,考虑到聚集的物种相当于模拟溶质之间的有效吸引力,尽管所使用的化学计量并不一定符合原子的实际情况。然后,我们使用严格的微观分析来解释在非常普遍的情况下,如何从原子物理描述中获得化学模型。我们证明,只要考虑精确计算,化学模型就没有好坏之分。尽管如此,为了获得尽可能简单的描述,最好还是采用能将过剩项最小化的物种标准。分子模拟结果表明,通常只需将直接接触的离子分组就可以定义物种。在某些情况下,还可以预测宏观簇的出现。
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Chemical Models for Dense Solutions
Here we examine the question of the chemical models widely used to describe dense solutions, particularly ionic solutions. First of all, a simple macroscopic analysis shows that in the case of weak interactions, taking into account aggregated species amounts to modelling an effective attraction between solutes, although the stoichiometry used does not necessarily correspond to atomic reality. We then use a rigorous microscopic analysis to explain how, in the very general case, chemical models can be obtained from an atomic physical description. We show that there are no good or bad chemical models as long as we consider exact calculations. To obtain the simplest possible description, it is nevertheless advisable to take the speciation criterion that minimises the excess terms. Molecular simulations show that very often species can be defined simply by grouping ions which are in direct contact. In some cases, the appearance of macroscale clusters can be predicted.
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Faraday Discussions
Faraday Discussions 化学-物理化学
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期刊介绍: Discussion summary and research papers from discussion meetings that focus on rapidly developing areas of physical chemistry and its interfaces
期刊最新文献
Ionic fluids out of equilibrium: electrodeposition, dissolution, electron transfer, driving forces: general discussion. Ionic fluids at equilibrium: thermodynamics, nanostructure, phase behaviour, activity: general discussion. New directions in experiment and theory, interfaces, and interactions: general discussion. Multi-reference coupled cluster theory using the normal ordered exponential ansatz. Analysis of uncertainty of neural fingerprint-based models.
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