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通过在统计缔合流体理论(SAFT)中引入双态理论预测水的异常性质。

Prediction of water anomalous properties by introducing the two-state theory in SAFT.

作者信息

Novak Nefeli, Liang Xiaodong, Kontogeorgis Georgios M

机构信息

Center for Energy Resources Engineering, Department of Chemical and Biochemical Engineering, Technical University of Denmark, 2800 Kgs Lyngby, Denmark.

出版信息

J Chem Phys. 2024 Mar 14;160(10). doi: 10.1063/5.0186752.

Abstract

Water is one of the most abundant substances on earth, but it is still not entirely understood. It shows unusual behavior, and its properties present characteristic extrema unlike any other fluid. This unusual behavior has been linked to the two-state theory of water, which proposes that water forms different clusters, one with a high density and one with a low density, which may even form two distinct phases at low temperatures. Models incorporating the two-state theory manage to capture the unusual extrema of water, unlike traditional equations of state, which fail. In this work, we have derived the framework to incorporate the two-state theory of water into the Statistical-Associating-Fluid-Theory (SAFT). More specifically, we have assumed that water is an ideal solution of high density water molecules and low density water molecules that are in chemical equilibrium. Using this assumption, we have generalized the association term SAFT to allow for the simultaneous existence of the two water types, which have the same physical parameters but different association properties. We have incorporated the newly derived association term in the context of the Perturbed Chain-SAFT (PC-SAFT). The new model is referred to as PC-SAFT-Two-State (PC-SAFT-TS). Using PC-SAFT-TS, we have succeeded in predicting the characteristic extrema of water, such as its density and speed of sound maximum, etc., without loss of accuracy compared to the original PC-SAFT. This new framework is readily extended to mixtures, and PC-SAFT-TS manages to capture the solubility minimum of hydrocarbons in water in a straightforward manner.

摘要

水是地球上含量最丰富的物质之一,但人们对它仍未完全了解。它表现出异常的行为,其性质呈现出不同于任何其他流体的特征极值。这种异常行为与水的双态理论有关,该理论提出水形成不同的簇,一种具有高密度,一种具有低密度,甚至在低温下可能形成两个不同的相。与传统的状态方程不同,纳入双态理论的模型能够捕捉到水的异常极值,而传统状态方程则无法做到。在这项工作中,我们推导出了将水的双态理论纳入统计缔合流体理论(SAFT)的框架。更具体地说,我们假设水是处于化学平衡的高密度水分子和低密度水分子的理想溶液。基于这一假设,我们对缔合项SAFT进行了推广,以允许两种具有相同物理参数但缔合性质不同的水同时存在。我们在扰动链SAFT(PC-SAFT)的背景下纳入了新推导的缔合项。新模型被称为PC-SAFT-双态(PC-SAFT-TS)。使用PC-SAFT-TS,我们成功地预测了水的特征极值,如密度和声速最大值等,与原始的PC-SAFT相比,精度没有损失。这个新框架很容易扩展到混合物,并且PC-SAFT-TS能够直接捕捉碳氢化合物在水中的溶解度最小值。

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