Permittivity Modeling in Electrolyte PC-SAFT

IF 2 3区 工程技术 Q3 CHEMISTRY, MULTIDISCIPLINARY Journal of Chemical & Engineering Data Pub Date : 2024-09-02 DOI:10.1021/acs.jced.4c00347
Lisa Rueben, Philipp Rehner, Joachim Gross, André Bardow
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Abstract

Accurately modeling the thermodynamic properties of electrolyte systems is key to designing decarbonized energy and chemical processes. For this purpose, a promising model class are electrolyte equations of state. These electrolyte equations of state require the relative static permittivity as an important input. The permittivity in solution deviates from the solvent permittivity, particularly at high ion concentrations. This deviation necessitates an approach to account for this dielectric decrement. This work presents a model for the dielectric decrement in the electrolyte equation of state ePC-SAFT. For this purpose, we extend ePC-SAFT by integrating our previous model for the relative static permittivity, based on perturbation theory. To account for the dielectric decrement, we obtain ion-specific permittivity parameters by adjusting to experimental mean ionic activity coefficients of water–salt mixtures. With a relative deviation of 4.1% averaged over all studied mixtures, the proposed approach accurately models mean ionic activity coefficients for lithium salts, sodium salts, potassium salts, and hydrogen halides. Moreover, the approach outperforms the common linear mixing approach based on the mole fraction. In this work, ePC-SAFT is implemented in the open-source software framework for equations of state FeOs and can be used as an easy-to-install Python package.

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电解质 PC-SAFT 中的幂率建模
准确模拟电解质系统的热力学特性是设计脱碳能源和化学过程的关键。为此,电解质状态方程是一类很有前途的模型。这些电解质状态方程需要将相对静态介电常数作为重要输入。溶液中的介电常数与溶剂介电常数存在偏差,尤其是在离子浓度较高的情况下。这种偏差需要一种方法来解释这种介电下降。本研究提出了电解质状态方程 ePC-SAFT 中的介电递减模型。为此,我们扩展了 ePC-SAFT,在扰动理论的基础上整合了之前的相对静态介电系数模型。为了考虑介电递减,我们根据水盐混合物的实验平均离子活度系数进行调整,得到了离子特定的介电系数参数。所有研究混合物的平均相对偏差为 4.1%,所提出的方法准确地模拟了锂盐、钠盐、钾盐和卤化氢的平均离子活度系数。此外,该方法优于基于分子分数的普通线性混合方法。在这项工作中,ePC-SAFT 是在 FeOs 状态方程开源软件框架中实现的,可以作为一个易于安装的 Python 软件包使用。
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来源期刊
Journal of Chemical & Engineering Data
Journal of Chemical & Engineering Data 工程技术-工程:化工
CiteScore
5.20
自引率
19.20%
发文量
324
审稿时长
2.2 months
期刊介绍: The Journal of Chemical & Engineering Data is a monthly journal devoted to the publication of data obtained from both experiment and computation, which are viewed as complementary. It is the only American Chemical Society journal primarily concerned with articles containing data on the phase behavior and the physical, thermodynamic, and transport properties of well-defined materials, including complex mixtures of known compositions. While environmental and biological samples are of interest, their compositions must be known and reproducible. As a result, adsorption on natural product materials does not generally fit within the scope of Journal of Chemical & Engineering Data.
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