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基于量子力学对扰动链极性统计缔合流体理论参数进行估计,以分析其物理意义并预测性质。

Quantum mechanically based estimation of perturbed-chain polar statistical associating fluid theory parameters for analyzing their physical significance and predicting properties.

作者信息

Nhu Nguyen Van, Singh Mahendra, Leonhard Kai

机构信息

Lehrstuhl für Technische Thermodynamik, RWTH Aachen, 52056 Aachen, Germany.

出版信息

J Phys Chem B. 2008 May 8;112(18):5693-701. doi: 10.1021/jp7105742. Epub 2008 Apr 16.

Abstract

We have computed molecular descriptors for sizes, shapes, charge distributions, and dispersion interactions for 67 compounds using quantum chemical ab initio and density functional theory methods. For the same compounds, we have fitted the three perturbed-chain polar statistical associating fluid theory (PCP-SAFT) equation of state (EOS) parameters to experimental data and have performed a statistical analysis for relations between the descriptors and the EOS parameters. On this basis, an analysis of the physical significance of the parameters, the limits of the present descriptors, and the PCP-SAFT EOS has been performed. The result is a method that can be used to estimate the vapor pressure curve including the normal boiling point, the liquid volume, the enthalpy of vaporization, the critical data, mixture properties, and so on. When only two of the three parameters are predicted and one is adjusted to experimental normal boiling point data, excellent predictions of all investigated pure compound and mixture properties are obtained. We are convinced that the methodology presented in this work will lead to new EOS applications as well as improved EOS models whose predictive performance is likely to surpass that of most present quantum chemically based, quantitative structure-property relationship, and group contribution methods for a broad range of chemical substances.

摘要

我们使用量子化学从头算和密度泛函理论方法,计算了67种化合物的尺寸、形状、电荷分布和色散相互作用的分子描述符。对于相同的化合物,我们将三种扰动链极性统计缔合流体理论(PCP-SAFT)状态方程(EOS)参数拟合到实验数据,并对描述符与EOS参数之间的关系进行了统计分析。在此基础上,对参数的物理意义、当前描述符的局限性以及PCP-SAFT EOS进行了分析。结果得到了一种可用于估算蒸气压曲线的方法,包括正常沸点、液体体积、汽化焓、临界数据、混合物性质等。当仅预测三个参数中的两个并将其中一个调整到实验正常沸点数据时,可获得对所有研究的纯化合物和混合物性质的出色预测。我们相信,这项工作中提出的方法将带来新的EOS应用以及改进的EOS模型,其预测性能可能会超过目前大多数基于量子化学、定量结构-性质关系和基团贡献法对广泛化学物质的预测性能。

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