Determination of the Dissociation Constants (pKa) of Eight Amines of Importance in Carbon Capture: Computational Chemistry Calculations, and Artificial Neural Network Models

IF 1.2 4区 化学 Q4 CHEMISTRY, PHYSICAL
Venkata Sai Priyatham Varma Alluri, William (Hoang Chi Hieu) Nguyen, A. Henni
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引用次数: 0

Abstract

This work focuses on determining the dissociation constants (pKa) of eight amines, namely, 3-(Diethylamino) propylamine, 1,3-Diaminopentane, 3-Butoxypropylamine, 2-(Methylamino) ethanol, Bis(2-methoxyethyl) amine, α-Methylbenzylamine, 2-Aminoheptane, and 3-Amino-1-phenylbutane, within temperatures ranging from 293.15 K to 323.15 K. The thermodynamic properties of the protonated reactions were regressed from the pKa work. In addition, the protonated order of both 3-(Diethylamino) propylamine and 1,3-Diaminopentane were determined using computational chemistry methods owing to their unsymmetrical structures. In addition to the experimental methods, the dissociation constants at the standard temperature (298.15 K) were also estimated using group functional models (paper–pencil) and computational methods. The computational methods include COSMO-RS and computational chemistry calculations. An artificial neural network (ANN) method was employed to model the data by collecting and combining the experimental properties to estimate the missing pKa values. Although the ANN models can provide acceptable results, they depend on the availability of the data. Instead of using the experimental properties, they were generated using software such as Aspen Plus or CosmothermX. The simulated ANN model can also provide very good fits to the experimental constant values.
碳捕获中八种重要胺解离常数(pKa)的测定:计算化学计算和人工神经网络模型
本文研究了3-(二乙基氨基)丙胺、1,3-二氨基戊烷、3-丁基丙胺、2-(甲氨基)乙醇、双(2-甲氧基乙基)胺、α-甲基苄胺、2-氨基庚烷和3-氨基-1-苯基丁烷在293.15 K至323.15 K温度范围内的解离常数(pKa)。利用pKa功回归了质子化反应的热力学性质。此外,由于3-(二乙胺)丙胺和1,3-二氨基戊烷的不对称结构,利用计算化学方法确定了它们的质子化顺序。除实验方法外,还利用基团功能模型(纸-铅笔)和计算方法估算了标准温度(298.15 K)下的解离常数。计算方法包括cosmos - rs和计算化学计算。采用人工神经网络(ANN)方法对数据进行建模,通过收集和结合实验性质来估计缺失的pKa值。尽管人工神经网络模型可以提供可接受的结果,但它们取决于数据的可用性。它们不是使用实验性质,而是使用Aspen Plus或CosmothermX等软件生成的。模拟的人工神经网络模型也能很好地拟合实验常数值。
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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来源期刊
Physics and Chemistry of Liquids
Physics and Chemistry of Liquids 化学-物理:凝聚态物理
CiteScore
3.30
自引率
8.30%
发文量
43
审稿时长
6-12 weeks
期刊介绍: Physics and Chemistry of Liquids publishes experimental and theoretical papers, letters and reviews aimed at furthering the understanding of the liquid state. The coverage embraces the whole spectrum of liquids, from simple monatomic liquids and their mixtures, through charged liquids (e.g. ionic melts, liquid metals and their alloys, ions in aqueous solution, and metal-electrolyte systems) to molecular liquids of all kinds. It also covers quantum fluids and superfluids, such as Fermi and non-Fermi liquids, superconductors, Bose-Einstein condensates, correlated electron or spin assemblies. By publishing papers on physical aspects of the liquid state as well as those with a mainly chemical focus, Physics and Chemistry of Liquids provides a medium for the publication of interdisciplinary papers on liquids serving its broad international readership of physicists and chemists.
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