New quantum chemistry-based descriptors for better prediction of melting point and viscosity of ionic liquids

被引:31
|
作者
Mehrkesh, Amirhossein [1 ]
Karunanithi, Arunprakash T. [1 ]
机构
[1] Univ Colorado Denver, Ctr Sustainable Infrastruct Syst, Denver, CO 80217 USA
基金
美国国家科学基金会;
关键词
Ionic liquids; Quantum chemistry; Physical properties prediction; STRUCTURE-PROPERTY RELATIONSHIPS; THERMOPHYSICAL PROPERTIES; PHYSICOCHEMICAL PROPERTIES; PHYSICAL-PROPERTIES; MOLECULAR-DYNAMICS; TEMPERATURE; TETRAFLUOROBORATE; SOLVENTS; DENSITY; DESIGN;
D O I
10.1016/j.fluid.2016.07.006
中图分类号
O414.1 [热力学];
学科分类号
摘要
Ionic liquids (ILs) are chemicals that are nonvolatile and hence have the potential to replace volatile organic compounds in industrial applications. A large number of ILs, through the combination of different cations and anions, can potentially be synthesized. In this context, it will be useful to intelligently design customized ILs through computer-aided methods. Practical limitations dictate that any successful attempt to design new ILs for industrial applications requires the ability to accurately predict their melting point and viscosity as experimental data will not be available for the designed structures. In this paper, we present two new correlations for precise prediction of melting point and viscosity of ILs solely based on inputs from quantum chemistry calculations (no experimental data or simulation results are needed). To develop these correlations we utilized data related to size, shape, and electrostatic properties of cations and anions that constitutes ILs. In this work, new descriptors such as dielectric energy of cations and anions as well as the values predicted by an 'ad-hoc' model for the radii of cations and anions (instead of their van der waals radii) were used. A large number of correlation equations consisting different combination of descriptors (as inputs to the model) were tested and the best correlation for viscosity and melting point were identified. The average relative errors were estimated as 3.16% and 6.45% for melting point, T-m, and ln(vis), respectively. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:498 / 503
页数:6
相关论文
共 50 条
  • [1] Heat Capacity Prediction of Ionic Liquids Based on Quantum Chemistry Descriptors
    Kang, Xuejing
    Liu, Xinyan
    Li, Jianqing
    Zhao, Yongsheng
    Zhang, Hongzhong
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2018, 57 (49) : 16989 - 16994
  • [2] Ionic liquids: Prediction of melting point by molecular-based model
    Farahani, Nasrin
    Gharagheizi, Farhad
    Mirkhani, Seyyed Alireza
    Tumba, Kaniki
    [J]. THERMOCHIMICA ACTA, 2012, 549 : 17 - 34
  • [3] Prediction of the Melting Point of Ionic Liquids with Clustering and Noeuroevolution
    Frausto-Solis, Juan
    Gonzalez-Barbosa, Juan Javier
    Cerecedo-Cordoba, Jorge Alberto
    Sanchez-Hernandez, Juan Paulo
    Diaz-Parra, Ocotlan
    Castilla-Valdez, Guadalupe
    [J]. INTERNATIONAL JOURNAL OF COMBINATORIAL OPTIMIZATION PROBLEMS AND INFORMATICS, 2023, 14 (03): : 24 - 30
  • [4] A quantitative prediction of the viscosity of ionic liquids using Sσ-profile molecular descriptors
    Zhao, Yongsheng
    Huang, Ying
    Zhang, Xiangping
    Zhang, Suojiang
    [J]. PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2015, 17 (05) : 3761 - 3767
  • [5] Quantum Chemistry-Based Approach for Density Prediction of non-ionic Hydrophobic Eutectic Solvents
    Kumar, Gaurav
    Kumar, Kishant
    Bharti, Anand
    [J]. JOURNAL OF SOLUTION CHEMISTRY, 2024, 53 (09) : 1195 - 1210
  • [6] Prediction of The Ionic Conductivity and Viscosity of Ionic Liquids by QSPR Using Descriptors of Group Contribution Type
    Matsuda, Hiroyuki
    Yamamoto, Hiroshi
    Kurihara, Kiyofumi
    Tochigi, Katsumi
    [J]. JOURNAL OF COMPUTER AIDED CHEMISTRY, 2007, 8 : 114 - 127
  • [7] Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids
    Cao, Lingdi
    Zhu, Peng
    Zhao, Yongsheng
    Zhao, Jihong
    [J]. JOURNAL OF HAZARDOUS MATERIALS, 2018, 352 : 17 - 26
  • [8] Low-viscosity and low-melting point asymmetric trialkylsulfonium based ionic liquids as potential electrolytes
    Fang, Shaohua
    Yang, Li
    Wei, Chao
    Peng, Chengxin
    Tachibana, Kazuhiro
    Kamijima, Kouichi
    [J]. ELECTROCHEMISTRY COMMUNICATIONS, 2007, 9 (11) : 2696 - 2702
  • [9] Machine Learning Models for Melting Point Prediction of Ionic Liquids: CatBoost Approach
    Blaise, Mathias
    Barras, Simon
    Yerly, Florence
    [J]. CHIMIA, 2023, 77 (09) : 625 - 627
  • [10] Ionic Liquids Based on the Concept of Melting Point Lowering Due to Ethoxylation
    Rothe, Manuel
    Mueller, Eva
    Denk, Patrick
    Kunz, Werner
    [J]. MOLECULES, 2021, 26 (13):