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Klimenko K, Carrera GVSM. QSPR modeling of selectivity at infinite dilution of ionic liquids. J Cheminform 2021; 13:83. [PMID: 34702358 PMCID: PMC8549394 DOI: 10.1186/s13321-021-00562-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 10/16/2021] [Indexed: 11/25/2022] Open
Abstract
The intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure–Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline + n-dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models’ predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n-dodecane was suggested.
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Affiliation(s)
- Kyrylo Klimenko
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências E Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516, Caparica, Portugal.
| | - Gonçalo V S M Carrera
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências E Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516, Caparica, Portugal
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Klimenko KO, Inês JM, Esperança JMSS, Rebelo LPN, Aires-de-Sousa J, Carrera GVSM. QSPR Modeling of Liquid-liquid Equilibria in Two-phase Systems of Water and Ionic Liquid. Mol Inform 2020; 39:e2000001. [PMID: 32469147 DOI: 10.1002/minf.202000001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 05/11/2020] [Indexed: 11/06/2022]
Abstract
The increasing application of new ionic liquids (IL) creates the need of liquid-liquid equilibria data for both miscible and quasi-immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two-phase systems were modeled using a Quantitative-Structure-Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y-scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two-phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines.
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Affiliation(s)
- Kyrylo Oleksandrovych Klimenko
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516 Caparica, Portugal
| | - João Miguel Inês
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516 Caparica, Portugal
| | - José Manuel Silva Simões Esperança
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516 Caparica, Portugal
| | - Luís Paulo Nieto Rebelo
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516 Caparica, Portugal
| | - João Aires-de-Sousa
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516 Caparica, Portugal
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Faramarzi Z, Abbasitabar F, Zare-Shahabadi V, Jahromi HJ. Novel mixture descriptors for the development of quantitative structure−property relationship models for the boiling points of binary azeotropic mixtures. J Mol Liq 2019. [DOI: 10.1016/j.molliq.2019.111854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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4
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Wu Y, Meng D, Zhao F, Wang Y, Gao J. Quantitative structure property relationship for relative volatility of isopropanol and water mixture. SEP SCI TECHNOL 2019. [DOI: 10.1080/01496395.2019.1675697] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Yumin Wu
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, China
| | - Dapeng Meng
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, China
| | - Fei Zhao
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, China
| | - Yinglong Wang
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, China
| | - Jun Gao
- College of Chemical and Environmental Engineering, Shandong University of Science and Technology, Qingdao, China
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Ma Y, Cui P, Wang Y, Zhu Z, Wang Y, Gao J. A review of extractive distillation from an azeotropic phenomenon for dynamic control. Chin J Chem Eng 2019. [DOI: 10.1016/j.cjche.2018.08.015] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Ma Y, Ma K, Wang H, Geng X, Gao J, Zhu Z, Wang Y. QSPR modeling of azeotropic temperatures and compositions for binary azeotropes containing lower alcohols using a genetic function approximation. Chin J Chem Eng 2019. [DOI: 10.1016/j.cjche.2018.06.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Pan Y, Ji X, Ding L, Jiang J. Prediction of Lower Flammability Limits for Binary Hydrocarbon Gases by Quantitative Structure-A Property Relationship Approach. Molecules 2019; 24:E748. [PMID: 30791456 PMCID: PMC6413142 DOI: 10.3390/molecules24040748] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Revised: 02/13/2019] [Accepted: 02/15/2019] [Indexed: 12/15/2022] Open
Abstract
The lower flammability limit (LFL) is one of the most important parameters for evaluating the fire and explosion hazards of flammable gases or vapors. This study proposed quantitative structure-property relationship (QSPR) models to predict the LFL of binary hydrocarbon gases from their molecular structures. Twelve different mixing rules were employed to derive mixture descriptors for describing the structures characteristics of a series of 181 binary hydrocarbon mixtures. Genetic algorithm (GA)-based multiple linear regression (MLR) was used to select the most statistically effective mixture descriptors on the LFL of binary hydrocarbon gases. A total of 12 multilinear models were obtained based on the different mathematical formulas. The best model, issued from the norm of the molar contribution formula, was achieved as a six-parameter model. The best model was then rigorously validated using multiple strategies and further extensively compared to the previously published model. The results demonstrated the robustness, validity, and satisfactory predictivity of the proposed model. The applicability domain (AD) of the model was defined as well. The proposed best model would be expected to present an alternative to predict the LFL values of existing or new binary hydrocarbon gases, and provide some guidance for prioritizing the design of safer blended gases with desired properties.
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Affiliation(s)
- Yong Pan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Xianke Ji
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Li Ding
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
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Fayet G, Rotureau P. New QSPR Models to Predict the Flammability of Binary Liquid Mixtures. Mol Inform 2019; 38:e1800122. [DOI: 10.1002/minf.201800122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 12/12/2018] [Indexed: 12/14/2022]
Affiliation(s)
- Guillaume Fayet
- INERISAccidental Risk Division Parc Technologique Alata 60550 Verneuil-en-Halatte France
| | - Patricia Rotureau
- INERISAccidental Risk Division Parc Technologique Alata 60550 Verneuil-en-Halatte France
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3D molecular fragment descriptors for structure–property modeling: predicting the free energies for the complexation between antipodal guests and β-cyclodextrins. J INCL PHENOM MACRO 2017. [DOI: 10.1007/s10847-017-0739-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Glavatskikh M, Madzhidov T, Solov'ev V, Marcou G, Horvath D, Varnek A. Predictive Models for the Free Energy of Hydrogen Bonded Complexes with Single and Cooperative Hydrogen Bonds. Mol Inform 2016; 35:629-638. [DOI: 10.1002/minf.201600070] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2016] [Accepted: 06/27/2016] [Indexed: 11/10/2022]
Affiliation(s)
- Marta Glavatskikh
- Laboratoire de Chémoinformatique; UMR 7140 CNRS; Université de Strasbourg; 1, rue Blaise Pascal 67000 Strasbourg France
- Laboratory of Chemoinformatics and Molecular Modeling; Butlerov Institut of Chemistry; Kazan Federal University; Kremlevskaya 18 Kazan Russia
| | - Timur Madzhidov
- Laboratory of Chemoinformatics and Molecular Modeling; Butlerov Institut of Chemistry; Kazan Federal University; Kremlevskaya 18 Kazan Russia
| | - Vitaly Solov'ev
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry; Russian Academy of Sciences; Leninskiy prosp., 31 119071 Moscow Russia
| | - Gilles Marcou
- Laboratoire de Chémoinformatique; UMR 7140 CNRS; Université de Strasbourg; 1, rue Blaise Pascal 67000 Strasbourg France
| | - Dragos Horvath
- Laboratoire de Chémoinformatique; UMR 7140 CNRS; Université de Strasbourg; 1, rue Blaise Pascal 67000 Strasbourg France
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique; UMR 7140 CNRS; Université de Strasbourg; 1, rue Blaise Pascal 67000 Strasbourg France
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Nieto-Draghi C, Fayet G, Creton B, Rozanska X, Rotureau P, de Hemptinne JC, Ungerer P, Rousseau B, Adamo C. A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes. Chem Rev 2015; 115:13093-164. [PMID: 26624238 DOI: 10.1021/acs.chemrev.5b00215] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Carlos Nieto-Draghi
- IFP Energies nouvelles , 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France
| | - Guillaume Fayet
- INERIS, Parc Technologique Alata, BP2 , 60550 Verneuil-en-Halatte, France
| | - Benoit Creton
- IFP Energies nouvelles , 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France
| | - Xavier Rozanska
- Materials Design S.A.R.L. , 18, rue de Saisset, 92120 Montrouge, France
| | - Patricia Rotureau
- INERIS, Parc Technologique Alata, BP2 , 60550 Verneuil-en-Halatte, France
| | | | - Philippe Ungerer
- Materials Design S.A.R.L. , 18, rue de Saisset, 92120 Montrouge, France
| | - Bernard Rousseau
- Laboratoire de Chimie-Physique, Université Paris Sud , UMR 8000 CNRS, Bât. 349, 91405 Orsay Cedex, France
| | - Carlo Adamo
- Institut de Recherche Chimie Paris, PSL Research University, CNRS, Chimie Paristech , 11 rue P. et M. Curie, F-75005 Paris, France.,Institut Universitaire de France , 103 Boulevard Saint Michel, F-75005 Paris, France
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Alibakhshi A, Mirshahvalad H, Alibakhshi S. A Modified Group Contribution Method for Accurate Prediction of Flash Points of Pure Organic Compounds. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b02786] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- A. Alibakhshi
- Department
of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran
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14
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The complexation of metal ions with various organic ligands in water: prediction of stability constants by QSPR ensemble modelling. J INCL PHENOM MACRO 2015. [DOI: 10.1007/s10847-015-0543-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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15
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Gaudin T, Rotureau P, Fayet G. Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b01457] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Théophile Gaudin
- INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
| | - Patricia Rotureau
- INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
| | - Guillaume Fayet
- INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
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Solov’ev V, Varnek A, Tsivadze A. QSPR ensemble modelling of the 1:1 and 1:2 complexation of Co2+, Ni2+, and Cu2+ with organic ligands: relationships between stability constants. J Comput Aided Mol Des 2014; 28:549-64. [DOI: 10.1007/s10822-014-9741-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Accepted: 04/01/2014] [Indexed: 12/01/2022]
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17
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Zare-Shahabadi V, Lotfizadeh M, Gandomani ARA, Papari MM. Determination of boiling points of azeotropic mixtures using quantitative structure–property relationship (QSPR) strategy. J Mol Liq 2013. [DOI: 10.1016/j.molliq.2013.09.037] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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18
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Solov’ev V, Marcou G, Tsivadze A, Varnek A. Complexation of Mn2+, Fe2+, Y3+, La3+, Pb2+, and UO22+ with Organic Ligands: QSPR Ensemble Modeling of Stability Constants. Ind Eng Chem Res 2012. [DOI: 10.1021/ie301271s] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Vitaly Solov’ev
- Institute of Physical Chemistry and
Electrochemistry, Russian Academy of Sciences, Leninskiy prospect, 31a, 119991, Moscow, Russian Federation
| | - Gilles Marcou
- Laboratoire d’Infochimie,
UMR 7177 CNRS, Université de Strasbourg, 4, rue B. Pascal, Strasbourg, 67000, France
| | - Aslan Tsivadze
- Institute of Physical Chemistry and
Electrochemistry, Russian Academy of Sciences, Leninskiy prospect, 31a, 119991, Moscow, Russian Federation
| | - Alexandre Varnek
- Laboratoire d’Infochimie,
UMR 7177 CNRS, Université de Strasbourg, 4, rue B. Pascal, Strasbourg, 67000, France
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Oprisiu I, Varlamova E, Muratov E, Artemenko A, Marcou G, Polishchuk P, Kuz'min V, Varnek A. QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids. Mol Inform 2012; 31:491-502. [DOI: 10.1002/minf.201200006] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2012] [Accepted: 04/23/2012] [Indexed: 11/11/2022]
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20
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Solov’ev VP, Kireeva N, Tsivadze AY, Varnek A. QSPR ensemble modelling of alkaline-earth metal complexation. J INCL PHENOM MACRO 2012. [DOI: 10.1007/s10847-012-0185-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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21
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KIM T, KANEKO H, YAMASHIRO N, FUNATSU K. Construction of Statistical Models for Predicting the Presence of Azeotropy at Any Pressure in Separation Processes. JOURNAL OF COMPUTER CHEMISTRY-JAPAN 2012. [DOI: 10.2477/jccj.2011-0028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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