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Application of hole theory to the viscosity of ionic and molecular liquids
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Gardas R.L., Coutinho J.A.P. Group contribution methods for the prediction of thermophysical and transport properties of ionic liquids. AIChE J. 2009, 55:1274-1290.
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QSPR analysis for intrinsic viscosity of polymer solutions by means of GA-MLR and RBFNN
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Gharagheizi F. QSPR analysis for intrinsic viscosity of polymer solutions by means of GA-MLR and RBFNN. Comput. Mater. Sci. 2007, 40:159-167.
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A new group contribution-based model for estimation of lower flammability limit of pure compounds
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Gharagheizi F. A new group contribution-based model for estimation of lower flammability limit of pure compounds. J. Hazard. Mater. 2009, 170:595-604.
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New neural network group contribution model for estimation of lower flammability limit temperature of pure compounds
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Gharagheizi F. New neural network group contribution model for estimation of lower flammability limit temperature of pure compounds. Ind. Eng. Chem. Res. 2009, 48:7406-7416.
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Prediction of upper flammability limit percent of pure compounds from their molecular structures
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Gharagheizi F. Prediction of upper flammability limit percent of pure compounds from their molecular structures. J. Hazard. Mater. 2009, 167:507-510.
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12
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84857066177
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Determination of diffusion coefficient of organic compounds in water using a simple molecular-based method
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Gharagheizi F. Determination of diffusion coefficient of organic compounds in water using a simple molecular-based method. Ind. Eng. Chem. Res. 2012, 51:2797-2803.
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Determination of normal boiling vaporization enthalpy using a new molecular-based model
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Gharagheizi F. Determination of normal boiling vaporization enthalpy using a new molecular-based model. Fluid Phase Equilib. 2012, 317:43-51.
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Fluid Phase Equilib.
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Gharagheizi, F.1
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78650085906
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A new neural network group contribution method for estimation of upper flash point of pure chemicals
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Gharagheizi F., Abbasi R. A new neural network group contribution method for estimation of upper flash point of pure chemicals. Ind. Eng. Chem. Res. 2010, 49:12685-12695.
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Gharagheizi, F.1
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15
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77958020996
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Prediction of Henry's law constant of organic compounds in water from a new group-contribution-based model
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Gharagheizi F., Abbasi R., Tirandazi B. Prediction of Henry's law constant of organic compounds in water from a new group-contribution-based model. Ind. Eng. Chem. Res. 2010, 49:10149-10152.
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Gharagheizi, F.1
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16
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54049139304
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A new neural network-group contribution method for estimation of flash point temperature of pure components
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Gharagheizi F., Alamdari R.F., Angaji M.T. A new neural network-group contribution method for estimation of flash point temperature of pure components. Energy Fuels 2008, 22:1628-1635.
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Energy Fuels
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Gharagheizi, F.1
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17
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80052367812
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Solubility parameters of nonelectrolyte organic compounds: determination using quantitative structure-property relationship strategy
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Gharagheizi F., Eslamimanesh A., Farjood F., Mohammadi A.H., Richon D. Solubility parameters of nonelectrolyte organic compounds: determination using quantitative structure-property relationship strategy. Ind. Eng. Chem. Res. 2011, 50:11382-11395.
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Gharagheizi, F.1
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Mohammadi, A.H.4
Richon, D.5
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18
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84860335530
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QSPR molecular approach for representation/prediction of very large vapor pressure dataset
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Gharagheizi F., Eslamimanesh A., Ilani-Kashkouli P., Mohammadi A.H., Richon D. QSPR molecular approach for representation/prediction of very large vapor pressure dataset. Chem. Eng. Sci. 2012, 76:99-107.
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Chem. Eng. Sci.
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Gharagheizi, F.1
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19
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84860335530
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QSPR molecular approach for representation/prediction of very large vapor pressure dataset
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Gharagheizi F., Eslamimanesh A., Ilani-Kashkouli P., Mohammadi A.H., Richon D. QSPR molecular approach for representation/prediction of very large vapor pressure dataset. Chem. Eng. Sci. 2012, 76:99-107.
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Richon, D.5
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20
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79955526954
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Determination of parachor of various compounds using an artificial neural network-group contribution method
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Gharagheizi F., Eslamimanesh A., Mohammadi A.H., Richon D. Determination of parachor of various compounds using an artificial neural network-group contribution method. Ind. Eng. Chem. Res. 2011, 50:5815-5823.
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Gharagheizi, F.1
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21
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79955610427
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QSPR approach for determination of parachor of non-electrolyte organic compounds
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Gharagheizi F., Eslamimanesh A., Mohammadi A.H., Richon D. QSPR approach for determination of parachor of non-electrolyte organic compounds. Chem. Eng. Sci. 2011, 66:2959-2967.
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Chem. Eng. Sci.
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22
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79954608677
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Representation/prediction of solubilities of pure compounds in water using artificial neural network-group contribution method
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Gharagheizi F., Eslamimanesh A., Mohammadi A.H., Richon D. Representation/prediction of solubilities of pure compounds in water using artificial neural network-group contribution method. J. Chem. Eng. Data 2011, 56:720-726.
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J. Chem. Eng. Data
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23
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79955886615
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Use of artificial neural network-group contribution method to determine surface tension of pure compounds
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Gharagheizi F., Eslamimanesh A., Mohammadi A.H., Richon D. Use of artificial neural network-group contribution method to determine surface tension of pure compounds. J. Chem. Eng. Data 2011, 56:2587-2601.
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J. Chem. Eng. Data
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Gharagheizi, F.1
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24
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80051871286
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Handling a very large data set for determination of surface tension of chemical compounds using quantitative structure-property relationship strategy
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Gharagheizi F., Eslamimanesh A., Tirandazi B., Mohammadi A.H., Richon D. Handling a very large data set for determination of surface tension of chemical compounds using quantitative structure-property relationship strategy. Chem. Eng. Sci. 2011, 66:4991-5023.
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25
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80051871286
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Handling a very large data set for determination of surface tension of chemical compounds using quantitative structure-property relationship strategy
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Gharagheizi F., Eslamimanesh A., Tirandazi B., Mohammadi A.H., Richon D. Handling a very large data set for determination of surface tension of chemical compounds using quantitative structure-property relationship strategy. Chem. Eng. Sci. 2011, 66:4991-5023.
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26
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84862124213
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A quantitative structure-property relationship for determination of enthalpy of fusion of pure compounds
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Gharagheizi F., Gohar M.R.S., Vayeghan M.G. A quantitative structure-property relationship for determination of enthalpy of fusion of pure compounds. J. Therm. Anal. Calorim. 2011, 1-6.
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27
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84859150628
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QSPR molecular approach for estimating Henry's law constants of pure compounds in water at ambient conditions
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Gharagheizi F., Ilani-Kashkouli P., Mirkhani S.A., Farahani N., Mohammadi A.H. QSPR molecular approach for estimating Henry's law constants of pure compounds in water at ambient conditions. Ind. Eng. Chem. Res. 2012, 51:4764-4767.
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28
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84859456958
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Computation of upper flash point of chemical compounds using a chemical structure-based model
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Gharagheizi F., Ilani-Kashkouli P., Mirkhani S.A., Mohammadi A.H. Computation of upper flash point of chemical compounds using a chemical structure-based model. Ind. Eng. Chem. Res. 2012, 51:5103-5107.
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29
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84870873443
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A simple accurate model for prediction of flash point temperature of pure compounds
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Gharagheizi F., Keshavarz M.H., Sattari M. A simple accurate model for prediction of flash point temperature of pure compounds. J. Therm. Anal. Calorim. 2011, 1-8.
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Gharagheizi, F.1
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30
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79959507972
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Prediction of standard enthalpy of combustion of pure compounds using a very accurate group-contribution-based method
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Gharagheizi F., Mirkhani S.A., Tofangchi Mahyari A.R. Prediction of standard enthalpy of combustion of pure compounds using a very accurate group-contribution-based method. Energy Fuels 2011, 25:2651-2654.
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Energy Fuels
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Gharagheizi, F.1
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31
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79959493797
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Prediction of enthalpy of fusion of pure compounds using an artificial neural network-group contribution method
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Gharagheizi F., Salehi G.R. Prediction of enthalpy of fusion of pure compounds using an artificial neural network-group contribution method. Thermochim. Acta 2011, 521:37-40.
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Thermochim. Acta
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Gharagheizi, F.1
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Prediction of triple-point temperature of pure components using their chemical structures
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Gharagheizi F., Sattari M. Prediction of triple-point temperature of pure components using their chemical structures. Ind. Eng. Chem. Res. 2010, 49:929-932.
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Power-law behavior in the viscosity of ionic liquids: existing a similarity in the power law and a new proposed viscosity equation
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Ghatee M.H., Zare M. Power-law behavior in the viscosity of ionic liquids: existing a similarity in the power law and a new proposed viscosity equation. Fluid Phase Equilib. 2011, 311:76-82.
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Temperature dependence of viscosity and relation with the surface tension of ionic liquids
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Ghatee M.H., Zare M., Zolghadr A.R., Moosavi F. Temperature dependence of viscosity and relation with the surface tension of ionic liquids. Fluid Phase Equilib. 2010, 291:188-194.
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35
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78650300657
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Data and QSPR study for viscosity of imidazolium-based ionic liquids
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Han C., Yu G., Wen L., Zhao D., Asumana C., Chen X. Data and QSPR study for viscosity of imidazolium-based ionic liquids. Fluid Phase Equilib. 2011, 300:95-104.
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36
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79960742113
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Improved reliable approach to predict melting points of energetic compounds
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Keshavarz M.H., Gharagheizi F., Pouretedal H.R. Improved reliable approach to predict melting points of energetic compounds. Fluid Phase Equilib. 2011, 308:114-128.
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Fluid Phase Equilib.
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Keshavarz, M.H.1
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37
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Computer-aided reverse design for ionic liquids by QSPR using descriptors of group contribution type for ionic conductivities and viscosities
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Matsuda H., Yamamoto H., Kurihara K., Tochigi K. Computer-aided reverse design for ionic liquids by QSPR using descriptors of group contribution type for ionic conductivities and viscosities. Fluid Phase Equilib. 2007, 261:434-443.
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Predictive quantitative structure-property relationship model for the estimation of ionic liquid viscosity
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Mirkhani S.A., Gharagheizi F. Predictive quantitative structure-property relationship model for the estimation of ionic liquid viscosity. Ind. Eng. Chem. Res. 2012, 51:2470-2477.
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Predictive quantitative structure-property relationship model for the estimation of ionic liquid viscosity
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Mirkhani S.A., Gharagheizi F. Predictive quantitative structure-property relationship model for the estimation of ionic liquid viscosity. Ind. Eng. Chem. Res. 2012, 51:2470-2477.
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An accurate model for the prediction of the glass transition temperature of ammonium based ionic liquids: a QSPR approach
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Mirkhani S.A., Gharagheizi F., Ilani-Kashkouli P., Farahani N. An accurate model for the prediction of the glass transition temperature of ammonium based ionic liquids: a QSPR approach. Fluid Phase Equilib. 2012, 324:50-63.
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Fluid Phase Equilib.
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42
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84856224118
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A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition
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Mirkhani S.A., Gharagheizi F., Sattari M. A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition. Chemosphere 2012, 86:959-966.
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Chemosphere
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Mirkhani, S.A.1
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A predictive quantitative structure-property relationship for glass transition temperature of 1,3-dialkyl imidazolium ionic liquids-Part 2. The nonlinear approach
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Mousavisafavi S.M., Gharagheizi F., Mirkhani S.A., Akbari J. A predictive quantitative structure-property relationship for glass transition temperature of 1,3-dialkyl imidazolium ionic liquids-Part 2. The nonlinear approach. J. Therm. Anal. Calorim. 2012, 1-10.
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A predictive quantitative structure-property relationship for glass transition temperature of 1,3-dialkyl imidazolium ionic liquids-Part 1. The linear approach
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