Correction: Vatani, A., et al. Prediction of Standard Enthalpy of Formation by a QSPR Model. Int. J. Mol. Sci. 2007, 8, 407-432.

Vatani, Ali; Mehrpooya, Mehdi; Gharagheizi, Farhad

doi:10.3390/ijms10020723

Cited by 13 publications

(18 citation statements)

References 1 publication

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“…In the present study, their DH 0 f 298 values are estimated by two sets of isodesmic reactions (R3)-(R6). The reaction enthalpies of (R3)-(R6) are firstly calculated and then these theoretical results are combined with the known standard enthalpies of formation [36,37] (CH 4 , À17.88 kcal mol À1 ; C 2 H 6 , À20.0 kcal mol À1 ; CH 2 F 2 , À108.2 kcal mol À1 ; CH 3 CHF 2 , À120 AE 1 kcal mol À1 ; CF 3 Fig. 1.…”

Section: Stationary Pointsmentioning

confidence: 99%

Direct dynamics studies for the reactions of CF3CHFCF3 and CF3CF2CHF2 with H atoms

Wang

Zhao

Zhang

2011

Journal of Fluorine Chemistry

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Section: Stationary Pointsmentioning

confidence: 99%

Direct dynamics studies for the reactions of CF3CHFCF3 and CF3CF2CHF2 with H atoms

Wang

Zhao

Zhang

2011

Journal of Fluorine Chemistry

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“…For example, Vatani et al [5] predicted standard enthalpy of formation of various organic compounds using genetic algorithm-based multivariate linear regression (GA-MLR). Gharagheizi [6] developed a QSPR model for estimation of enthalpy of sublimation of pure components from theoretical derived molecular descriptors.…”

Section: Introductionmentioning

confidence: 99%

Application of QSPR for the prediction of gas to 1-octanol solvation enthalpy using support vector regression

Golmohammadi

Dashtbozorgi

Acree

2013

Physics and Chemistry of Liquids

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A quantitative structure property relationship model was developed to predict gas to 1-octanol solvation enthalpy (DH Solv ) of 127 different organic compounds using support vector machine (SVM). The variable selection method of genetic algorithm (GA) was employed to select optimal subset of descriptors. The five descriptors selected by GA were used as inputs for construction of the multiple linear regression (MLR), artificial neural network (ANN) and SVM models. The standard errors of for the prediction data set given by MLR, ANN and SVM were 6.438, 4.004 and 2.711 kJ mol À1 , respectively, which indicated that the prediction result agrees well with the experimental values of these compounds and also revealed the superiority of SVM over MLR and ANN models. Furthermore, the derived models also provided some insight into what structural features are related to the gas to DH Solv of various organic compounds.

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“…This seems quite surprising at first sigh due the thermodynamically disfavored existence of terminal alkene vs internal alkene. 135 Studies made using MeOD instead of MeOH on the alkoxycarbonylation of 1-octene in a mixture MeOD/toluene : 1/4 showed that all isomers with 0-15 D atoms exist. 117 The existence of the deuterated isomers was the first evidence to the ability of the catalyst to isomerise the C=C bond (Figure 1.11).…”

Section: Isomerisationmentioning

confidence: 99%

Polymer precursors from catalytic reactions of natural oils

et al. 2012

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Dimethyl 1,19-nonadecanedioate is produced from the methoxycarbonylation of commercial olive, rapeseed or sunflower oils in the presence of a catalyst derived from [Pd 2 (dba) 3 ], bis(ditertiarybutylphosphinomethyl)benzene (BDTBPMB) and methanesulphonic acid (MSA). The diester is then hydrogenated to 1,19-nonadecanediol using Ru/1,1,1-tris-(diphenylphosphinemethyl)ethane (triphos). 1,19-Nonadecadienoic acid is hydrogenated to short chain oligoesters, which can themselves be hydrogenated to 1,19-nonadecanol by hydrogenation in the presence of water.

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Correction: Vatani, A., et al. Prediction of Standard Enthalpy of Formation by a QSPR Model. Int. J. Mol. Sci. 2007, 8, 407-432.

Cited by 13 publications

References 1 publication

Direct dynamics studies for the reactions of CF3CHFCF3 and CF3CF2CHF2 with H atoms

Direct dynamics studies for the reactions of CF3CHFCF3 and CF3CF2CHF2 with H atoms

Application of QSPR for the prediction of gas to 1-octanol solvation enthalpy using support vector regression

Polymer precursors from catalytic reactions of natural oils

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