Performance of the SMD and SM8 models for predicting solvation free energy of neutral solutes in methanol, dimethyl sulfoxide and acetonitrile

Zanith, Caroline C.; Pliego, Josefredo R.

doi:10.1007/s10822-014-9814-3

Cited by 31 publications

(35 citation statements)

References 43 publications

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“…Among the possible continuum methods, the SMD model of Cramer and Truhlar has been tested on several problems involving solvation and chemical reactions and usually presents a good performance for solvation of neutral species. 24,[33][34][35][36][37][38] In the cases that free ionic species are involved, the method is less reliable and may be needed to use hybrid clustercontinuum approaches. 39,40 In the application of continuum models for chemical reactions, it is important to take care about the type of reaction is being considered.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, the error in the solvation free energy for the SMD model for neutral molecules is usually less than 1 kcal mol -1 . 24,37,38 In the second set of reactions, ions exist like ion pairs and ion pairs are being formed or are reacting. In addition, in the reaction (4), a free solvated ion is reacting with a neutral molecule and forming a new solvated ion and a new neutral molecule.…”

Section: Introductionmentioning

confidence: 99%

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Solvent selection for chemical reactions: automated computational screening of solvents using the SMD model

Dalessandro¹,

Pliego²

2018

Quim. Nova

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Finding the most efficient solvent for a chemical reaction can demand costly experimental procedure and the screening is usually limited to few solvents. The use of theoretical methods could accelerate the search for the best solvent, able to promote most effective kinetics and thermodynamics of a reaction. In this work, it was proposed an automated procedure that calculates the solvent effect for a chemical reaction using all the 179 solvents available in SMD (solvation model density). The reaction of 2-bromoacetophenone with pyridine was used as a test. The SMD model correctly predicts the reactivity trends for five solvents, which experimental data are available. We have found that sulfolane, a less usual solvent, is the best one for this reaction. The present study points out that computational screening of large set of solvents using the SMD model is a viable approach and could be useful for chemical reactions optimization.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solvent selection for chemical reactions: automated computational screening of solvents using the SMD model

Dalessandro¹,

Pliego²

2018

Quim. Nova

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“…In a recent study, the mean unsigned error was in the range of 0.5 to 1.0 kcal mol -1 for simple organic molecules in the organic solvents methanol, dimethyl sulfoxide and acetonitrile. 14 In the case of more functionalized molecules, like those studied in this work, the error is probable to be even higher. Second, the use of infinity dilution condition in the SMD calculations.…”

Section: Resultsmentioning

confidence: 79%

Fast Screening of Solvents for Simultaneous Extraction of Furfural, 5-Hydroxymethylfurfural and Levulinic Acid from Aqueous Solution Using SMD Solvation Free Energies

Dalessandro¹,

Pliego²

2017

J. Braz. Chem. Soc.

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Acid catalyzed decomposition of C 5 and C 6 sugars has been considered an important source of biomass derived chemicals. An essential step in this process is the extraction of furfural, hydroxymethylfurfural and levulinic acid from aqueous phase. In this work, a computational screening of 178 organic solvents for simultaneous extraction of these chemicals from aqueous phase has been done with the continuum SMD (solvation model based on density) model. Our analysis has taken in account the partition coefficient, water miscibility, boiling point and toxicity of the organic solvents. The present theoretical results indicate that C 6 and C 7 ketones are the most adequate solvents, and 4-heptanone is predicted to be particularly useful. Keywords: partition coefficient, green chemistry, MIBK, 4-heptanone, tributyl phosphate IntroductionPetroleum remains as the main source of feedstock for manufacture of fuels and chemicals. Nevertheless, the increased concern on environmental problems related to carbon dioxide emissions and the petroleum dependence have encouraged the development of biomass based fuels and chemicals. [1][2][3][4][5] Among the different processes for biomass transformation, acid catalyzed hydrolysis of xylan and glycan components of wood to form C 5 and C 6 sugars have received attention. 6 In the catalytic process, xylose and glucose (or fructose) are transformed to furfural, 5-hydroxymethylfurfural (HMF) and levulinic acid via acid-catalyzed dehydration (Scheme 1).In the acid medium used for generation of the products, more degradation can take place, leading to formic acid. Scheme 1. Formation of furfural, HMF and levulinic acid from acid catalyzed dehydration of C 5 and C 6 sugars. Dalessandro and Pliego Jr. 431 Vol. 29, No. 2, 2018 Current processes for recovering of furfural makes use of steam. However, the yield is limited and recently the use of solvent extraction has been advocated as more effective process. 6,7 In the same way, extraction of HMF during the reaction is desirable because it avoids degradation of this compound. 5 An efficient extraction requires an adequate solvent, which should have low miscibility with water and lead to a high partition coefficient for each compound. Recent experimental studies have investigated different solvents for extraction [7][8][9][10] and some authors have suggested that 2-butanol and 2-butanone are effective for HMF, while methyl isobutyl ketone (MIBK) is effective for both HMF and furfural compounds. 5,6,9 Other possibility for identifying efficient solvents for extraction is via computational methods. Thus, Sandler and co-workers 11 have used the COSMO-SAC method to investigate the ability of several linear and branched alcohols to extract HMF from aqueous solution and have found that the method produces reasonable partition coefficients. Following this work, Blumenthal et al. 12 have reported a very extensive screening of solvents for extraction of HMF using the COSMO-RS method and they have suggested 110 potential solvents superior to that i...

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“…15 The most recent version of the SM models, SMD, 11 was reported to have a mean unsigned error (MUE) of 0.6-1.0 kcal mol À1 in the free energy of solvation based on a study of 2346 solute/ solvent pairs involving 318 neutral solutes and 91 solvents. Zanith and Pliego 20 have recently investigated the predictive capabilities of SMD and its precursor SM8 for 77 data points corresponding to the Gibbs free energy of solvation of a number of solutes in methanol, DMSO (dimethyl sulfoxide), and acetonitrile, three common solvents. They reported a root mean squared error (RMSE) of 0.53 kcal mol À1 in acetonitrile, 0.83 kcal mol À1 in methanol and 1.22 kcal mol À1 in DMSO for SMD and 0.69 kcal mol À1 in acetonitrile, 0.71 kcal mol À1 in methanol and 1.05 kcal mol À1 in DMSO for SM8.…”

Section: Introductionmentioning

confidence: 99%

Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs

Borhani

García‐Muñoz

Luciani

et al. 2019

Phys. Chem. Chem. Phys.

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Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute. Furthermore, while most QSPR models include either experimental or quantum mechanical descriptors, the proposed models combine both, using experimental descriptors to represent the solvent and quantum mechanical descriptors to represent the solute. Up to twelve experimental descriptors and nine quantum mechanical descriptors are considered in the proposed models. Extensive internal and external validation is undertaken to assess model accuracy in predicting the Gibbs free energy of solvation for a large number of solute/solvent pairs. The best MLR model, which includes three solute descriptors and two solvent properties, yields a coefficient of determination (R 2 ) of 0.88 and a root mean squared error (RMSE) of 0.59 kcal mol À1 for the training set. The best PLS model includes six latent variables, and has an R 2 value of 0.91 and a RMSE of 0.52 kcal mol À1 . The proposed models are compared to selected results based on continuum solvation quantum chemistry calculations. They enable the fast prediction of the Gibbs free energy of solvation of a wide range of solutes in different solvents.

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Performance of the SMD and SM8 models for predicting solvation free energy of neutral solutes in methanol, dimethyl sulfoxide and acetonitrile

Cited by 31 publications

References 43 publications

Solvent selection for chemical reactions: automated computational screening of solvents using the SMD model

Solvent selection for chemical reactions: automated computational screening of solvents using the SMD model

Fast Screening of Solvents for Simultaneous Extraction of Furfural, 5-Hydroxymethylfurfural and Levulinic Acid from Aqueous Solution Using SMD Solvation Free Energies

Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs

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