Correlation and prediction of critical micelle concentration using polar surface area and LFER methods

Saunders, Robert A.; Platts, James Alexis

doi:10.1002/poc.749

Cited by 19 publications

(12 citation statements)

References 31 publications

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“…Our dataset covers a wide range of CMC values, from 0.0033 mM to 180 mM. In this study, CMCs were analyzed in decimal logarithm (in M), as performed by other workers 31,38,[42][43][44][45][46][47] , since a linear dependency of log CMC with the length of the alkyl chain was evidenced by experimentalists 1 . Finally, the distribution of log CMC data is close to normality, with data ranging between -5.5 and -0.7 and a maximum around -2.5 (see Figure 1).…”

Section: Experimental Datasetmentioning

confidence: 99%

“…Although many QSPR models have been proposed for CMC, even for non-ionic surfactants 38 , none of them are specifically dedicated to the CMC of sugar-based surfactants. Besides, only a few authors even considered sugar-based surfactants 31,38,[42][43][44][45][46][47] , and only three of their QSPR models were tested on a validation set 43,45,46 . Khayamian et al 43 developed a neural network for the CMCs of non-ionic surfactants (including sugar-based surfactants) validated with an average relative error of 1.5% but on only 5 molecules.…”

Section: Introductionmentioning

confidence: 99%

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New QSPR Models to Predict the Critical Micelle Concentration of Sugar-Based Surfactants

Gaudin

Rotureau

Pezron

et al. 2016

Ind. Eng. Chem. Res.

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Sugar-based surfactants represent a fruitful field of research in the context of sustainable chemistry since they can be obtained from renewable resources. In this work, new Quantitative Structure Property Relationships (QSPR) models for the critical micelle concentration (CMC) dedicated to sugar-based surfactants are proposed in order to reduce testing in a screening perspective. An important literature compilation allowed the constitution of a dataset of 83 sugar-based surfactants for which accurate CMC values were found. Then, a series of QSPR models were developed based on molecular descriptors of the whole molecule, and of the hydrophobic and hydrophilic fragments taken separately. Different models were considered by including quantum-chemical descriptors with hope to access physically based models, and by using only simple constitutional descriptors to favor fast and easy prediction. The best QSPR model was obtained including quantum-chemical descriptors of the whole molecular structure with a root mean square error (RMSE) of 0.32 (log) evaluated on a validation set of 27 molecules. A simpler model with good performances was also found (with a RMSE of 0.36 (log) on the validation set), including only constitutional-based fragment descriptors, that can be easily computed from the 2-dimension structure of the hydrophilic and hydrophobic fragments.

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Section: Experimental Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New QSPR Models to Predict the Critical Micelle Concentration of Sugar-Based Surfactants

Gaudin

Rotureau

Pezron

et al. 2016

Ind. Eng. Chem. Res.

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“…The first correlation was given by Klevens [1] who empirically found that logarithm of cmc linearly decreases with increasing length of an alkyl chain. Recently, the QSPR was used for predicting the cmc values of nonionic surfactants [2][3][4][5], anionic surfactants [5][6][7][8] and cationic surfactants [9]. In papers [2,3,10] molecular connectivity indices together with another topological descriptors are correlated with cmc values.…”

Section: Introductionmentioning

confidence: 99%

Prediction of critical micelle concentration of cationic surfactants using connectivity indices

Mozrzymas

Różycka‐Roszak

2010

J Math Chem

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Relationship for predicting Logcmc for cationic surfactants having chloride as counterion from only molecular connectivity indices was found. It is suggested that the index 0 χ ν includes some information about hydrophobicity while indices 4 χ pc and 4 χ ν pc include some information about hydrophilicity of the cationic surfactants studied. The structures of 23 compounds used for the correlation are quite diverse. Keywords Cationic surfactants • Chloride • QSPR • cmc • Molecular connectivity indices Abbreviations and Symbols QSPR Quantitative Structure-Property Relationship cmc Critical micelle concentration r Correlation coefficient F Fisher ratio value m χ Connectivity index m-th order m χ ν Valence connectivity index m-th order δ i Connectivity degree δ ν i Valence connectivity degree Z ν Number of valence electrons Z Total number of electrons in the i-th atom h Number of hydrogen atoms

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“…Both of these QSPR models were obtained using MLR approach over a set of descriptors based on molecular topology and constitution. Apart from the work of Anoune et al [91], QSPR models intended to the CMC prediction were trained over specific chemical families that are: nonionic [89,[92][93][94][95][96][97][98], cationic [99][100][101], and anionic [90,94,[102][103][104][105][106][107] surfactants. Table 1 presents statistical coefficients of QSPR models listed in the literature.…”

Section: Cmc Predictionmentioning

confidence: 99%

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confidence: 99%

Prediction of Surfactants’ Properties using Multiscale Molecular Modeling Tools: A Review

Creton

Nieto‐Draghi

Pannacci

2012

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Oil & Gas Science and Technology -Rev. IFP Energies nouvelles, Vol. 67 (2012), No. 6, pp. 969-982 Copyright c 2013, IFP Energies nouvelles DOI: 10.2516 Prediction of Surfactants' Properties using Multiscale Molecular Modeling Tools: A Review B. Creton, C. Nieto-Draghi and N. Pannacci Bois-Préau, 92852 Rueil-Malmaison -France e-mail: benoit.creton@ifpen.fr -carlos.nieto@ifpen.fr -nicolas.pannacci@ifpen.fr Résumé -Prédiction de propriétés des tensioactifs à l'aide d'outils de modélisation moléculaire : une revue -Une des voies possibles de récupération assistée du pétrole, l'EOR (Enhanced Oil Recovery), consiste en l'injection d'un fluide ASP (Alkaline/Surfactant/Polymer) dans le réservoir dans le but de déplacer le pétrole piégé vers le puits de production. La conception et/ou l'optimisation de mélanges ASP, de tensioactifs ou de mélanges de tensioactifs est donc d'un intérêt premier pour améliorer l'efficacité d'un tel procédé. Les codes de simulation moléculaire développés et largement validés durant ces dernières décennies apparaissent comme des outils incontournables pour la compréhension des effets microscopiques, la prédiction de propriétés de tensioactifs complexes ou encore l'optimisation des structures voire de la composition de mélanges de tensioactifs. Dans cet article, nous présentons une revue des travaux de la littérature sur le potentiel de diverses techniques de simulation moléculaire pour la prédiction de propriétés structurales ou thermophysiques des tensioactifs. Les techniques de simulation auxquelles nous nous sommes intéressés sont la dynamique moléculaire (MD), les simulations Monte Carlo (MC), la dissipative particle dynamics (DPD) ainsi que des approches statistiques faisant un lien direct entre structure et propriété (QSPR, pour Quantitative Structure-Property Relationship). IFP Energies nouvelles, 1-4 Avenue de Abstract -Prediction of Surfactants' Properties using Multiscale Molecular Modeling Tools: A Review -During one of the existing Enhanced Oil Recovery (EOR) procedures, a mixture of Alkaline/Surfactant/Polymer (ASP) is injected into wells in order to INTRODUCTIONThe actual capacity of oil extraction still remains limited and can be roughly estimated to 30-60%, or more, of the reservoir's original oil within the considered field [1]. The production process is typically split into tree distinct phases: -the primary recovery which is the consequence of natural effects such as the pressure of the reservoir; -the secondary recovery which consists in injecting water or gas to move the oil to the wellbore; -the tertiary recovery or Enhanced Oil Recovery (EOR), which gathers techniques such as thermal recovery, chemical or microbial injection [2][3][4]. The chemical injection technique can involve combinations of Alkaline/Surfactant/Polymer (ASP) in which the alkali reacts with some of the crude oil components decreasing the water/oil InterFacial/surface Tension (IFT) [5]. Surfactants are used to reduce the water/oil IFT and the role of Multiscale molecular modeling tools from qua...

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Correlation and prediction of critical micelle concentration using polar surface area and LFER methods

Cited by 19 publications

References 31 publications

New QSPR Models to Predict the Critical Micelle Concentration of Sugar-Based Surfactants

New QSPR Models to Predict the Critical Micelle Concentration of Sugar-Based Surfactants

Prediction of critical micelle concentration of cationic surfactants using connectivity indices

Prediction of Surfactants’ Properties using Multiscale Molecular Modeling Tools: A Review

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