A quantitative structure-property relationship study for the prediction of critical micelle concentration of nonionic surfactants

Wang, Zhengwu; Li, Ganzuo; Zhang, Xiaoyi; Wang, Rongkai; Lou, Anjing

doi:10.1016/s0927-7757(01)00812-3

Cited by 40 publications

(29 citation statements)

References 14 publications

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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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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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Section: Cmc Predictionmentioning

confidence: 99%

mentioning

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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“…Historically, the first correlation was given by Klevens [1] who empirically found that logarithm of cmc linearly decreases with increasing length of an alkyl chain. Over the years, several general methodologies were presented for predicting the cmc values of nonionic surfactants [2][3][4], anionic surfactants [5][6][7], and cationic surfactants [8]. More recently, the connectivity indices together with other molecular descriptors were used for prediction of cmc of surfactants [2,3,9].…”

Section: Introductionmentioning

confidence: 99%

“…Over the years, several general methodologies were presented for predicting the cmc values of nonionic surfactants [2][3][4], anionic surfactants [5][6][7], and cationic surfactants [8]. More recently, the connectivity indices together with other molecular descriptors were used for prediction of cmc of surfactants [2,3,9]. Connectivity indices have been widely used as molecular structural descriptors that contain a large amount of information about the molecule, including the number of non-hydrogen atoms, the details of the electronic structure of each atom, and the molecular structure features [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Critical Micelle Concentration of Nonionic Surfactants by a Quantitative Structure – Property Relationship

Mozrzymas¹,

Różycka‐Roszak

2010

CCHTS

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A quantitative structure - property relationship (QSPR) was used to predict the critical micelle concentration (cmc) of nonionic surfactants. The relation was developed for a diverse set of 23 nonionic surfactants (r = 0.99, F-test = 1042.5) employing the connectivity and valence connectivity indices only. The molecular connectivity indices were calculated for the whole molecule which was a simple and general approach.

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“…Another QSPR study that correlates different parameters particularly, Keir and Hall index of zero order of the hydrophobic segment, Heat of formation, and the dipole moment of the surfactants to the critical micelle concentration of nonionic surfactants in aqueous solution. [12] In this study, seven descriptors were selected for prediction critical micelle concentration. Those were selected due to their direct physical significances on the behavior of whole chemical structure of surfactants in their aqueous solutions, in contrast with (KH0) and (IC) descriptors that are deduced from the contribution of the hydrophobic fragment to cmc.…”

Section: Introductionmentioning

confidence: 99%

QSPR for Nonionic Surfactants

Gad

2007

Journal of Dispersion Science and Technology

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A quantitative structure property relationship; QSPR was preformed as a means to predict critical micelle concentration of nonionic surfactants via correlating properties to parameters calculated from molecular structure. Such parameters; molecular weight, M w , hydrophobichydrophilic fragments molecular weight ratio, x, polarizability, a, partition coefficient, log P, energy of hydration, E H , Van der Waal's surface area, G, and dipole moment,m. Principal component analysis PCA and multiple linear regression technique MLR were performed to examine the relationship between multiple variables of the above parameters and the critical micelle concentration of nonionic surfactants. The results of PCA explain the interrelationships between log cmc and different variables. The linear relationship between the selected parameters and the critical micelle concentration was modeled according to the better statistical results. The best regressed model has highest coefficient of determination (R 2 5 0.9889), highest statistical significance (F 5 391.599), and lowest standard errors (s 2 5 0.2358). The obtained a QSPR model allows estimating of critical micelle concentration for nonionic surfactants using theoretical-calculated descriptors.Keywords Anionic surfactants, critical micelle concentration, principal component analysis, multiple linear regression, quantitative structure property relationship (QSPR) computational chemistry INTRODUCTIONObviously, the surface tension decreases as the surfactant concentration increases. Surfactant molecules first adsorbed at the liquid air interface until the surface of the solution is completely saturated. Then, the excess molecules start to from micelles over a narrow range of concentration at which critical micelle concentration is determined.Comprehensive studies were performed to understand the effect of the chemical structure of different surfactants, anionic, [1] cationic, [2,3] amphoteric, [4] and nonionic [5] on their adsorption and micellization processes and thermodynamic properties. Some empirical relationships based on experimental data between the chemical structural of surfactants and cmc have been developed for surfactant types. [6] In a previous study, the structural effect of N-alkyl sulfoacetates on surface and thermodynamic properties of their solutions has been studied. [7] It correlates the logarithm of cmc to the number of carbon atoms. log cmc ¼ A þ Bn, n is the number of carbon atoms and A and B are constants depending on temperature and homologous series. The same empirical equation was obtained for N-alkyl M-trimethylsilane Ammonium

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A quantitative structure-property relationship study for the prediction of critical micelle concentration of nonionic surfactants

Cited by 40 publications

References 14 publications

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

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

Prediction of Critical Micelle Concentration of Nonionic Surfactants by a Quantitative Structure – Property Relationship

QSPR for Nonionic Surfactants

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A quantitative structure-property relationship study for the prediction of critical micelle concentration of nonionic surfactants

Cited by 40 publications

References 14 publications

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

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

Prediction of Critical Micelle Concentration of Nonionic Surfactants by a Quantitative Structure &#x2013; Property Relationship

QSPR for Nonionic Surfactants

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Prediction of Critical Micelle Concentration of Nonionic Surfactants by a Quantitative Structure – Property Relationship