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

Mozrzymas, Anna; Różycka‐Roszak, Bożenna

doi:10.2174/138620710790218195

Cited by 19 publications

(15 citation statements)

References 11 publications

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“…In the process of searching for the simple relationship were used, just as in the previous papers [8, 12], ten indices: five molecular connectivity indices and five valence molecular connectivity indices, from zeroth to fourth order in each case. These indices were calculated for the compounds studied (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Not long ago, a quantitative structure–property relationship (QSPR) was used for predicting the cmc values of conventional non-ionic [5–8] and ionic [9–12] surfactants. The values of the cmc of gemini surfactants can be significantly changed by a slight modification of the structure of the molecule; therefore modelling and predicting the critical micelle concentration of gemini surfactants directly from the structure of the molecule by the QSPR analysis can be of great interest.…”

Section: Introductionmentioning

confidence: 99%

“…The previous QSPR models [8, 12] show that critical micelle concentration can be correlated and predicted by using the molecular connectivity indices only. In the present work cationic gemini surfactants are taken into consideration, and just as in the previous papers, in the QSPR study ten indices are used: five connectivity indices and five valence connectivity indices, from zeroth to fourth order in both cases.…”

Section: Introductionmentioning

confidence: 99%

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Modelling of the Critical Micelle Concentration of Cationic Gemini Surfactants Using Molecular Connectivity Indices

Mozrzymas

2013

J Solution Chem

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Modelling of the critical micelle concentrations (cmc) using the molecular connectivity indices was performed for a set of 21 cationic gemini surfactants with medium-length spacers. The obtained model contains only the second-order Kier and Hall molecular connectivity index. It is suggested that the index 2χ includes some information about flexibility. The obtained model was used to predict log10 cmc of other cationic gemini surfactants. The agreement between calculated and experimental values of log10 cmc for the gemini surfactants that were not used in the correlation is very good.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling of the Critical Micelle Concentration of Cationic Gemini Surfactants Using Molecular Connectivity Indices

Mozrzymas

2013

J Solution Chem

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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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On the hydrophobic chains effect on critical micelle concentration of cationic gemini surfactants using molecular connectivity indices

Mozrzymas

2020

Monatsh Chem

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The influence of the structure of hydrophobic tail chains on the critical micelle concentration of cationic gemini surfactants, using only the molecular connectivity indices, has been investigated in this work. The best model obtained shows that the relationship between the logarithm of critical micelle concentration and the alkyl chains length is parabolic. The formula has been derived for compounds with the same head groups and the same, medium length, spacer but with various hydrocarbon tail chains. Good-quality QSPR model obtained can be used to predict the critical micelle concentration value of structurally similar gemini surfactants as well as to design the structure of the hydrophobic tail chains to obtain new molecules more active in micelle formation. Graphic abstract

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

Cited by 19 publications

References 11 publications

Modelling of the Critical Micelle Concentration of Cationic Gemini Surfactants Using Molecular Connectivity Indices

Modelling of the Critical Micelle Concentration of Cationic Gemini Surfactants Using Molecular Connectivity Indices

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

On the hydrophobic chains effect on critical micelle concentration of cationic gemini surfactants using molecular connectivity indices

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