Modeling of Nonionic Micelles

Wijmans, Christopher M.; Linse, Per

doi:10.1021/la00010a027

Cited by 86 publications

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“…Although the computational power has been increased using massive parallelized codes, the simulation of the aggregation process of surfactants is still a challenging task. An appropriate choice between a continuous and detailed description of the space [13][14][15][16][17], and the use of a lattice model with approximative descriptions of solvant/surfactant molecule interactions is still required [18][19][20][21][22][23]. One of the key tasks is the phase diagram computation of systems containing surfactants.…”

Section: Molecular Dynamics and Monte Carlo Simulationsmentioning

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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Section: Molecular Dynamics and Monte Carlo Simulationsmentioning

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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“…MD simulations of micelles by other groups include simple chainlike model systems (2)(3)(4) and atomic level models of real surfactants such as SDS (5), n-decyltrimethylammoniumchloride (6). The formation of micelle like aggregates in nonaqueous solvents has received less attention than the related phenomenon in water.…”

Section: Introductionmentioning

confidence: 99%

Reverse Micelle Formation Of The Dipeptide Boc- Ile-Ile-NHMe

Chellam¹,

Mandal²

2010

Int. Jour. Appl. Bio-Eng.

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The dipeptide derivative Boc-Ile-Ile-NHMe synthesized in our laboratory is found to form reverse micelles in the presence of water in chloroform solvent. Reverse micelles finds it's importance in the field of drug delivery. The critical micellar concentration (cmc) has been determined by varying the temperature for each R value, where R is the ratio of water to peptide concentration. The R value has been changed from a minimum value of 0.52 to a maximum value of 10. The aggregation number has also been determined for the micelles formed for different R values at room temperature. The change in the micropolarity of the micellar environment has also been monitored using pyrene. The thermodynamic parameters G m , H m and S m has also been determined for the system.

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“…The phase behavior of oil-water-amphiphile systems has been investigated [15][16][17]38] by Monte Carlo simulations and quasichemical theory. Moreover, the formation and properties of micelles were studied extensively for binary solvent-amphiphile systems by Monte Carlo simulations [12,13,[18][19][20][21][22][23][24][25][26][27][28][29] and single-chain mean-field theory (SCMFT) [24,30]. Some properties of micellar aggregates for oil-wateramphiphile systems were also studied by Monte Carlo simulations [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Ternary oil—water—amphiphile systems: self-assembly and phase equilibria

2002

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The micellization properties and phase behavior of model ternary surfactant -oil -water systems were studied by grand-canonical Monte Carlo simulations assisted by histogramreweighting techniques. Larson's lattice model was used for a symmetrical H 4 T 4 surfactant, where H represents hydrophilic and T hydrophobic groups. In contrast to earlier studies, we studied oil chains of variable length, from T to T 4 , and determined quantitatively the effect of added oil on the critical micelle concentration (cmc). It was found that the cmc decreases as the volume fraction of oil in the aqueous phase is increased. Longer oil chains have a stronger effect than shorter chains at the same volume fraction. The oil, tail and head density profiles were obtained for typical micelles. Simulation results for the density profiles indicate that long-chain oils are almost exclusively located within the micellar aggregates, in agreement with single-chain mean-field theory predictions. Ternary phase diagrams were obtained both by simulations and by quasichemical theory. The results suggest that the critical micelle concentration line is directed towards the oil-lean corner of the three-phase triangle, if there is one present in the system. It was also found that the amount of oil that can be dissolved in the ternary system increases sharply at the surfactant critical micelle concentration, indicating that micellization greatly enhances oil solubility.

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Modeling of Nonionic Micelles

Cited by 86 publications

References 0 publications

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

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

Reverse Micelle Formation Of The Dipeptide Boc- Ile-Ile-NHMe

Ternary oil—water—amphiphile systems: self-assembly and phase equilibria

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