Surfactant micellization and micellar solubilization in aqueous solution can be modeled using a molecular-thermodynamic (MT) theoretical approach; however, the implementation of MT theory requires an accurate identification of the portions of solutes (surfactants and solubilizates) that are hydrated and unhydrated in the micellar state. For simple solutes, such identification is comparatively straightforward using simple rules of thumb or group-contribution methods, but for more complex solutes, the hydration states in the micellar environment are unclear. Recently, a hybrid method was reported by these authors in which hydrated and unhydrated states are identified by atomistic simulation, with the resulting information being used to make MT predictions of micellization and micellar solubilization behavior. Although this hybrid method improves the accuracy of the MT approach for complex solutes with a minimum of computational expense, the limitation remains that individual atoms are modeled as being in only one of two states-head or tail-whereas in reality, there is a continuous spectrum of hydration states between these two limits. In the case of hydrophobic or amphiphilic solutes possessing more complex chemical structures, a new modeling approach is needed to (i) obtain quantitative information about changes in hydration that occur upon aggregate formation, (ii) quantify the hydrophobic driving force for self-assembly, and (iii) make predictions of micellization and micellar solubilization behavior. This article is the first in a series of articles introducing a new computer simulation-molecular thermodynamic (CS-MT) model that accomplishes objectives (i)-(iii) and enables prediction of micellization and micellar solubilization behaviors, which are infeasible to model directly using atomistic simulation. In this article (article 1 of the series), the CS-MT model is introduced and implemented to model simple oil aggregates of various shapes and sizes, and its predictions are compared to those of the traditional MT model. The CS-MT model is formulated to allow the prediction of the free-energy change associated with aggregate formation (gform) of solute aggregates of any shape and size by performing only two computer simulations-one of the solute in bulk water and the other of the solute in an aggregate of arbitrary shape and size. For the 15 oil systems modeled in this article, the average discrepancy between the predictions of the CS-MT model and those of the traditional MT model for gform is only 1.04%. In article 2, the CS-MT modeling approach is implemented to predict the micellization behavior of nonionic surfactants; in article 3, it is used to predict the micellization behavior of ionic and zwitterionic surfactants.
A predictive molecular-thermodynamic theory is developed to model the effect of counterion binding on micellar solution properties of binary surfactant mixtures of ionic and nonionic (or zwitterionic) surfactants. The theory combines a molecular-thermodynamic description of micellization in binary surfactant mixtures with a recently developed model of counterion binding to single-component ionic surfactant micelles. The thermodynamic component of the theory models the equilibrium between the surfactant monomers, the counterions, and the mixed micelles. The molecular component of the theory models the various contributions to the free-energy change associated with forming a mixed micelle from ionic surfactants, nonionic (or zwitterionic) surfactants, and bound counterions (referred to as the free energy of mixed micellization). Specifically, the various molecular contributions to the free energy of mixed micellization model the underlying physics associated with the assembly of, and the interactions between, the surfactant polar heads, the surfactant nonpolar tails, and the bound counterions. Utilizing known structural characteristics of the surfactants and the counterions, along with the solution conditions, the free energy of mixed micellization is minimized to predict various optimal micelle characteristics, including the degree of counterion binding, the micelle composition, and the micelle shape and size. These predicted optimal micelle characteristics are then used to predict the critical micelle concentration (cmc) and the average micelle aggregation number. Our predictions of the degree of counterion binding, the cmc, and the average micelle aggregation number show good agreement with available experimental results from the literature for several binary surfactant mixtures. In addition, the theory is used to shed light on the relationship between the micelle composition, counterion binding and ion condensation, and the micelle shape transition.
DataRail is distributed under the GNU General Public License and available at http://code.google.com/p/sbpipeline/
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