Characterization of dynamics and mechanism in the self-assembly of AOT reverse micelles

Abstract

Reverse micelles (RMs) are recognized as a paradigm of molecular self-assembly and used in a variety of applications, such as chemical synthesis and molecular structure refinement. Nevertheless, many fundamental properties including their equilibrium size distribution, internal structure, and mechanism of self-assembly remain poorly understood. To provide an enhanced microscopic understanding of the assembly process and resulting structural distribution, we perform multiple nonequilibrium molecular dynamics simulations of dioctyl sulfosuccinate sodium salt (AOT) RM assembly, quantifying RM size, water core structure, and dynamics. Rapid assembly of smaller RM from a random mixture is observed to establish a constant AOT water loading within a nanosecond consistent with a diffusion-adsorption mechanism validated through the Monte-Carlo simulation of a model system. The structure of RM water cores and RM molecular volume during RM assembly is characterized during the AOT assembly process. A moment-closure equation is developed from a novel master equation model to elucidate the elementary events underlying the AOT selfassembly process. The resulting kinetic model is used to explore the role of monomer addition and dissociation, RM association and dissociation, and RM collision-induced exchange, all dependent on average RM size, which provides fundamental insight regarding the mechanisms and time scales for AOT RM self-assembly. The nascent dynamics that rapidly establish water loading, intermediate time scales of RM fusion, and longer time scale dynamics of inter-RM exchange essential in establishing the equilibrium condition are quantified through these kinetic models. Overall, this work provides insight into AOT RM self-assembly and provides a general theoretical framework for the analysis of the molecular self-assembly dynamics and mechanism.

Multiscale Aggregation of the Amyloid Aβ16–22 Peptide: From Disordered Coagulation and Lateral Branching to Amorphous Prefibrils

“…Our methodological approach can be further exploited to improve the capability of modeling the amyloid aggregation process in a variety of environmental conditions including shearing and crowding, matching experimental approaches to reconstruct the energy landscape of amyloid aggregation . By following the strategy used in ref , the aggregation process could be fitted by using a master kinetic equation in order to individuate the kinetics of the key molecular steps.…”

Finite-Size Effects and Optimal System Sizes in Simulations of Surfactant Micelle Self-Assembly

“…As a way of determining which MARTINI solvent model is most appropriate for micelle self-assembly, the equilibrium distributions of micelles resulting from the large ( N = 500 DPC) simulations of each model were compared directly to the SANS profile produced by Pambou et al The systems were considered to have reached equilibrium after 2.5 μs based on the convergence of the ergodic measure of the average number of micelles present (see Figure S2a in the Supporting Information). Further analysis investigated the mean relaxation time for micelle formation for each solvent system, using the function where N m ( T ) is the equilibrium number of micelles, which ranged from 10 to 16 across the solvent systems. The stretched exponential was fit to the G ( t ) function for each solvent system.…”

“…Further analysis investigated the mean relaxation time for micelle formation for each solvent system, using the function where N m ( T ) is the equilibrium number of micelles, which ranged from 10 to 16 across the solvent systems. The stretched exponential was fit to the G ( t ) function for each solvent system. Values of the exponent α ranged from 0.65 to 0.76, consistent with the observed degree of dynamical heterogeneity.…”

“…Dodecylphosphocholine (DPC), which has a dodecyl hydrocarbon tail and a zwitterionic phosphocholine head, is a commonly used surfactant. DPC micelles have been characterized through the methods of ultracentrifugation, dynamic light scattering, NMR, − small-angle neutron scattering (SANS), and small-angle X-ray scattering (SAXS). , Molecular dynamics (MD) simulations of DPC micelle self-assembly have been conducted with coarse-grained (CG) − and all-atom (AA) modeling with explicit − and implicit − solvent. Fundamental aspects of the mechanism of self-assembly, the equilibrium state of micellar solutions, and the impact of micelle encapsulated impurities on micelle size, however, remain poorly understood.…”

Finite-size effects and optimal system sizes in simulations of surfactant micelle self-assembly

“…DPC micelles have been characterized in terms of ultracentrifugation, 1 dynamic light scattering, 1 NMR, [2][3][4] small-angle neutron scattering (SANS), 5 and small-angle X-ray scattering (SAXS). 6,7 Molecular dynamics (MD) simulations of DPC micelle self-assembly have been conducted with coarse-grained (CG) [8][9][10] and all-atom (AA) modeling with explicit [11][12][13][14][15][16] and implicit [17][18][19] solvent. Fundamental aspects of the mechanism of self-assembly, the equilibrium state of micellar solutions, and the impact of micelle encapsulated impurities on micelle size, however, remain poorly understood.…”