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Micelles, Rods, Liposomes, and Other Supramolecular Surfactant Aggregates: Computational Approaches

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Abstract

Surfactants are an interesting class of compounds characterized by the segregation of polar and apolar domains in the same molecule. This peculiarity makes possible a whole series of microscopic and macroscopic effects. Among their features, their ability to segregate particles (fluids or entire domains) and to reduce the surface/interfacial tension is the utmost important. The interest in the chemistry of surfactants never weakened; instead, waves of increasing interest have occurred every time a new field of application of these molecules has been discovered. All these special characteristics depend largely on the ability of surfactants to self-assemble and constitute supramolecular structures where their chemical properties are amplified. The possibility to obtain structural and energy information and, above all, the possibility of forecast the self-organizing mechanisms of surfactants have had a significant boost via computational chemistry. The molecular dynamics models, initially coarse-grained and subsequently (with the increasing computer power) using more accurate models, allowed, over the years, to better understand different aspects of the processes of dispersion, self-assembly, segregation of surfactant. Moreover, several other aspects have been investigated as the effect of the counterions of many ionic surfactants in defining the final supramolecular structures, the mobility of side chains, and the capacity of some surfactant to envelope entire proteins. This review constitutes a perspective/prospective view of these results. On the other hand, some comparison of in silico results with experimental information recently acquired through innovative analytical techniques such as ion mobility mass spectrometry which have been introduced.

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Indelicato, S., Bongiorno, D., Calabrese, V. et al. Micelles, Rods, Liposomes, and Other Supramolecular Surfactant Aggregates: Computational Approaches. Interdiscip Sci Comput Life Sci 9, 392–405 (2017). https://doi.org/10.1007/s12539-017-0234-7

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