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Cited by 262

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Coarse-grained molecular modeling of non-ionic surfactant self-assembly

Shinoda, Wataru, DeVane, Russell, Klein, Michael L.·2008·Soft Matter

Abstract

A coarse-grained (CG) molecular model for nonionic surfactants is presented. The transferability and versatility are demonstrated by applying the model to the bulk aqueous solution as well as to the air–water and oil–water interfacial systems. The model is designed to reproduce several key properties including surface/interfacial tension, bulk density, compressibility, hydration/transfer free energy as well as distribution functions obtained by all-atom molecular dynamics simulations. The CG surfactants are demonstrated to spontaneously organize into micelles, and either hexagonal or lamellar bulk structures, respectively, at the corresponding concentration and thermodynamic conditions for those phases. Moreover, even a hexagonal-to-lamellar phase transformation is attainable using the present CG model. The experimental surface pressure–area (π–A) curve for a representative surfactant monolayer at the air–water interface is well reproduced. Micelle budding is observed from an overly compressed monolayer; a phenomenon that is likely to be a reasonable finding because the hydration/transfer free energy is a fitted parameter in the model. The correct molecular partitioning together with a relatively rapid relaxation of the CG model system towards equilibrium enables the computation of self-assembled surfactant behavior at the mesoscale regime.

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Metadata

Authors
Shinoda, W., DeVane, R., & Klein, M. L.
Venue
Soft Matter
Published
2008
Volume
4
Issue
12
Pages
2454
ISSN
  • 1744-683X (ISSN-L)
  • 1744-6848
OpenAlex
W2061042170
Cited by
262
References
25
FWCI
7.59
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.