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Simulating the Self-Assembly of Gemini (Dimeric) Surfactants

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Karaborni, S., Esselink, K., Hilbers, P. A. J., Smit, B., Karthauser, J., van Os, N. M., Zana, R.·1994·Science

Abstract

The morphologies and dynamics of aggregates formed by surfactant molecules are known to influence strongly performance properties spanning biology, household cleaning, and soil cleanup. Molecular dynamics simulations were used to investigate the morphology and dynamics of a class of surfactants, the gemini or dimeric surfactants, that are of potential importance in several industrial applications. Simulation results show that these surfactants form structures and have dynamic properties that are drastically different from those of single-chain surfactants. At the same weight fraction, single-chain surfactants form spherical micelles whereas gemini surfactants, whose two head groups are coupled by a short hydrophobic spacer, form thread-like micelles. Simulations at different surfactant concentrations indicate the formation of various structures, suggesting an alternative explanation for the unexpected viscosity behavior of gemini surfactants.

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Metadata

Authors
Karaborni, S., Esselink, K., Hilbers, P. A. J., Smit, B., Karthauser, J., van Os, N. M., & Zana, R.
Venue
Science
Published
1994-10-14
Volume
266
Issue
5183
Pages
254-256
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W1995549179
Cited by
298
References
0
FWCI
11.70
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Simulating the Self-Assembly of Gemini (Dimeric) Surfactants. https://app.cochat.ai/papers/10.1126/science.266.5183.254
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Karaborni, S., Esselink, K., Hilbers, P. A. J., Smit, B., Karthauser, J., van Os, N. M., & Zana, R. (1994). Simulating the Self-Assembly of Gemini (Dimeric) Surfactants. Science. https://doi.org/10.1126/science.266.5183.254

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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.