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Cited by 370

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Density-Functional Theory for Complex Fluids

Wu, Jianzhong, Li, Zhidong·2007·Annual Review of Physical Chemistry

Abstract

Density-functional theory (DFT) and its variations provide a fruitful approach to the computational modeling of the microscopic structures and phase behavior of soft-condensed matter. The methodology takes deep root in quantum mechanics but shares a mathematical similarity with a number of classical approaches in statistical mechanics. This review discusses different strategies commonly used to formulate the free-energy functional of complex fluids for either phenomena-oriented applications or as a generic description of the thermodynamic nonideality owing to various components of intermolecular forces. We emphasize the connections among different schemes of DFT approximations, their underlying assumptions, and inherent limitations. We also address extensions of equilibrium DFT to phenomenological theories for the dynamic properties of complex fluids and for the kinetics of phase transitions. In addition, we highlight the recent literature concerning applications of DFT to diverse static and time-dependent phenomena in complex fluids.

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Metadata

Authors
Wu, J., & Li, Z.
Venue
Annual Review of Physical Chemistry
Published
2007-05-01
Volume
58
Issue
1
Pages
85-112
ISSN
  • 0066-426X (ISSN-L)
  • 1545-1593
OpenAlex
W2170267434
Cited by
370
References
1
FWCI
0.96
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Density-Functional Theory for Complex Fluids. https://app.cochat.ai/papers/10.1146/annurev.physchem.58.032806.104650
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Wu, J., & Li, Z. (2007). Density-Functional Theory for Complex Fluids. Annual Review of Physical Chemistry. https://doi.org/10.1146/annurev.physchem.58.032806.104650

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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.