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Theory of inhomogeneous polymers: Fundamentals of the Gaussian random-walk model
Abstract
In earlier work a theory of inhomogeneous polymers was developed from a mean field theory point of view. Applications were made to polymer−polymer interfaces and to the microdomain structure of block copolymers. Here a connection is made between the intuitive mean field arguments for these problems and more fundamental statistical mechanics. Functional integral techniques are employed extensively.
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Citations
Cited by
Stable and unstable phases of a diblock copolymer melt
Matsen, M. W., & Schick, M. (1994) · Physical Review Letters · doi:10.1103/physrevlett.72.2660 · Cited by 1,417
50th Anniversary Perspective: Block Polymers—Pure Potential
Bates, C. M., & Bates, F. S. (2017) · Macromolecules · doi:10.1021/acs.macromol.6b02355 · Cited by 788
Origins of complex self-assembly in block copolymers
Matsen, M. W., & Bates, F. S. (1996) · Macromolecules · doi:10.1021/ma960744q · Cited by 578
Efficient computation of the structural phase behavior of block copolymers
Tzeremes, G., Rasmussen, K. Ø., Lookman, T., & Saxena, A. (2002) · Physical Review E · doi:10.1103/physreve.65.041806 · Cited by 328
Daoulas, K. C., Theodorou, D. N., Roos, A., & Creton, C. (2004) · Macromolecules · doi:10.1021/ma035383a · Cited by 41
Metadata
- Authors
- Helfand, E.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 1975-02-01
- Volume
- 62
- Issue
- 3
- Pages
- 999-1005
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W1969837995
- Cited by
- 591
- References
- 0
- FWCI
- 6.56
- Type
- journal article
- Language
- en
- Open access
- closed
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- Helfand, E. (1975). Theory of inhomogeneous polymers: Fundamentals of the Gaussian random-walk model. The Journal of Chemical Physics. https://doi.org/10.1063/1.430517
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
