Hostname: page-component-76fb5796d-25wd4 Total loading time: 0 Render date: 2024-04-26T13:08:45.735Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling the Rheology of Commercial Long Chain Branched Polymer Melts

Published online by Cambridge University Press:  01 February 2011

Seung Joon Park  and
Ronald G. Larson
Seung Joon Park
Affiliation:
Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Ronald G. Larson
Affiliation:
Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Get access

Abstract

The predictions of a general “hierarchical model” for the rheology of general mixtures of linear and branched polymers are compared to experimental data for well-defined long-chain branched polymers. For a wide range of branched polymer melts, which include star-linear blends, H-polymers, and comb polymers, the predictions of the model agree well with experimental data. We apply the hierarchical model to metallocene-catalyzed polyethylenes (mPEs), in which the branched structures are generated by a Monte Carlo method based on the known reaction kinetics. The hierarchical model captures the shape of the curves of viscoelastic moduli vs. frequency of mPEs well and predicts accurately the effect of long chain branching on the linear viscoelastic properties. The quantitative agreement of the hierarchical model prediction with experimental data of well-defined long-chain branched polymers and mPEs shows that information on branching structure could be inferred from rheological measurements on combinatorial sets of mixtures of an unknown branched with different combinations of linear polymers.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 804: Symposium JJ – Combinatorial and Artificial Intelligence Methods in Materials Science II , 2003 , JJ4.3
Copyright
Copyright © Materials Research Society 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Janzen, J. and Colby, R. H., J. Mol. Struct. 485–486, 569 (1999).Google Scholar
2. Larson, R. G., Macromolecules 34, 4556 (2001).Google Scholar
3. Milner, S. T. and McLeish, T. C. B., Macromolecules 30, 2159 (1997).Google Scholar
4. Milner, S. T., McLeish, T. C. B., Young, R. N., Hakiki, A., and Johnson, J. M., Macromolecules 31, 9345 (1998).Google Scholar
5. Park, S. J., Shanbhag, S., and Larson, R. G., Macromolecules (2003) (submitted).Google Scholar
6. Wood-Adams, P. M., Dealy, J. M., deGroot, A. W., and Redwine, O. D., Macromolecules 33, 7489 (2000).Google Scholar
7. Park, S. J. and Larson, R. G., R. G. J. Rheol. 47, 199 (2003).Google Scholar
8. Soares, J. P. and Hamielec, A. E., Macromol. Theory Simul. 5, 547 (1996).Google Scholar
9. Costeux, S., Wood-Adams, P., Beigzadeh, D., Macromolecules 35, 2514 (2002).Google Scholar
10. Struglinski, M. J., Graessley, W. W., and Fetters, W. W., Macromolecules 21, 783 (1988).Google Scholar
11. McLeish, T. C. B., Allgaier, J., Bick, D. K., Bishko, G., Biswas, P., Blackwell, R., Blottiere, B., Clarke, N., Gibbs, B., Groves, D. J., Hakiki, A., Heenan, R. K., Johnson, J. M., Kant, R., Read, D. J., and Young, R. N., Macromolecules 32, 6734 (1999).Google Scholar
12. Daniels, D. R., McLeish, T. C. B., Crosby, B. J., Young, R. N., and Fernyhough, C. M., Macromolecules 34, 7025 (2001).Google Scholar
13. Crosby, B. J., Magnus, M., deGroot, W., Daniels, R., and McLeish, T. C. B., J. Rheol. 46, 401 (2002).Google Scholar