Normal and Shear Forces Between Polymer Brushes

  • Gary S. Grest
Chapter
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Part of the Advances in Polymer Science book series (POLYMER, volume 138)

Abstract

Surface-polymer interactions are important in many technological applications, including colloidal stablization and adherence. Recently there has been considerable progress in understanding these interactions and the resulting forces between polymer-bearing surfaces. End-grafted polymers, commonly referred to as polymer brushes, are one example of a polymer-surface complex which has many interesting properties. In this article, recent progress in understanding the normal and shear forces between polymer brushes is reviewed with emphasis on the contributions from molecular simulations. These simulations show that under steady-state shear flow, some of the individual chains of a polymer brush stretch in the direction of flow while most are buried inside of the brush and are not affected by the shear flow. The height of the brush is only weakly dependent on the shear rate in contrast with several theoretical models. When two surfaces bearing end-grafted chains are brought into contact the normal force increases rapidly with decreasing plate separation, while the shear force is in most cases significantly smaller, particularly for large compressions. However, for weak compression, the range and the magnitude of the shear force depends on both the solvent quality and shear rate. These results, first observed experimentally using the surface force apparatus and recently confirmed in simulation, suggest a way to dramatically reduce the frictional force between two surfaces. For small relative velocity of the two surfaces, the surfaces slide pass each other with almost no change in the average radius of gyration of the chains or the amount of interpenetration of chains from the two surfaces. However, for large shear rates, there is significant stretching and some disentanglement of the chains.

Keywords

Shear Rate Shear Force Normal Force Chem Phys Polymer Brush 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Gary S. Grest
    • 1
  1. 1.Corporate Research Science LaboratoriesExxon Research & Engineering CompanyAnnandaleUSA

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