Polymer brushes for friction control: Contributions of molecular simulations

When polymer chains are grafted to solid surfaces at sufficiently high density, they form brushes that can modify the surface properties. In particular, polymer brushes are increasingly being used to reduce friction in water-lubricated systems close to the very low levels found in natural systems, such as synovial joints. New types of polymer brush are continually being developed to improve with lower friction and adhesion, as well as higher load-bearing capacities. To complement experimental studies, molecular simulations are increasingly being used to help to understand how polymer brushes reduce friction. In this paper, we review how molecular simulations of polymer brush friction have progressed from very simple coarse-grained models toward more detailed models that can capture the effects of brush topology and chemistry as well as electrostatic interactions for polyelectrolyte brushes. We pay particular attention to studies that have attempted to match experimental friction data of polymer brush bilayers to results obtained using molecular simulations. We also critically look at the remaining challenges and key limitations to overcome and propose future modifications that could potentially improve agreement with experimental studies, thus enabling molecular simulations to be used predictively to modify the brush structure for optimal friction reduction.

Automated summary

When polymer chains are densely grafted to solid surfaces, they form brushes that can alter surface properties and reduce friction in water-lubricated systems. Molecular simulations have been used to understand the mechanisms of friction reduction and improve the design of polymer brushes. This review focuses on the progress in molecular simulations of polymer brush friction, from simple models to more detailed ones that capture brush topology, chemistry, and electrostatic interactions. Matching experimental data and addressing remaining challenges are discussed to enhance the predictive capability of molecular simulations for optimizing brush structures and reducing friction.