Date: Wednesday 19th of October 2016, 14:00.
Location: JBL0C05 (Joseph Banks Laboratories).
‘Modelling polymer crystallisation under flow: from molecular shape to flow properties and crystallisation’
by Dr Richard Graham,
School of Mathematical Sciences,
University of Nottingham, Nottingham, (UK).
Polymer molecules, due to their size, move much more slowly than simple molecules. They are sufficiently slow that flow can unravel individual chains. This molecular deformation leads to flow properties that are richly non-linear and strongly non-Newtonian. Furthermore, molecular deformation drastically increases the rate of crystallisation in polymers and changes the resulting crystal structures. By distorting the configuration of polymer chains, flow breaks down the kinetic barriers to crystallisation and directs the resulting crystallisation. These effects are of central importance to the polymer industry as crystallisation determines virtually all of the useful properties of polymer products. However, modelling polymer crystallisation is extremely challenging due to the huge spread in relevant lengthscales and timescales. Furthermore, the most pronounced crystallisation effects are seen at low undercooling. In this temperature regime the nucleation of small crystals, from which bulk crystallisation occurs, is extremely slow. This makes crystallisation especially difficult to simulate because the nucleation dynamics are controlled by extremely rare activated crossing of the nucleation barrier.
We have recently been using a highly coarse-grained simulation algorithm for polymer nucleation. This has provided some encouraging comparisons with experiments. Nevertheless, an extended multiscale approach will be needed to simultaneously include the correct molecular physics, while also producing models that are sufficiently tractable for use in computational modelling of polymer processing. I will summarise current results and discuss methods of increasing the speed of barrier crossing simulations, along with techniques to map simulation algorithms on to non-stochastic models. Finally, I will also highlight some possible future methods to increase the physical detail of the underlying polymer nucleation model.