Performance enhancement of polymer nanocomposites via multi-scale modelling of processing and properties

Computer aided numerical modelling is a valuable tool in materials research. It is used to predict the behaviour of materials under conditions that might be difficult to replicate experimentally either because experimentation is too costly or too difficult. It is also used to aid understanding of how the large scale, macroscopic properties of materials such as strength, are related to the nanoscale [nanometer = 1 millionth of a millimetre] structure of the material at the molecular level. If the structure at the molecular level can be related to the behaviour of the material at the macroscopic level then it becomes possible to design materials to suit our needs, e.g. to make them lighter or easier to process.Polymers, because of their properties and ease of processing into complex shapes are among the most important materials available to us today e.g. the next revolution in low cost electronics will be made possible by the use of polymers in microchips. An exciting new family of materials are the polymer nanocomposites (NCs), in which particles with nanoscale dimensions are dispersed in the polymer. The benefits of NCs derive primarily from the exceptionally large amounts of particle surface area that can be achieved for a small addition of particles (e.g. 5% by weight). Thus they offer dramatic improvement in material performance with significant increases in mechanical and gas barrier properties. The user of such a material therefore gets a more effective product (or one containing less material for the same effectiveness). This project is aimed at developing computer modelling tools to help producers of materials, and product designers and manufacturers exploit these materials to the full, much more quickly than could be done by experimental methods. In this project we will be creating computer modelling approaches that will work with any polymer matrix NC. To ensure the work is of benefit to industry we shall concentrate on applying the methods to modelling how NCs behave in manufacturing processes (stretch blow moulding and thermoforming) involving large-strain biaxial stretching of relatively thin sheets. These processes are used to make products for packaging, the automotive and medical device industry. NCs designed and processed effectively offer the chance to drastically reduce the amount of polymer needed in such cases, and therefore to help solve the environmental problem of plastics waste. The materials to be modelled will be therefore the most promising for NCs in such applications: polypropylene [PP] and polyethylene terephthalate [PET] and the nanoparticles will be derived from clays composed of layers of silicate material [platelets]. The main challenges in the project will be to find modelling techniques that will be capable of describing the material at the nanoscale level of the clay platelets [platelet size, orientation, platelet/polymer interaction] and then linking this to the behaviour of the material as it is processed into a product and finally to how the product behaves in use. This will involve the following steps (1) Determine the initial microstructure in a polymer sheet using Transmission Electron Microscopy [TEM] to produce a high resolution picture of the material. The images will then be analysed using image analysis software to produce data for material modelling. (2) Construct a model of the sheet based on the data from (1). (3) Use mathematical models to model the way in which the material will behave during forming and then incorporate this model into a simulation of the actual process used to manufacture the plastic products. (4) Run the simulation to predict processing behaviour and final part properties. (5) Validation of modelling results through experimentation. This work will be carried out by a multidisciplinary team consisting of material scientists, engineers and physicists from University of Bradford, Oxford and Queen's University Belfast, in collaboration with industry.