Understanding hierarchical structures and behaviours of polymeric nanocomposites (PNCs) is essential to achieve optimum improvement in the properties of PNCs for a wide range of applications. To control the process of material synthesis, it is vital to employ computational strategies that can accurately predict the properties of candidate materials. Therefore, this chapter reviews the computational approaches to predicting the behaviours of PNCs. A general approach to modelling the physical and mechanical properties of PNCs in terms of analytical and numerical techniques is first presented, with a view to understand modelling approaches at different levels of complexities, lengths, and time scales, such as molecular, microscale, mesoscale, and macroscale. Then, specific attention is given to multiscale hierarchical modelling of PNCs to highlight techniques to bridge the gap between numerical and analytical models at different scales. This chapter further considers various aspects of emerging applications of single scale and multiscale numerical techniques to nanostructure systems. Lastly, it discusses current challenges encountered in the application of computational methods to improve the performance of polymeric nanomaterials in line with future prospects, prior to the concluding remarks.
|Title of host publication||Hybrid Polymeric Nanocomposites from Agricultural Waste|
|Editors||S. A. Bello, B. I. Kharissov|
|Publisher||CRC Press, Taylor & Francis Group|
|Number of pages||39|
|Publication status||Published - 27 Oct 2022|