Molecular Modelling and Machine Learning to Optimize the Cellulose Nanocrystal Interface with Water and Polymers
Cellulose nanocrystals (CNCs) are unique renewable, biodegradable and non-toxic materials with impressive mechanical properties. When used to reinforce polymers, they can provide significant improvements in stiffness. The strength/weight ratio of CNCs can be as much as 8 times that of stainless steel, making them an interesting future material for automotive applications.
However, before CNCs can be used as a commercially viable reinforcing agent we have to make their interface properties similar to that of traditional polymers. One key difference between CNCs and polymers is that CNCs are highly hydrophilic, whereas polymers are highly hydrophobic. To make them compatible, we need to impart strong hydrophobicity on the CNCs through surface modification. This project, funded by Ford USA (funding body) and in collaboration with Prof Tiff Walsh from Deakin’s Institute of Frontier Materials (IFM), we look into optimising the surface modification process of the CNCs to achieve high hydrophobicity.
The project has two major steps: 1) Developing a physics-based nano-level simulation model of CNCs, led by Prof Tiff Walsh, and; 2) Using machine learning-based optimisation to develop new surface modification chemistry, led by A/Prof Santu Rana from A2I2. Once the physics-based model is developed the A2I2 team will identify surface modification techniques that, when used in the simulation model, produce the highest hydrophobicity in the treated CNCs. Once a set of design options are identified, Ford USA will evaluate them in the real world.
This is one of a handful of projects in A2I2 through which we aim to demonstrate that hard science can be complemented with advanced machine learning technology to achieve commercially successful outcomes.
For more information on this project contact Santu Rana.
Author: Santu Rana
Editor: Shannon Ryan