Supervisor(s): Dr. Michael Thompson

Project Description: Solid-liquid dispersions consisting of polymer nanoparticles have several applications such as adhesives, coatings, and pharmaceutics. Current techniques to produce polymer dispersions normally involve hazardous solvents for particle synthesis through the cost-intensive process as extra energy is needed for the recovery of the solvent. Solvent Free Emulsification Extrusion (SFEE) is a sustainable greener technology developed by Xerox Corporation as well as some other industry leaders that eliminates solvent and produces nano-sized (100-500 nm) polymer dispersions suited to high viscous polymers (100-1000 Pa.s). However, this technique presents considerable sensitivity to various process variables making it difficult to model, troubleshoot, and implement in the industry by scaling up. Also, the inherent complexity of the process and the presence of high viscous non-Newtonian materials make it almost impossible to develop a first principle model as it is cost, time, and skill required. In this project, the aim is to further the mechanistic understanding of the process and develop a model to predict the behavior of the SFEE process using data-driven approaches such as multivariate statistical data analysis as well as machine learning techniques.