Developing Digital Twins for Microfluidic Nanoparticle Manufacture

The manufacture of nanoparticles via microfluidics offers unparalleled control over particle size, composition, and functionality, enabling breakthroughs in drug delivery, diagnostics, and materials science. However, optimizing these systems remains a challenge due to the complex interplay of constituent material properties and process parameters with fluid flow and molecular self-assembly.

This project aims to develop a digital twin framework to simulate, optimize, and predict microfluidic nanoparticle production.

Process models for microfluidic nanoparticle manufacture will be developed, informed by in situ experimental data to capture formation and performance of complex multicomponent nanoparticle systems, such as lipid nanoparticles (LNP). Experimental data from this and other aligned projects will be used to build and validate prototype digital twins to capture formation processes and application performance of nanoparticles within the quality by digital design (QbDD) framework.

Digital twins will be based on hybrid approaches using both physics based and data driven models, capturing relevant properties and their variability. They will identify critical process parameters (CPPs) and their correlation with the critical quality attributes (CQAs) of the formulated nanomedicine, optimising process conditions and enhancing scalability.

By bridging the gap between physical systems and simulations, this project seeks to enable faster innovation cycles, reduce waste, and enhanced process reliability and scalability. The digital twin framework will provide a versatile tool for researchers and industry to advance future nanomedicines applications.