Automated Flow Synthesiser for Block, Block-statistical, and Gradient Copolymers Material Libraries Construction

This project accelerates polymer material discovery by integrating digital chemistry into an autonomous, closed-loop experimentation framework. By leveraging RAFT polymerisation and flow chemistry, polymers with sophisticated architectures are synthesised in a high-throughput manner, and machine learning models are used to map the complex chemical space and the resulting polymer properties. The developed platforms utilise real-time analytics and chemometric techniques to continuously monitor polymerisation kinetics and provide feedback to an AI model to efficiently guide material synthesis. The success achieved in this work represents a significant milestone toward the realisation of a data-driven materials discovery paradigm and self-driving laboratory in the future.