Autonomous Self-Optimising Continuous Flow Reactors for Precision Polymer Synthesis

This project aims to develop a platform that utilises machine learning algorithms and continuous flow; that can find optimum conditions for a variety of high value polymers for high-throughput synthesis. Such platforms are well studied for small molecules but less so for macro-molecules like polymers. The main optimisation objectives for polymerisations are low dispersity and high conversion polymers, in the last decade, the reaction times of methods such as RAFT have been reduced down from hours to minutes making them suitable for high-throughput flow systems. This is done by using initiators with short half-lives in certain solvents. Many complications render polymerisation difficult to carry out in flow, such as, laminar velocity flow profiles affecting dispersity and causing fouling. The aims of this project are to build upon a current multi-variable, multi-objective reactor that is currently limited by the number of variables that can be used in the optimisation. The current reactor uses a one-pot solution of reagents which are pumped through a heated coil into an inline GPC and benchtop NMR, the algorithm finds a set of conditions that give low dispersity and high conversion. As the algorithm works on a set of training points to produce a surrogate function no a priori knowledge is needed. The development of this platform would use multiple pumps to carry out optimisations to find the ideal reagent ratios so other objectives such as molecular weight and reaction time can be investigated - with an aim to form a "Dial-A-Polymer" platform, allowing custom polymers to be produced and to find the best set of conditions to make them rapidly and precisely with a few clicks.