Continuous Flow Platforms for the Synthesis and Optimisation of Polymeric Materials via RAFT Polymerisation

This thesis focuses on the development and use of continuous flow platforms to perform the synthesis and subsequent chain extension of poly(dimethylacrylamide) (PDMAm), via reversible addition fragmentation chain transfer (RAFT) polymerisation, in order to obtain a range of polymeric nanoparticles. Initially, a stainless-steel flow reactor was developed and polymerisation kinetics were obtained for all RAFT polymerisations in both batch and flow reactors. Whilst good control over the polymerisations were observed for both batch and flow reactors slightly accelerated kinetics were observed in flow reactors. A range of poly(dimethylacrylamide)- poly(diacetone acrylamide) based polymerics nanoparticles were then synthesised in the flow reactor. A series of spherical micelles were successfully formed with particle size increasing with PDAAm DP. However, significant fouling was observed during the synthesis of higher order morphologies and no pure phases were obtained. A PFA flow reactor was then developed for synthesising higher order polymeric nanoparticles. At the same time polymerisation kinetics were also accelerated by using an initiator (VA-044) with a significantly higher rate of decomposition. In order to more easily monitor the accelerated reaction kinetics a benchtop NMR was placed at the reactor outlet to allow for continuous online monitoring of the polymerisation. A series of PDMAm-PDAAm spherical nanoparticles were successfully synthesised in the flow reactor in 20 minutes. When targeting higher order morphologies sphere/worm and worm/vesicle mixed phases were succesfully formed. Pure vesicle phases were only formed when high PDAAm DP (> 200) were targeted due to limited chain mobility and poor mixing in the flow reactor. Finally, an automated flow reactor platform that incorporated NMR and GPC was developed and used to monitor and screen reaction conditions for the RAFT solution polymerisations of dimethylacrylamide (DMAm) and tert-butylacrylamide. Furthermore, incorporation of an advanced machine learning algorithm (TS-EMO) allowed simultaneous self-optimisation of these polymerisations for both conversion and dispersity.