Flow photochemistry in single-chain polymer nanoparticle synthesis

Chain folding of synthetic polymers is achieved by intramolecular cross-linking to form a polymer architecture known as single chain polymer nanoparticles (SCNPs) which potentially can provide functions exclusively seen in folded biomacromolecules. The generation of SCNPs, however, is limited by the requirement of a high dilution chemical step, necessitating the use of large reactors to produce reasonable quantities of material. In our previous study we present how the chemical folding of macromolecules into SCNPs is achieved in both batch and flow photochemical processes by the photodimerization of anthracene units in polymethylmethacrylate under UV irradiation. By studying the formation of SCPNs for the first time through flow photochemical process, we provided new path towards the synthesis of larger quantities of SCNPs and thus their applications in different aspects of polymer science. As SCPNs formation by the possible path of flow photochemistry process have been studied and compared to the classical batch process, and its advantages over batch have been addressed, there is remaining the question whether it is also a more environmentally friendly process. Life cycle assessment (LCA) can provide quantitative analysis information presenting the environmental impact scores for each process, batch and flow, and each step in the processes. In addition, process modelling of solvent recovery and different solvents evaluation are considered in this study. As SCPNs materials are possible candidates in the plastic industry, material characterization at the solid state have been limited. However, at lab scale production, where the focus is mainly on the ‘proof-of-concept’ of the new developed material and less on the optimization of the process, unlike in commercial scale, there is a risk of high amount of solvents waste and high energy requirements. This study offers the observation and evaluation of different SCPNs formation through different processing paths at the lab scale using LCA tools.