Nick Warren is an Associate Professor at School of Chemical and Process Engineering at the University of Leeds. He was awarded an Masters in Chemistry from the University of Bristol in 2005 following which he conducted two years industrial research. He then moved to the University of Sheffield where he obtained a PhD in Polymer Chemistry. He continued as a postdoctoral researcher in Sheffield working in the area of polymerisation-induced self-assembly (PISA) until 2016, when he moved to Leeds to start his independent research career. His research group aims to design a new generation of sustainable and functional polymer materials by exploiting the latest advances in both polymer chemistry and self-optimising reactor technologies equipped with advanced online monitoring and computer control. He can be found on Twitter @njwarren1.
Read Nick’s Emerging Investigator article ‘Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation’.
How do you feel about Polymer Chemistry as a place to publish research on this topic?
The vision of our research group is to develop technologies which aim to enhance precision and reproducibility in polymer synthesis and it is therefore vital that we target polymer chemists directly. Polymer Chemistry is the ideal avenue for this, and we hope it encourages adoption of new technologies in polymer synthesis labs around the world. Hopefully over the next few years, we can work with others to discover new materials with our platforms by implementing them for more technically demanding polymerisation processes.
What aspect of your work are you most excited about at the moment and what do you find most challenging about your research?
The ability to control our systems remotely, means we anticipate that networks of reactors in different labs around the world can communicate via cloud computing to optimise and discover new polymers. We are really excited by the fact that this is bringing artificially intelligent approaches to polymer discovery one step closer!
There are many advantages that flow chemistry affords here, but the challenges associated with polymer solutions in flow means a lot of work is required to optimise the reactor geometries and to provide consistent mixing. However, by working with fluid dynamics experts we are beginning to address these problems, which have traditionally been a major barrier. We are also keen to enable multi-step processes, without human intervention with each characterised in real-time. This includes post-polymerisation processing, and purification. There are also significant challenges in dealing with all sorts of data, which means we’re going to need to tailor our machine learning algorithms to accept this – essentially teaching robots how to do polymer synthesis!