Archive for April, 2022

Paper of the month: The difference between photo-iniferter and conventional RAFT polymerization: high livingness enables the straightforward synthesis of multiblock copolymers

Lehnen et al. highlight the role of reversible deactivation as a key difference between photo-iniferter and conventional RAFT polymerization.

The use of light has become increasingly widespread in diverse polymerization approaches including reversible-addition fragmentation chain-transfer (RAFT) strategies. Among these, the photo-iniferter (PI)-RAFT polymerization in which light directly activates the chain transfer agent (CTA), has been shown to overcome several of the restrictions of conventional RAFT resulting in increased chain end fidelity. In this context, reversible deactivation is accepted to determine the fate of the growing radical via pathways that need to be understood to offer the means to further push the limits of PI-RAFT polymerization.  

To address this, Hartlieb and collaborators studied the PI-RAFT using an acrylamide (N-acryloyl morpholine) and a xanthate ((2-((ethoxycarbonothioyl)thio)propionic acid)). This monomer-CTA pair combination was selected on the basis of the low chain transfer capabilities (Ctr < 1) expected to result in high dispersities (>1.5). When targeting different degrees of polymerization (DP), the control over the molecular weight distribution was not found to significantly increase. However, control could be achieved through slow monomer addition that results in increasing the numbers of activation-deactivation events per monomer addition. Importantly, the high livingness associated with PI-RAFT proved to be invaluable in chain extension experiments since it was found to enable the straightforward, easy and rapid synthesis of very high molecular weight multiblock copolymers with up to 20 blocks and a high number of repeating units per block (DP = 25-100) with impressive precision.  

In summary this study highlights the role of reversible deactivation and employs the high livingness of PI-RAFT to demonstrate its enormous potential for the synthesis of polymeric materials and more specifically segmented macromolecules.

Tips/comments directly from the authors:

  • We want to emphasize how fast and easy polymerization reactions can be performed using this technique as the shown xanthate is an extremely powerful iniferter
  • The shown multiblocks were produced in a very straight forward way; no rigorously clean or inert conditions or specialized equipment.
  • The photo-iniferter process is older than RAFT polymerization but its full potential isn’t used yet.  

 

The difference between photo-iniferter and conventional RAFT polymerization: high livingness enables the straightforward synthesis of multiblock copolymers, Polym. Chem., 2022, 13, 1537-1546

Link to the paper: https://pubs.rsc.org/en/content/articlelanding/2022/py/d1py01530c

Link to Dr Matthias Hartlieb’s group website: https://www.uni-potsdam.de/polybio

You can follow Dr Matthias Hartlieb on Twitter: @PolyBioPotsdam

 

Dr. Kelly Velonia is an Advisory Board Member and a Web Writer for Polymer Chemistry. She joined the Department of Materials Science and Technology in 2007. Research in her group focuses on the synthesis and applications of bioconjugates and biopolymers.

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Polymer Chemistry Emerging Investigator – Nicholas Warren

Profile picture of Nicholas WarrenNick 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!

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