Details of Grant 

EPSRC Reference:	EP/Z002532/1
Title:	Autonomous Reactors for Accelerating the route from Bench-to-Shelf for Sustainable High-Value Polymer Materials
Principal Investigator:	Warren, Dr N J
Other Investigators:	Kapur, Professor N Bourne, Professor RA
Researcher Co-Investigators:
Project Partners:	Infineum UK Ltd Synthomer Ltd
Department:	Chemical and Process Engineering
Organisation:	University of Leeds
Scheme:	Standard Research - NR1
Starts:	01 September 2024	Ends:	28 February 2026	Value (£):	258,134
EPSRC Research Topic Classifications:	Manufacturing Machine & Plant Materials Processing
EPSRC Industrial Sector Classifications:	Manufacturing
Related Grants:
Panel History:
Summary on Grant Application Form
Polymers play a vital role in our daily lives and we continuously encounter polymers that are specifically designed and optimised for optimal performance. They are present in various aspects of our lives, such as clothing, computer displays, and medical technologies. However, in order to maintain a sustainable and healthy society, we need advanced solutions that offer higher performance and new capability that are affordable. They could also pave the way for innovative materials that open doors to new medicines, advanced lubricants, organic photovoltaics, and lithium battery matrix technologies. Living anionic polymerisation is a highly precise chemical synthesis technique that can be used to make these polymers, allowing for an array of molecular architectures. However, there is a lack of efficient methods to quickly screen polymers synthesised using this technique. Currently, it is only carried out in specialised laboratories equipped with the necessary infrastructure and skilled personnel to meet the rigorous experimental conditions. Due to this, scientists will make only one or two batches of material per week meaning rapid prototyping is impossible. Here, we will develop a platform technology which facilitates synthesis of polymers by LAP using an automated reactor platform which can maintain precise conditions with minimal human input. By equipping this instrumentation with machine learning capability, we will demonstrate an ability to rapidly screen polymers and demonstrate the ability to scale-up whilst maintaining the precision required. This technology will precipitate an array of opportunities for developing new sustainable materials which can contribute to solving challenges facing society.
Key Findings
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Potential use in non-academic contexts
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Further Information:
Organisation Website:	http://www.leeds.ac.uk