A chemist currently makes one or two batches of a new high-performance polymer per week, but an automated reactor with machine learning could produce and test dozens in the same time. This matters because living anionic polymerisation—a technique that builds polymers with atomic-level precision—is too slow and labour-intensive for rapid prototyping. Scientists must work in specialised labs under exacting conditions, manually running each batch. That bottleneck blocks the development of advanced materials for organic photovoltaics, lithium battery matrices, and new medicines. The project builds an automated reactor platform that maintains the precise conditions needed for living anionic polymerisation with minimal human input. Machine learning algorithms will then screen the resulting polymers quickly, identifying promising candidates for scale-up while preserving the precision that makes the technique valuable. If successful, this platform could collapse the timeline from lab discovery to commercial product for sustainable high-value polymers. It would allow researchers to explore far more molecular architectures than current manual methods permit, accelerating the development of materials that make electronics, energy storage, and medical technologies more efficient and affordable.
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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.
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