Automated On-line Optimisation of Flow Chemistry Synthesis (Reference Maskell LRC122)

Description

Functional materials should be efficient, selective, stable, and scalable if they are to be used beyond the lab. The strategic research aim of the Centre - to transform the way materials are designed and made - is to embed these concepts from the outset of all projects. Achieving this aim motivates this proposal.

While automated robotic platforms are capable of carrying out hundreds of parallel reactions, they currently lack the capacity to screen them in real-time, or to undertake any but the simplest work-up. Automated on-line optimisation is hence not possible, and an indication of the success of the material-intensive screen is not available until after it is completed. Importantly, scale-up is also often not possible from a robot synthesis without re-optimization and laborious manual synthesis. This results in long lead-times.  

Continuous flow synthesis enables quicker reactions, fine control of parameters, and, crucially, easy scale-up to industrially-relevant scenarios involving large quantities of material. A flow reactor can be programmed to sequentially carry out reactions to generate libraries of molecules in a short time-scale. However, identifying the optimal sequence of reactions to perform is challenging. At present, the material is analysed separately from the flow reactor, such that the analysis becomes the ‘rate determining step’ of the overall optimisation process. To achieve automated optimisation, there is a need to ‘close the loop’ by introducing an on-line analysis method that optimises the reactor’s parameters without any need for human intervention.

This project seeks to develop such automated techniques in the context of the control of continuous flow chemistry and thereby enable real-time, automated on-line optimisation of organic synthesis for functional materials. The project therefore strongly aligns with the Centre’s themes of organic materials, robotics & intelligent automation.

Qualifications: 2:1 Degree in Computer Science, Engineering, Statistics or a similar discipline.  

Please apply by completing the online postgraduate research application form here.

Please ensure you quote the following reference on your application: Automated On-line Optimisation of Flow Chemistry Synthesis (Reference Maskell LRC22).

Availability

Open to EU/UK applicants

Funding information

Funded studentship

The award is primarily available to students resident in the UK/EU and will pay full tuition fees and a maintenance grant for 3years (£14,553 pa in 2017/18). Non-EU nationals are not eligible for this position and applications from non-EU candidates will not be considered unless you have your own funding.
Please note that this is a PhD Graduate Teaching Assistantship (GTA) and as such will have teaching commitments and contractual obligations to teaching associated with it.  

Supervisors

References

https://www.agslatergroup.com/ 

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