PhD Studentship - Developing Accurate Data Analysis Methodologies for Raman Spectroscopy Quality Control of Advanced Materials

Research theme: Nanomaterials

This 4 year PhD is part of the Faculty of Science and Engineering and National Physical Laboratory collaborative programme. Tuition fees will be paid and you will receive a tax free stipend set at the UKRI rate (£19,237 for 2024/25). This is open to UK students and those with settled status. The start date is September 2024.

The challenge to meet the UK’s Net-Zero emissions target will require significant innovation in advanced materials, graphene and related 2D materials offer a route to achieving these goals, with applications already demonstrated in reinforcing concrete for structural applications. To provide confidence in the materials being used it is critical that industry have measurement methods to ensure product quality that are traceable back to standardised measurement methods.

This Advanced Materials and Data Science project will develop data analysis methods for Raman spectra of commercially produced graphene, building on previous scoping work recently published by the University of Manchester. Raman spectroscopy is widely used in graphene characterisation and is well-placed to be used as an in-line method due to its short measurement time and non-destructive nature. Machine learning (ML) tools will be evaluated and developed to simplify and speed-up the analysis and interpretation of the data. These tools will be validated against existing, standardised data analysis methods to ensure the novel methods are robust. As well as faster analysis, the results will be correlated to additional characterisation measurement results such as SEM, AFM, XPS and other appropriate techniques.

This project will have access to the fabrication and characterisation facilities available within the University of Manchester’s Department of Materials, National Graphene Institute (NGI), and Henry Royce Institute. This project will also be delivered in collaboration with the National Physical Laboratory (NPL), the UK’s National Measurement Institute. The successful candidate will be supported and trained by experts at NPL and will have access to facilities and equipment relevant to the project. They will also gain access to professional development opportunities, training and support offered through the Postgraduate Institute for Measurement Science (PGI).

This project will require time to be invested in developing the machine learning tools and optimising analysis based on practical sampling methodologies that can be applied at scale. The project will also strengthen the existing collaborations both with the University and with industrial partners and is likely to lead to further opportunities for joint research.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Before you apply, please contact the supervisors: Dr Mark Bissett and Prof Ian Kinlock mark.bissett@manchester.ac.uk ian.kinloch@manchester.ac.uk

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