Fully-funded PhD Studentship in Automated Beta-emitting Radioisotope Identification and Monitoring in Boreholes

Proposed here is the development of an automated detection and fingerprinting method using machine learning and spectra unfolding techniques for the real-time, in-situ monitoring and determination of weak beta-emitting radionuclides found in groundwater around nuclear sites without the need for sampling. This will utilise state-of-the-art technology recently developed by the research team capable of detecting beta-emitting radionuclides in water.

Remote monitoring of groundwater sites is notoriously difficult owing to the array of chemical and physical contaminates that can potentially be found due to the varied past uses of sites, such as Sellafield, including non-nuclear activities. Detection and identification of beta-emitting radionuclides such as 3H, 40K, 90Sr, 125Sb, etc. in boreholes is a complex but important task due to the emission of ionising radiation which can be hazardous to human health. Hence the requirement to monitor groundwater to ensure it meets national and international legislation. This task is difficult as the beta particles emitted have broad, over-lapping energy spectra, and are absorbed within a very short distance from creation in water. Currently, the assay of radionuclides producing weak beta radiation is undertaken via the sending of samples to 3rd party labs for extraction and analysis – a costly and time-consuming method.

This project will build on a previous successful NDA bursary funded project ‘In-situ Real-time Monitoring of Waterborne Low Energy Betas’ (NNL/UA/006) led by the research team here. In this project, a prototype system was developed [1, 2] capable of detecting weak beta-emitting radionuclides, such as tritium, in groundwater. The detectors developed were designed to fit in boreholes and detect radionuclides in water without having to remove samples from the borehole. An efficient data management system [1] was also developed to facilitate extended testing and communications required for deployment.

The proposed project will use this technology directly but extend the functionality to automatically detect and quantify the concentrations of individual weak beta-emitting radionuclides, such as tritium and 14C, in the presence of 90Sr, which is the dominant high-energy beta-emitting radionuclide in groundwater on site at Sellafield. The research will focus on developing machine learning algorithms to extend total beta analysis to individual weak beta-emitting radionuclides, automating the analysis, and negating the need for sampling and laboratory separation and chemical analysis. It hence will become far cheaper to screen boreholes frequently.

SATURN_Nuclear_CDT

Informal enquiries and how to apply

Interested candidates are strongly encouraged to contact the project supervisor Dr David Cheneler (d.cheneler@lancaster.ac.uk) to discuss their interest in and suitability for the project prior to submitting an application.

The application process can be found on our website: Apply | EPSRC Centre for Doctoral Training in Skills And Training Underpinning a Renaissance in Nuclear | The University of Manchester

• Please complete the enquiry form to express your interest.
• We strongly recommend you contact the project supervisor after completing the form to speak to them about your suitability for the project.
• If your qualifications meet our standard entry requirements, the CDT Team will send your enquiry form and CV to the named project supervisor.

If you have any questions about making an application, please email: SATURN@manchester.ac.uk

Applicants should have a minimum of an upper second-class honours degree in electronic engineering, mechatronic engineering, computer science, or a related technical subject.