Skip to main navigationSkip to main content
The University of Southampton
Ocean and Earth Science, National Oceanography Centre Southampton

Research project: Development of Robust Automated Techniques for Radionuclide Separation

Currently Active: 
Yes

Efficient characterisation of radioactive waste is essential in determining correct disposal routes leading to decreased nuclear decommissioning costs.  The measurement of specific alpha and beta radionuclides in a sample currently involves multiple dissolution, preconcentration and separation steps. These procedures are time consuming and require significant analyst input.

Source: F. Burrell
Sequential injection system

Automation

Separation and isolation of the analyte is often achieved using column-based ion exchange or extraction chromatography resins. 

These methods can be automated using flow injection or sequential injection systems.

Having less analyst input has benefits:

Less human error - Improves quality of results
Lower labour costs - More cost effective
Unmanned operation - Greater throughput

Image credit: Magnox Ltd
Bradwell turbine hall demolition

Resin Characterisation

Full kinetic and thermodynamic characterisation of commercial resins adds extra benefits:

Consistent flow rate increases reproducibility - Improves quality of results
Using the highest flow rate possible - Greater throughput

Aims

  1. Compile a database of thermodynamic and kinetic parameters for commercial resins
  2. Create predictive separation models for different radionuclides
  3. Build an automated separation system from component parts
  4. Develop user friendly control software
  5. Validate the method using simulated and real wastes arising from decommissioning

Partners

This project is funded by the Nuclear Decommissioning Authority with National Nuclear Laboratory supervision. Facility use and commercial expertise has kindly been provided by GAU Radioanalytical Laboratories.

Useful Downloads

Need the software?PDF Reader

Related research groups

Geochemistry
Share this research project Share this on Facebook Share this on Twitter Share this on Weibo
Privacy Settings