Modelling the corrosion of spent nuclear fuels by DFT methods

The PhD project, supervised primarily by MS at the Chemistry Department of the University of Surrey, will seek to investigate experimentally and computationally the corrosion mechanism, including intermediates, products and reaction rates, of spent nuclear fuel (SNF) in oxidative environments.

The objective is therefore to be able to predict quantitatively the degree of corrosion and product formation as a function of time and temperature by using a combination of experimental techniques (i.e., fluorescence, Raman, EXAFS) and computational techniques (density functional theory).

The focus will be on understanding the surface chemistry of nuclear fuel, modelling the formation and dissolution of the surface oxides exposed to wet environments. The main deliverable will be an analytical, thermodynamic and kinetic description of the corrosion mechanism of U, UO₂ and UO_x.

Hubbard-corrected density functional theory (DFT+U) and Dynamical mean-field theory (DMFT) will be employed to provide an accurate description of structure and electronic properties of the material surface and the reactive species, predict the geometry and energetics of the local and global minima as well as the activation energies for the reactions occurring on the surface of the materials investigated.

Experimental validation performed in the group of DR will allow us to calibrate and benchmark the theoretical results against state-of-the-art measurements at the University of Surrey and partner laboratories, including laser fluorescence and Raman spectroscopy, as well as X-ray spectroscopy at synchrotron facilities (EXAFS).

This project will also benefit from parallel experimental work at the University of Bristol (Martin, Springell and Scott groups), that will supply single-crystal thin films of U oxides with a precisely defined surface orientation.

UK or EU applicants who hold a First or 2:1 UK honours degree in a relevant subject area (e.g., chemistry, physics, material science) or a good master’s degree (a distinction is usually required).

A passion for computational research, problem-solving and collaborative experimentation is required. Experience in scientific modelling and working knowledge of a programming language (e.g., Fortran, Python, C) would be highly beneficial.

News piece: Collaborative decommissioning research TRANSCENDS the individual approach.