Impact of a sodium nitrate saline plume on the radionuclide confinement properties of cementitious matrices

Using modelling to predict the migration of radioactive species through a well-known porous matrix, such as concrete, is a major challenge for society, particularly in the context of studies linked to the radioactive waste management. Demonstrating that the proposed model is robust through targeted laboratory experiments under extreme chemico-physical conditions is one of the scientific challenges proposed by the CEA as part of this PhD research project.
The young reseacher will be responsible for designing, carrying out and modelling experimental lab-tests on the retention and diffusion of radionuclides of interest in controlled cementitious conditions or under perturbation due to the nitrate plume leading to very high concentrations in the pore solution. The main expected result is to propose a predictive model coupling chemistry under extreme ionic strenght conditions and transport through complex cementitious matrices, validated by experimental data acquired on simple systems.
Surrounded by a team of experts in the field of measuring and modelling radionuclides migration in porous media, the PhD student will be able to develop or extend his/her skills in the following areas: chemistry, analytical chemistry, physico-chemistry, radiochemistry and modelling.

Development of an automated and miniaturised system for the isotopic analysis of nuclear samples

Miniaturisation, which is the process of reducing an object’s, a method’s or a function’s dimensions while preserving or even upgrading its performances as compared to the classical scale, has a particular interest in the field of analytical chemistry for nuclear applications. Indeed, most of the analyses are performed in gloveboxes where miniaturisation and automation are a direct solution to the need for reduced doses and waste volumes. This PhD aims at developing a miniaturised and automated system, in a glove box, for performing high-precision isotopic measurements. This system will use capillary electrophoresis (CE) hyphenated with a nuclearised multicollector ICP-MS (MC-ICP-MS). During this PhD, the student will make use of micro-machining machines and 3D printers to develop an ergonomic system which will then be coupled to last generation MC-ICP-MS instruments available in our laboratory. The project will be focused on the conception of the automated system and its integration in the glove box, and on the further development of the existing CE method in order to perform isotopic and elemental analyses with nuclear samples. This PhD is hosted in a laboratory internationally recognized for its ability to carry out high precision isotopic measurements. An analytical chemistry curriculum is expected and a Master 2 internship is available before this PhD.

Radiolytic Degradation of N,N-dialkylamides: Effects on Metal Complexation

N,N-dialkylamides (or monoamides) are promising extractant molecules for the development of new processes for nuclear fuel reprocessing. In this context, these extractant molecules are exposed to radiolysis caused by ionizing radiation from radionuclides, which leads to the formation of new compounds through the breaking or modification of chemical bonds. Such changes in solution composition can alter the extractive properties, particularly in terms of efficiency and selectivity.
This thesis aims to study the impact of radiolysis on the speciation of actinide-ligand complexes in solution, in order to improve the understanding of the phenomena observed under ionizing radiation. We propose an approach combining experimental studies (chromatographic and spectroscopic techniques) with theoretical calculations (such as bond dissociation energies, identification of probable radical attack sites, stability of metal-ligand complexes, etc.) to describe the molecular speciation of species in solution. Organic compounds formed during radiation and the metallic complexes will be characterized to evaluate the modifications caused by radiation.

Spectrometry and Artificial Intelligence: development of explainable, sober and reliable AI models for materials analysis

The discovery of new materials is crucial to meeting many current societal challenges. One of the pillars of this discovery capacity is to have means of characterizing these materials which are rapid, reliable and whose measurement uncertainties are qualified, even quantified.

This PhD project is part of this approach and aims to significantly improve the different ion beam induced spectrometry (IBA) techniques using advanced artificial intelligence (AI) methods. This project aims to develop explainable, sober and reliable AI models for materials analysis.
The PhD project proposed here has three main objectives:

- Develop an uncertainty model using probabilistic machine learning techniques in order to quantify the uncertainties associated with a prediction.
- Due to the very large number of possible combinatory-generated configurations, it is important to understand the intrinsic dimensionality of the problem. We wish to implement means of massive dimensionality reduction, in particular non-linear methods such as autoencoders, as well as PIML (Physics Informed Machine Learning) concepts.
- Evaluate the possibility of generalization of this methodology to other spectroscopic techniques.

Top