New silicon-based alloys and composites for all-solid-state batteries: from combinatorial synthesis by magnetron sputtering to mechanosynthesis
All-solid state lithium-ion batteries using sulphide-based electrolytes are among the most studied at present in order to improve energy density, safety and fast charging. Although lithium metal was initially the preferred choice for the anode, the difficulties encountered in its implementation and the performance achieved suggest that alternatives should be proposed. Silicon offers an interesting compromise in terms of energy density and lifetime. However, it is necessary to look at anode materials developed for all-solid state batteries. To this end, we propose to collaborate with CEA Tech Nouvelle-Aquitaine, which has set up a combinatorial synthesis methodology using magnetron sputtering, in order to accelerate the identification of new compositions of silicon-based materials. Libraries of materials with compositions gradient in thin films will be prepared at CEA Tech Nouvelle-Aquitaine and then studied at CEA Grenoble. The most promising compositions will then be prepared by mechanosynthesis and characterised at CEA Grenoble. Significant work will be carried out on milling processes in order to optimise particle size and homogeneity, as well as structure and microstructure. Attention will also be paid to integration in all-solid state cells, drawing on the laboratory's expertise.
Design and reliability of modular architecture for reconfigurable and repairable PV panels
The integration of photovoltaic modules has become a challenge for adaptation to climate change, notably with the installation of specific PV modules in urban spaces, on vehicles or on agricultural farms. These modules are required to operate in more complex situations presenting high temporal variability and changing exposure to the sun. The scientific challenges of the project are to determine the conditions needed for optimizing the performance of PV modules regarding these external disturbances by the study of reconfigurable electrical module architectures. A reliability model will be developed to integrate the impact of the system architecture, in order to guarantee an improved level of reliability. In-depth work will be carried out on the entire PV module, from cell technologies to the final electrical characteristics requested, including electrical switching technologies. In a second phase, we will develop a design methodology in conjunction with a precise state of the art of available power switching technologies. The method will be applied to a use case responding primarily to the problem of shading and/or localised failure of the PV module. Finally, the proposed architectures will be evaluated by life cycle analysis. The designs authorizing maintenance or replacement of certain elements will be detailed and compared to the performance of usual modules.
Understanding the fundamental properties of PrOx based oxygen electrodes through ab-initio and electrochemical modelling for solid oxide cells application
Solid Oxide Cells (SOCs) are reversible and efficient energy-conversion systems for the production of electricity and green hydrogen. Nowadays, they are considered as one of the key technological solutions for the transition to a renewable energy market. A SOC consists of a dense electrolyte sandwiched between two porous electrodes. To date, the large-scale commercialization of SOCs still requires the improvement of both their performances and lifetime. In this context, the main limitations in terms of efficiency and degradation of SOCs have been attributed to the conventional oxygen electrode in La0.6Sr0.4Co0.2Fe0.8O3. To overcome this issue, it has recently been proposed to replace this material with an alternative electrode based on PrOx. Indeed, this material has a high electro-catalytic activity for the oxygen reduction and good transport properties. The performance of cells incorporating this new electrode is promising and might enable to reach the targets required for large-scale industrialization (i.e. -1.5A/cm2 at 1.3V at 750°C and a degradation rate of 0.5%/kh). However, it has been shown that PrOx undergoes phase transitions depending on the cell operating conditions. The impact of these phase transitions on the electrode properties and on its performance and durability are still unknown. Thus, the purpose of the PhD is to gain an in-depth understanding of the physical properties for the different PrOx phases in order to investigate their role in the electrode reaction mechanisms. The study will contribute to validate whether PrOx based electrodes are good candidates for a new generation of SOCs and help to identify an optimized electrode using a methodology combining ab-initio calculation with electrochemical modelling.
Simulation of heterogeneities in battery cells using materials with lower environmental impact
The electrification of vehicles to decarbonize our activities faces a dilemma concerning batteries, their environmental impact and the supply of materials needed to manufacture them. The low-environmental-impact materials being considered today to meet these needs (LF(M)P, sodium-ion technology, etc.) have specific electrochemical characteristics that need to be anticipated before they can be used in large-capacity batteries. These two- or multi-phase materials have an electrical potential that is only slightly dependent on the state of charge. This characteristic favors the appearance of a highly heterogeneous state of charge in the cell. The complex mechanism is linked in particular to fast charging, which is very important for vehicles, and which creates significant heating at the heart of the cells. These heterogeneities limit battery performance and shorten their lifespan. In addition, the flat voltage profile and heterogeneities make it extremely difficult to diagnose the cell's state of charge and state of health. Yet this information is crucial for battery management that maximizes battery life.
Our laboratory is developing advanced modeling tools that enable us to simulate these phenomena. Using a highly detailed numerical model of a large cell, applied to realistic cycling conditions, the candidate will highlight the internal state of cells, which is difficult to access experimentally, and show how cycling, thermal management or diagnostic strategies need to be adapted for the more sustainable chemistries envisaged today. To do this, he will use CEA's software platforms and supercomputers, and draw on CEA/LITEN's expertise covering all technological stages, from materials to real-life cell testing.
Study of catalysis on stainless steels
The materials (mainly stainless steels) aging of the spent nuclear fuel reprocessing plant is the focus of an important R&D activity at CEA. The control of this aging will be achieved by a better understanding the corrosion mechanisms the stainless steels in nitric acid (the oxidizing agent used in the reprocessing steps).
The aim of the PhD is to develop a model of corrosion on a stainless steel in nitric acid as a function of temperature and the acid nitric concentration. This PhD represents a technological challenge because currently few studies exist on in situ electrochemical measurements in hot and concentrated nitric acid. The PhD student will carry out by coupling electrochemical measurements, chemical analyses (UV-visible-IR spectrometry...) and surfaces analyses (SEM, XPS,…). Based on these experimental results, a model will be developed, which will be incorporated in the future in a more global model of the industrial equipments aging of the plant.
The laboratory is specialized in the corrosion study in extreme conditions. It is composed of a very dynamic and motivated scientific team which has the habit to receive students.
Kinetics of the Melting Front in a Phase Change Material Used for Decay Heat Removal in an Innovative Nuclear Reactor
In the context of developing innovative sodium-cooled fast reactors (SFR), this PhD aims to explore the use of a phase change material (PCM) to remove residual power. The PCM studied in this project is Zamak, a metallic alloy that presents advantageous properties for such thermal applications. Some SFR designs incorporate passive safety systems intended to ensure the removal of residual power, which refers to the heat generated by delayed fission and radioactive decay of fuel isotopes after reactor shutdown. The use of PCM is a promising option, as they can absorb and store heat through a melting process and subsequently release it gradually during a solidification process.
The core of this PhD focuses on Computational Fluid Dynamics (CFD) modeling of the Zamak melting process and the scaling of this model for use in a system-size calculation tool. The main challenge lies in predicting the behavior of the melting front, its stability, and its impact on the kinetics of residual power removal. This melting front is influenced by numerous factors such as the wetting angle and the physico-chemical properties of the PCM-wall or PCM-surrounding gas interface, which will be examined throughout the thesis. The research will thus involve developing a CFD model that integrates these aspects, using a porous enthalpy approach, allowing predictive simulations of the PCM's behavior in the residual power removal system. A scaling analysis will then be conducted.
The PhD candidate will be part of a research team on innovative reactors at the IRESNE institute located at the CEA Cadarache site. Career opportunities after the thesis include academic research, R&D, and the nuclear industry, as well as sectors utilizing PCM technologies.
Tri-axial cell investigations and consideration of the influence of the behaviour of the agglomerates (U-Pu)O2 microstructure on the simulation of fuel shaping
The research topic concerns the influence of the behaviour of the (U-Pu)O2 agglomerate microstructure on the simulation of fuel shaping through triaxial cell investigations. It is based on multi-scale experimental and numerical studies in order to propose simulations of the shaping of actinide fuels, taking into account the breakage and rearrangement of agglomerates in the behaviour laws VER on homogenised VER. To this end, investigations in triaxial cells are envisaged, on the one hand on VER using X-CT tomography on simulating inactive model powders and on the other hand on industrial-sized samples on real active powders. Fracture tests using X-ray tomography will also be carried out on inactive materials and without tomography on active materials, in order to compare experimental and numerical results in the case of damage to pre-sintered fuels. A comparison will also be planned to take into account the impact of the proposed approach on the parameters of the models currently used for macroscopic simulations of fuel shaping on an industrial scale.
A revolution in intervention in complex environments: AI and Digital twins in synergy for innovative and effective solutions.
Scientific Context
The operation of complex equipment, particularly in the nuclear sector, relies on quick and secure access to heterogeneous data. Advances in generative AI, combined with Digital Twins (DT), offer innovative solutions to enhance human-system interactions. However, integrating these technologies into critical environments requires tailored approaches to ensure intuitiveness, security, and efficiency.
Proposed Work
This thesis aims to develop a generative AI architecture enriched with domain-specific data and accessible via mixed reality, enabling a glovebox operator to ask natural language questions. The proposed work includes:
A review of the state-of-the-art on Retrieval-Augmented Generation (RAG), ASR/TTS technologies, and Digital Twins.
The development and integration of a chatbot for nuclear operations.
The evaluation of human-AI interactions and the definition of efficiency and adoption metrics.
Expected Outcomes
The project aims to enhance safety and productivity through optimized interactions and to propose guidelines for the adoption of such systems in critical environments.
Atomistic investigation of the diffusion of small xenon clusters in the metallic nuclear fuel UMo
This project is centered on the application of atomistic methods in order to investigate the stability and diffusion of intra-granular xenon clusters within the metallic nuclear fuel UMo.
Uranium – molybdenum alloys UMo present excellent thermal properties and a good uranium density. For those reasons, they are considered as nuclear fuel candidates for research reactors. It is therefore crucial to deploy new computational methodologies in order to investigate the evolution of their thermophysical properties under irradiation conditions.
During this PhD project, you will be in charge of validating (and, if necessary, recalibrating) the atomistic computational models for UMo that have been published in the literature. You will then apply those to the simulation of the stability and diffusion of small xenon clusters (typically up to 5 xenon atoms) within UMo crystals. Those computations will be performed leveraging accelerated molecular dynamics methods, and systematically compared to the results obtained for the reference nuclear fuel UO2. The results will also be analyzed by comparison to experimental measurements performed within the department, as well as used as reference data for larger-scale nuclear fuel performance codes. The results of your research will be published in scientific journals, and you are expected to attend international conferences to present your findings.
Those different investigations will allow you to acquire a set of competences applicable to many areas of materials science: ab initio calculations, machine-learning adjustment of interatomic potentials, classical and accelerated molecular dynamics, as well as many elements of statistical physics and condensed matter physics, which are among the areas of expertise of the PhD advisors.
The PhD will be based in the Fuel Behavior Modeling Laboratory (IRESNE Institute, CEA Cadarache), a dynamic research environment within which you will have the opportunity to interact with other PhD students. You will also benefit from a rich collaborative network (experimental researchers from the department, ISAS Institute at CEA Saclay, CINAM Laboratory in Marseille), that will allow you to become a member of the nuclear materials research community.
Optimization by Artificial Intelligence of In Situ Characterization of Pure Beta Radionuclides in Complex Environments
Before, during, and after... the characterization of the radiological state is essential at all stages of the decommissioning scenario of a nuclear facility. Can we intervene directly on-site, or is teleoperation necessary? Has the contamination of a given area been completely eliminated? How should we categorize a particular nuclear waste to optimize its future management?
In-situ non-destructive nuclear measurements aim to evaluate the radiological state of processes and equipment in real time, while meeting criteria of efficiency, safety, flexibility, and reliability, and reducing costs through rapid, precise, and non-invasive analyses. While characterization techniques for gamma emitters are well mastered, those for pure beta emitters remain a significant challenge due to the low range of beta radiation in matter and the ambient gamma noise, which makes in-situ detection particularly complex.
The integration of artificial intelligence (AI) tools, such as machine learning or deep learning, in this field opens new perspectives. These technologies enable the automation of the analysis of large amounts of data while extracting complex information that is often difficult to interpret manually, particularly for deconvoluting continuous beta radiation spectra. Initial results obtained in the framework of L. Fleres' thesis have shown that AI can effectively predict and quantify the beta-emitting radionuclides present in a mixture. Although promising, this approach, tested in laboratory conditions, still needs to be qualified in real-world field conditions.
The proposed thesis aims to continue and refine these developments. It will involve integrating new algorithms, exploring various neural network architectures, and enriching learning databases to improve the performance of current systems for the in-situ characterization of beta emitters. This will include scenarios where the beta/gamma signal-to-noise ratio is degraded, as well as the detection of low levels of activity in the presence of natural radioactivity. Other research avenues will include the detection of low-energy radionuclides and the adaptation of deconvolution tools for large-surface detectors.
The characterization methodology developed at the end of the project will have strong potential for industrial valorization, particularly in the fields of decontamination and decommissioning. The doctoral candidate will join a team with extensive experience in the implementation of non-destructive radiological characterization techniques and methods in-situ and will have the opportunity to evaluate the proposed solutions on some of the largest decommissioning projects in the world.
Desired Profile: The ideal candidate holds a degree from an engineering school or a Master's (M2) with solid knowledge of nuclear measurement, particularly regarding the physical phenomena related to the interactions of ionizing radiation with matter. Skills in statistical data processing methods and programming (Python, C++) would also be appreciated.