Study of embrittlement and thermal fatigue of stainless steels
In order to study the behavior of nuclear materials under dynamic stress, CEA Valduc wishes to develop a new device integrated into a glove box. For this purpose, a collaboration with CEA GRAMAT and an industrial specialist in the field has been set up. During the experiments conducted with thisdevice, some components are subjected to very high temperatures and the presence of gaseous hydrogen for a very short time.
Initially, different grades of austenitic steels were tested under these severe conditions and a prototype was ordered and received at the industrial site.
The study has three objectives. The first is the acquisition of new experimental mechanical and microstructural data at the micrometric scale from the previously exposed steel batches. The second concerns the microstructural analysis of thermal fatigue. Finally, the third and final objective is the integration of these new data into a numerical tool for microstructural evolution simulation aiming at reproducing the global effect of aging, in thermal and pressure cycles by coupling, for example, CALPHAD-type codes and the use of multiphysical COMSOL-type codes.
The work will take place according to the three components carried out in parallel. It will also be requested from the post-doctoral student in charge of this study to:
- participate to the implementation and monitoring of collaborations with recognized experts in the fields of embrittlement, thermal fatigue, and microstructural analysis,
- synthesize and publish, as far as possible, the results obtained in the form of various documents and publications in international journals or conference communications
Cell manufacturing and electrochemical testing of solid-state batteries
Holding a PhD in electrochemistry, materials science, chemistry, or process engineering, the postdoctoral researcher will work closely with project partners on the development of manufacturing processes and prototyping of solid-state battery cells of 4?? generation (Li/NMC high-nickel) and 5?? generation (Li/Sulfur).
The work will focus on electrode shaping and assembly of solid-state cells, using processes such as coating, extrusion, and alternative approaches including 3D printing. These processes will be optimized to produce prototype cells (button cells and pouch format) with capacities up to 1 Ah, incorporating optimized interfaces. The cells will then be electrochemically tested to evaluate performance in terms of specific capacity, coulombic efficiency, and cycling stability.
Most experimental work will be conducted in controlled environments (gloveboxes), with regular characterization of both electrodes and assembled cells. Main responsibilities will include:
- Contributing to the definition of test plans based on internal data and literature,
- Developing and optimizing manufacturing processes for electrodes and solid-state cells,
- Producing and testing Gen4b and Gen5 prototype cells,
- Evaluating electrochemical performance and analyzing results,
- Presenting results clearly and concisely,
- Proposing improvements, ensuring smooth laboratory operations, and adhering to safety and quality standards,
- Disseminating research through publications, scientific presentations,
Spin-lattice interactions in Machine Learning assisted ab initio simulations
The scientific field addressed by this postdoctoral project lies at the intersection of ab initio molecular dynamics, machine learning, and the thermodynamic characterization of materials under extreme conditions. Traditional AIMD simulations are a powerful tool to study temperature- and pressure-dependent properties from first principles, but their high computational cost limits their widespread use. By developing and applying machine learning-assisted sampling techniques like MLACS, this postdoc aims to drastically reduce the computational burden while retaining ab initio accuracy. This enables the efficient exploration of phase diagrams in high-pressure and high-temperature conditions. This research contributes to both fundamental understanding and practical modeling of materials, offering high-impact tools for the scientific community.
Study of the Thermodiffusion of Small Polarons in UO2
The position is published on the CEA website at the following address:
https://www.emploi.cea.fr/job/emploi-post-doctorat-etude-en-ab-initio-de-la-thermodiffusion-des-petits-polarons-dans-UO2-h-f_36670.aspx
Impact of Microstructure in Uranium Dioxide on Ballistic and Electronic Damage
During reactor irradiation, nuclear fuel pellets undergo microstructural changes. Beyond 40 GWd/tU, a High Burnup Structure (HBS) appears at the pellet periphery, where initial grains (~10 µm) fragment into sub-grains (~0.2 µm). In the pellet center, under high temperatures, weakly misoriented sub-grains also form. These changes result from energy loss by fission products, leading to defects such as dislocations and cavities. To study grain size effects on irradiation damage, nanostructured UO2 samples will be synthesized at JRC-K, using flash sintering for high-density pellets. Ion irradiation experiments will follow at JANNuS-Saclay and GSI, with structural characterizations via Raman spectroscopy, TEM, SEM-EBSD, and XRD. The postdoc project will take place at JRC-K, CEA Saclay, and CEA Cadarache under expert supervision.
Development of new Potassium-ion cells with high performances and low environmental impact
Lithium ion batteries are considered as the reference system in terms of energy density and cycle life and will play a key role in the energetic transition, especially concerning electric vehicles. However, such a technology involves the use of a large amount of critical elements and active materials are synthesised using energy intensive processes.
In this way, our team is developing a new Potassium-ion batteries technology with high performances but with a low environmental impact.
For this innovative and ambitious project, CEA-LITEN (one of the most important research institute in Europe) is looking for a talented post-doctoral researcher in material chemistry. The post-doctoral position is opened for a young researcher with a high scientific level, interested by valorising her/his results through different patents and/or scientific publications.
Calculation of the thermal conductivity of UO2 fuel and the influence of irradiation defects
Atomistic simulations of the behaviour of nuclear fuel under irradiation can give access to its thermal properties and their evolution with temperature and irradiation. Knowledge of the thermal conductivity of 100% dense oxide can now be obtained by molecular dynamics and the interatomic force constants[1] at the single crystal scale, but the effect of defects induced by irradiation (irradiation loop, cluster of gaps) or even grain boundaries (ceramic before irradiation) remain difficult to evaluate in a coupled way.
The ambition is now to include defects in the supercells and to calculate their effect on the force constants. Depending on the size of the defects considered, we will use either the DFT or an empirical or numerical potential to perform the molecular dynamics. AlmaBTE allows the calculation of phonon scattering by point defects [2] and the calculation of phonon scattering by dislocations and their transmission at an interface have also recently been implemented. Thus, the chaining atomistic calculations/AlmaBTE will make it possible to determine the effect of the polycrystalline microstructure and irradiation defects on the thermal conductivity. At the end of this post-doc, the properties obtained will be used in the existing simulation tools in order to estimate the conductivity of a volume element (additional effect of the microstructure, in particular of the porous network, FFT method), data which will finally be integrated into the simulation of the behavior of the fuel element under irradiation.
The work will be carried out at the Nuclear Fuel Department of the CEA, in a scientific environment characterised by a high level of expertise in materials modelling, in close collaboration with other CEA teams in Grenoble and in the Paris region who are experts in atomistic calculations. The results will be promoted through scientific publications and participation in international congresses.
References:
[1] Bottin, F., Bieder, J., Bouchet, J. A-TDE
Strain driven Group IV photonic devices: applications to light emission and detection
Straining the crystal lattice of a semiconductor is a very powerful tool enabling controlling many properties such as its emission wavelength, its mobility…Modulating and controlling the strain in a reversible fashion and in the multi% range is a forefront challenge. Strain amplification is a rather recent technique allowing accumulating very significant amounts of strain in a micronic constriction, such as a microbridge (up to 4.9% for Ge [1]), which deeply drives the electronic properties of the starting semiconductor. Nevertheless, the architectures of GeSn microlasers under strong deformation and recently demonstrated in the IRIG institute [2] cannot afford modulating on demand the applied strain and thus the emission wavelength within the very same device, the latter being frozen “by design”. The target of this 18 months post doc is to fabricate photonic devices of the MOEMS family (Micro-opto-electromechanical systems) combining the local strain amplification in the semiconductor and actuation features via an external stimulus, with the objectives to go towards: 1-a wide band wavelength tunable laser microsource and 2-new types of photodetectors, both in a Group IV technology (Si, Ge and Ge1-xSnx). The candidate will conduct several tasks at the crossroads between fabrication and optoelectronic characterization:
a-simulation of the mechanical operation of the expected devices using FEM softwares, and calculation of the electronic states of the strained semiconductor
b-fabrication of devices at the Plateforme Technologique Amont (lithography, dry etching, metallization, bonding), based on results of a
c-optical and material characterization of the fabricated devices (PL, photocurrent, microRaman, SEM…) at IRIG-PHELIQS and LETI.
A PhD in the field of semiconductors physics or photonics, as well as skills in microfabrication are required.
[1] A. Gassenq et al, Appl. Phys. Lett.108, 241902 (2016)
[2] J. Chrétien et al, ACS Photonics2019, 6, 10, 2462–2469
Automatic machine learning identification of nanoscale features in transmission electron microscopy images
Imaging nanoscale features using transmission electron microscopy (TEM) is key to predicting and assessing the mechanical behaviour of structural materials in nuclear reactors or in the fields of nanotechnology. These features, visible by phase contrast (nanobubbles) or diffraction contrast (dislocation loops or coherent precipitates), are prime candidates for automation. Analysing these micrographs manually is often tedious, time-consuming, non-universal and somehow subjective.
In this project, the objective is to develop a Python-based framework for data treatment of transmission electron microscopy (TEM) images.
Machine Learning approaches will be implemented in order to tackle the following tasks:
- Data collection: The success of any machine learning approach is linked to the database quality. In this project, a huge database is available. Four microscopists are involved in the project and will continuously enrich the database with images containing easily recognizable features.
- Denoising and finding the defect contour both through existing open-access software and in-house developed descriptors. Representative ROI (region-of-Interest) will be generated on images.
- Design of the Convolutional Neural Network (CNN) Architecture and model training: A collective feature map will be generated for the entire images in order to identify some representatives ROI. Each ROI is then overlaid to the original feature map and is passed to the CNN for individual region classifications. Secondly, recent advances in image segmentation will be placed in the core engine of the workflow.
- Model performance metrics: The aim is to reach a compromise between the training time and the detector performance.
The process will be applied to nanometer-sized features formed under irradiation in nuclear oriented materials (Co-free high entropy alloys (HEA), UO2) and precipitates in materials with a technological interest (coherent Cr precipitates in Cu).
Hydrolysis energy distribtution in model glasses using molecular simulation and Machine Learning
The objective of the project is to develop a tool based on molecular simulations combined with Machine Learning to estimate rapidly the distributions of hydrolysis and reformation energies of the chemical bonds on the surface of alumino silicate glasses(SiO2+Al2O3+CaO+Na2O).
The first step will consist in validating the classical force fields used to prepare the hydrated SiO2-Al2O3-Na2O-CaO systems by comparison with ab initio calculations. In particular, metadynamics will be used to compare classical and ab initio elementary hydrolysis mechanisms.
The next step will consist in performing « Potential Mean Force » calculations using the classical force fields to estimate distributions of hydrolysis and reformation energies on large statistics in few glass compositions. Then by using Machine Learning and atomic structural descriptors, we will try to correlate local structural characteristics of the chemical bonds to the hydrolysis and reformation energies. Methods such as Kernel Ridge Regression, Random Forest or Dense Neural Network will be compared.
At the end, a generic tool will be available to rapidly estimate distributions of hydrolysis and reformation energies for a given glass composition.