Electricity production from nuclear power plants generates radioactive wastes, the management of which represents a major industrial and environmental concern. Thus, low- or intermediate - level radioactive aqueous waste streams may be concentrated by evaporation, and immobilized with a Portland cement, before being sent to disposal. Nevertheless, interactions may occur between some components of the waste and the cement phases or aggregates, and decrease the stability of the final waste forms. Thereby, the formation of a gel-like product has been recently observed on the surface of some cemented drums of evaporator concentrates which were produced in the 80’s in Belgium. This product results from a reaction between silica from the aggregates and the very alkaline pore solution of the concrete. However, its composition and rheological properties differ from those reported for alkali-silica gels in civil engineering. Extensive work has been performed to better understand the processes involved in the gel formation within the cement-waste forms and characterize its properties. Based on these results, the post-doctoral project will be focussed on the mitigation of alkali silica reaction in cement-waste forms. Two approaches will be more particularly investigated by decreasing the water saturation ratio of concrete and/or the pH of its pore solution using supercritical carbonation.
This project is intended for a post-doctoral fellow wishing to develop skills in materials science, with an interest in advancing the field of cement chemistry and improving the conditioning of radioactive waste. It will be performed in collaboration with ONDRAF-NIRAS, the Agency in charge of radioactive waste management in Belgium, and will build upon the expertise of two laboratories at CEA Marcoule: the Cements and Bitumen for Waste Conditioning Laboratory for materials elaboration and characterization, and the Supercritical and Decontamination Laboratory.
Photovoltaic modules are required to last 25- 30 years in harsh outdoor environment. The packaging of PV modules plays an essential role in reaching this target. PV cells are protected by a glass frontsheet, and highly engineered polymeric encapsulants and backsheets. Encapsulants provide moisture, oxygen &UV barrier, electrical isolation and mechanical protection of highly fragile cells while they must ensure optical coupling between the various layers. Current industrial process technology for module manufacturing is lamination that adds additional constraints to the formulation of encapsulants. These numerous requirements lead to ever-involving complex encapsulant composition and behavior.
The aim of this post-doc is to establish the correlation between the material properties of engineered plastics– their processing conditions and thermo-mechanical behavior in high performance PV modules with heterojunction, back-contact or Si/Perovksite tandem cells. Material selection and lamination process development will be guided by detailed material characterization (DSC, DMA, Peel strength, TGA, WVTR, Soxhlet extraction etc.). Moreover, we aim to establish insights in the encapsulant processing conditions and its impact on mechanical stability of PV modules. The selection of the encapsulants to investigate will be strongly guided by eco-design to lower the environmental impact and to increase the recyclability of modules. This postdoc is conducted in the frame of an EU collaboration.
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).
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.
Historically, photovoltaic (PV) energy was developed together with the rise of space exploration. In the 90’s, multijunction solar cells based on III-V materials progressively replaced silicon (Si) cells, taking advantage of higher efficiency levels and electrons/protons irradiation resistance. Nowadays, the space environment is again looking at Si based PV applications: request of higher PV power, moderated space mission lengths, cost reduction issues (€/W Si ~ III-V/500), higher efficiencies p-type Si PV cells… Solar cells are exposed to cosmic irradiation in space, especially to electrons and protons fluxes. The latter’s affect the cells performances, essentially because of bulk defect formations and charge carrier recombination. In order to use Si based solar cells in space, we need to increase their irradiation resistance, which is the main goal of this post-doc position. To do so, the work will first consist in elaborating new Si materials, with increased irradiation resistance. Compositional aspects of the Si will be modified, particularly by introducing elements limiting the formation of bulk defects under irradiations, developing electrical passivation properties. The electronic properties of the materials will be deeply characterized before and after controlled irradiation. Then, this Si material will be used to fabricate heterojunction solar cells. Their performances will be evaluated again before and after irradiation. Such experimental work could be supported by numerical simulation at the device scale.
The development of the use of renewable energies and energy storage as well as the progress made by power electronic components are gradually leading to a rethinking of the architectures of low voltage electrical distribution networks in buildings. These developments will allow the development of direct current or mixed alternating-direct current networks supplied by static converters. On this type of network, faults become more difficult to manage due to the power sources used. Indeed, the usual signatures of the short-circuit or the overload are no longer the same and will vary according to the converters used and the architecture of the network. For this, it is necessary to identify, by simulation, the most suitable protection topologies (by neutral systems for example) and to identify the typical fault signatures. Ultimately, these signatures will provide optimum detection devices.
The goal is to improve TRUST-FC software -a joint development between LITEN and DES institutes at CEA- for detailed full 3D simulation of hydrogene PEM fuel cells and to run simulations on whole real bipolar plate geometries. Funded by AIDAS virtual lab (CEA/Forshungs Zentrum Juelich), a fully coupled electro-chemical, fluidic and thermal model has been built, based on CEA software TRUST. The model has been benchmarked against its FZJ counterpart (Open fuelcell, based on OpenFoam). The candidate will adapt the software and toolchain to larger and larger meshes up to billion cells meshes required to model a full bipolar plate. Besides, he will introduce two phase flow models in order to address the current technological challenges (local flooding or dryout). This ambitious project is actively supported by close collaboration with CEA/DES and FZJ.
The energy transition will lead to a very strong growth in the demand for rare earths (RE) over the next decade, especially for the elements (Nd, Pr) and (Dy, Tb). These RE, classified as critical materials, are used almost exclusively to produce NdFeB permanent magnets, and constitute 30% of their mass.
Several recent international studies, aiming to identify new alloys with low RE content and comparable performances to the dense magnetic phase Nd2Fe14B, put hard magnetic compounds of RE-Fe12 type as advantageous substitution solutions, allowing to reduce more that 35% of the amount of RE, while keeping the intrinsic magnetic properties close to those of the Nd2Fe14B composition.
The industrial developments of the RE-Fe12 alloys cannot yet be considered due to the important technological and scientific challenge that remain to be lifted in order to be able to produce dense magnets with resistance to demagnetization sufficient for current applications (coercivity Hc > 800 kA/m).
The aim of the post-doctoral work is to develop Nd-Fe12 based alloys with optimized intrinsic magnetic properties and to master the sintering of the powders in order to obtain dense magnets with coercivity beyond 800 kA/m, to fulfil the requirements of the applications in electric mobility. Two technological and scientific challenges are identified:
- understanding of the role of secondary phases on the coercivity. This will open the way to the implementation of techniques called "grain boundary engineering", well known for the NdFeB magnets to have remarkably improved the resistance to demagnetization.
- mastering the sintering step of these powders at low temperature (< 600°C) in order to avoid the decomposotion of the magnetic phase by grain boundary engineering
Historically, photovoltaics was developed in conjunction with the growth of space exploration. During the 90's, III-V multi-junction solar cells were progressively replaced silicon, for their superior performance & radiation hardness. Today, the context is favorable to a revival of space Si: increasing PV power needs, missions with moderate durations & constraints (LEO), very low cost & high performance terrestrial Si cells (p-type > 26% AM1.5g). However, for Si cells, conventional irradiation ageing methods & sequences (ECSS) are less appropriate. As the literature mainly comes from 80s - 90s, it is necessary to revisit the topic for the latest generation of passivated contacts Si cells (developed at CEA INES) and the unique double beam irradiation facilities of JANNuS platform - CEA Saclay.
This work is part of the SiNRJs project, at the interface between two CEA departments, dealing with space photovoltaics & materials irradiation. The scientific & technological approach adopted: 1. fabrication of passivated contact Si cells (HeT and/or Poly-Si) 2. Si cells optoelectronic characterizations before irradiation (IV AM1.5/AM0, EQE, etc.) 3. Cells & samples proton irradiations, in situ characterizations (Raman & El) 4. Ex situ characterizations after irradiations (IV AM1.5/AM0, EQE, etc) 5. Results analysis and synthesis. From a scientific point of view, the key issues to be addressed concern the understanding of the mechanisms/dynamics of defect creation/healing under this double electronic and ballistic excitation.