Thermodynamic investigation of Metal-Insulator-Transition materials – The case of doped VO2 for smart windows applications
The present post-doc proposal aims to develop a specific thermodynamic database on the V-O-TM (TM=Fe,Cr) system by using the CALPHAD approach. The candidate will conduct experimental campaigns to obtain relevant data to feed the thermodynamic models. The candidate will mostly use the experimental equipment available at the lab (DTA, annealing furnaces, high temperature mass spectrometry, laser heating, SEM-EDS). In addition, the post-doc may participate to combinatorial high-throughput activities led by other laboratory of the Hiway-2-Mat consortium (e.g., ICMCB in Bordeaux), allowing a better connection between the CALPHAD simulation output and the accelerated characterization platform. The thermodynamic database will be then included in the autonomous research routine implemented in the material exploration path.
Role of metal containers on the alteration of high-level waste confinement glasses in geological storage conditions: glass-iron interactions in hydrogen-tight reactors
Vitrified waste resulting from nuclear power plant fuel reprocessing, as well as their steel containers and overpacks, are intended for permanent storage in deep geological layers. Water will be the vector of glass alteration and potential migration of radioactive elements. The most advanced storage concept to date provides for the glass package to be protected for its thermal decay step from interaction with water by an unalloyed steel overpack. However, whether in the form of metallic iron or corrosion products of steels (oxides, carbonates, sulfides), iron plays a significant role in glass alteration.
The objective of this work is to understand and quantify the mechanisms of glass-iron interaction in order to strengthen the operational models for waste package performance. To this end, a bench of ten hydrogen-tight instrumented reactors has been developed in the laboratory. It has allowed the implementation of a first series of long-term experiments of several months, which concerned a non-radioactive model glass and a iron carbonate. The objective will be to carry out these interaction experiments using metallic iron this time, to characterize the sampled solutions and neoformed alteration products, and to interpret the experiments using the modeling tools available in the laboratory.
Behavior of materials in molten salts
Access to clean and affordable Energy is a key challenge in the current context of climate emergency. Several leads have been considered for several years but technological issues remain up to date to make it happen. From concentrated solar plant to 4th generation of nuclear reactor, molten salt is a promising media (both for heat transfer fluid and the fuel itself). Nevertheless, due to the presence of impurities, molten salts are highly corrosive for commonly used materials.
Most of the commercial alloys - either nickel based or iron base - seems to suffer from rapid attack. It is then needed to broaden the scope of the studies by investigating innovative materials. Thus, a screening of materials is planned to select the most interesting ones. After a thorough filtering, a study of the corrosion mechanism will be carried out through analysis at different scales (SEM, DRX, SDL, ICP, etc … )as well via electrochemical techniques and thermodynamic modelisation (HSC and FactSage).
The aim of the post doctoral subject offered at the S2CM (Service of corrosion and Behavior of Materials) consists in the entire study of the behavior, from the sample preparation to the caracterization of corrosion products. This topic is highly experimental and goes deep in the understanding of the corrosion mechanisms. This post doc position is part of a project gathering top - Notch industrial and academics (EDF,Framatome, Orano and the CNRS). Results obtained are subject to be presented to the different partners.
Mitigation of Alkali Silica Reaction in concrete used for radwaste stabilization and solidification
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.
Next generation PV module packaging design and mechanical testing
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.
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.
Development of irradiation resistant silicon materials and integration in photovoltaïcs cells for space applications
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.
Modeling of faults on low voltage DC networks in buildings, towards fault detection algorithms
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.