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.
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).
Implementation of a sensor allowing the online monitoring of the corrosion of stainless steels in a hot and concentrated nitric acid medium
The control of materials (mainly stainless steel) aging of the spent nuclear fuel reprocessing plant is the subject of permanent attention. Some installations at La Hague plant will have to be replaced very soon. In this context, it is important for the industry to develop sensors that are resistant to concentrated nitric acid (˜ 2.5 mol / L) and temperature (from ambient to 130 °C), allowing the online monitoring of the corrosion.
The aim of this work is to manufacture one sensor for the detection of corrosion of the steel intended for handling by the operators of the plant. In case of a positive response, the second sensor is used.
The challenges of this work are essentially technological since it will develop or use materials adapted to concentrated and hot nitric acid media.
The laboratory is specialized in the corrosion study in extreme conditions. It is composed of a very dynamic and motivated scientific team.
Thermodynamic modelling of protective coating for solid oxide electrolysis cells
In the pursuit of a sustainable energy future, solid oxide electrolysis cells (SOECs) are a highly promising technology for producing clean hydrogen by electrolysis of water at high temperature (between 500 and 850°C). Although high operating temperature offers many benefits (high efficiency and low power consumption), it can lead to degradation of the interconnectors. Coatings are proposed to improve the long-term performance of interconnectors and reduce corrosion problems. The aim is to find the best coating candidates with high thermodynamic stability, high electrical conductivity and low cation diffusivity. In this context, you will join the LM2T team within the DIADEM Project (https://www.diadem.cnrs.fr/2023/03/29/atherm_coat/) for innovative materials.
Your role will be to:
1)Perform thermodynamic simulations using CALPHAD method and Thermo-Calc Software to predict the stability range of a set of coating candidates (e.g. spinel oxides and perovskites) and the possible decomposition reactions in different atmosphere conditions (temperature and oxygen partial pressure). In this step, the candidate will also perform a critical review of the thermodynamic data available in the literature.
2)To couple information obtained from CALPHAD calculations and the thermodynamic database to estimate the thermal expansion and electrical conductivity of the most promising compositions.
The candidate will work closely with the experimental team (ISAS/LECNA and UMR-IPV) producing the coatings to guide future trials and adapt the method to better meet large-scale production needs.
Thermodynamic Modelling of Complex Oxides for Smart Sensors
The search for more efficient materials follows a pattern that has changed very little over the years, involving poorly automated phases of synthesis, characterization and measurement of functional properties. Although this pattern has proved its strength in creating knowledge bases, it remains ineffective because it is time-consuming and generally covers a reduced range of compositions. The project Hiway-2-mat (https://www.pepr-diadem.fr/projet/hiway-2-mat/) seeks to use high-throughput combinatorial approaches and develop autonomous configurations to explore the compositional spaces of complex oxide materials, with the aim of accelerating the discovery of materials for smart sensors. In this context, CALPHAD method is a valuable tool for materials exploration, as it can provide a number of useful insights into the role of oxidation state or oxygen partial pressure on phase stability, and on the degree of substitution of doping elements in an oxide matrix. The aim is to calculate phase diagrams of complex oxides based on available databases, either to better prepare combinatorial experiments, or to drive the autonomous robot on the fly, providing additional information for on-line characterization.
Your role will be to:
1)Perform thermodynamic simulations using CALPHAD method and Thermo-Calc Software to predict the stability range of a set of complex oxides (Ba/Ca/Sr)(Ti/Zr/Sn/Hf)O3 at different temperatures and oxygen partial pressures. In this step, the candidate will also perform a critical review of the thermodynamic data available in the literature.
2)Include key elements in the available database.
3)Develop a rapid screening method to search for the most promising compositions.
The candidate will work closely with the experimental platform development team to guide future trials and adapt the method to better meet the needs of large-scale production.