In order to improve the stability of devices in operation, we plan to integrate Metal Organic Frameworks from different sources into Si/PVK tandem solar cells, to characterize materials modifications with different techniques (XRD, SEM, Photoluminescence, etc…) and to make aging studies of full devices following last recommended standards ISOS-L2 (light plus temperature) and ISOS-LC (light cycling).
This postdoctoral position is part of the ANR ATICS project (Advanced Tri-dimensional Imaging of Complex Particulate Systems), which aims to develop a set of advanced tools and methods for modeling and reconstructing holograms to enhance the practical capabilities of three-dimensional imaging through digital inline holography. This is a collaborative research project lasting four years, involving four university laboratories, the CNRS, grandes écoles, and the CEA. Within this framework, the objective of the postdoctoral work is to provide physical knowledge and data to other team members and to demonstrate the contributions of the theoretical and numerical developments made in ATICS in two research areas in which the partners are regularly involved: multiphase flows and recycling processes. To achieve this, new experimental devices for measuring the composition of liquids will be developed, leveraging the potential of inline digital holography at various scales, from microfluidics to the study of sprays in acoustic levitation. The work will be conducted in close collaboration with the teams at the IUSTI laboratory of Aix-Marseille University.
Accurate assessment of the inventory of long-lived radionuclides represents a major challenge for nuclear sites. Selenium 79 is one of the seven main long-lived fission products, but very few actual measurements of 79Se in real samples are reported in the literature. Its measurement is difficult due to its low concentration in spent fuel.
The main goal of this post-doctoral project is to develop an analytical protocol for lowering the detection limit (below ng/L) for 79Se in nuclear waste, and more specifically in the zircaloy cladding of spent fuel.
The candidate will be responsible for sample preparation, establishing protocols for the separation of selenium by ion exchange chromatography, development of an ICP-MS/MS measurement method to eliminate interferences and achieve the best possible sensitivity, interpretation of the results as well as their presentation at scientific conferences and publication in peer-reviewed journals.
The post-doctorate is initially financed for one year, but may be extended for a further year to develop the measurement of 107Pd and 126Sn.
In today’s climate emergency, access to clean and cheap energy is more important than ever. Several ways have been envisaged for several years now, but a number of technological issues still need to be overcome before they can be put into practice, as they represent breakthroughts. Whether for energy storage than for fourth generation nuclear reactors, molten salt environment used as coolant and/or as fuel is highly corrosive requiring a complexe choice of structural materials.
The aim of this subject proposed in the Corrosion and Materials Behavior Section is to study in depth the chemical properties of different chloride molten salts : the basic ternary salt (NaCl-MgCl2-CeCl3) but also the corrosion/fission/activation products that can be produced (MxCly with M=Cr, Fe, Te, Nd, Ni, Mo,…). The activity coefficients and solubility limits of these metallic elements will be determined using various techniques such as electrochemistry and Knudsen cell mass spectrometry. If required, this study can be completed by the phase transition temperature and heat capacity measurements using differential scanning calorimetry.
In the frame of the ANR project AAIMME dedicated to the Positron-Emission Tomography (PET), we propose a 24-month post-doctoral position that will focus on the development of signal processing methods for the detector ClearMind, designed at the CEA-IRFU. The detector is specifically developed to provide a precise interaction time in the sensitive volume. It consists of a scintillator PbWO4 detector, coupling with a Micro Channel Plate PhotoMultiplier Tube, whose signals are digitized using fast acquisition modules SAMPIC. The main advantage is to exploit both fast Tcherenkov and scintillation photons to reconstruct as accurately as possible the interactions inside the Crystal.
The analysis of the detector signal represents a major challenge: they are complex and intricated, thus, it necessitates a dedicated processing step.
The objective of this post-doc is to develop these trustworthy Machine Learning algorithms to reconstruct the properties of the gamma-ray interaction in the detector, with the highest achievable accuracy, using the detector signals.
The Postdoctoral position will be part of the PEPR Battery Hipohybat project. This project aims to develop high-power Na-ion batteries in close collaboration with academic partners such as Collège de France and IS2M. The Na+ ion conductivity in the bulk of the electrolyte, as well as at the electrode-electrolyte interface (EEI), are the two major criteria that need to be optimized in electrolytes to enable the development of fast-charging Na-ion cells.
The first strategy to increase bulk conductivity will be to use less viscous co-solvents, such as ethers or nitriles. However, these solvents exhibit poor electrochemical stability. Therefore, as a first step, the impact of adding such co-solvents to the state-of-the-art Na-ion electrolyte in various proportions will be studied to (i) determine their electrochemical stability windows and (ii) analyze their solvation/desolvation behavior, which is critical for their power rate capabilities. The fluorinated counterparts of the most promising co-solvents will also be investigated to improve oxidative stability and enable the formation of a stable solid electrolyte interphase at the negative electrode.
The second approach will focus on identifying additives that lead to ‘non-resistive’ interphases. Both commercial and in-house synthesized additives will be explored for this purpose. By tuning the three electrolyte components together, new formulations will be developed to achieve a better compromise between fast Na?-ion kinetics and stable cycling performance of Na-ion cells.
The objective of this study is to nuclearize the Delayed Hydride Cracking (DHC) experiment developed as part of Pierrick FRANCOIS PhD research (2020-2023). This experiment enables the reproduction of the DHC phenomenon in Zircaloy cladding under laboratory conditions to determine the material's fracture toughness in case of DHC: KI_DHC.
The term "nuclearize" refers to the adaptation of the experiment to test irradiated materials within dedicated shielded enclosures (called hot cells), where materials are handled using remote manipulators. The experimental protocols described in Pierrick FRANCOIS' thesis must therefore be modified, and ideally simplified, to allow for their implementation in hot cells. This will require close collaboration with the personnel responsible for the tests and the use of numerical simulation tools developed during the same PhD research.
The development of this hot cell procedure will be used by the postdoctoral researcher to assess the risk of HC during dry storage of spent fuel assemblies by quantifying the fracture toughness of irradiated claddings.
During Fukushima Daiichi nuclear reactor accident, several hundred tons of fuel debris (the mixture generated by the reactor core melting and its interaction with structural materials) have been formed. Japanese government plans to dismantle with 30 to 40 years Fukushima Daiichi nuclear power station, which implies recovering these fuel debris that are there. CEA is part to several projects aiming at mastering the risks due to aerosols generated during fuel debris cutting.
The post-doctoral work objective is to exploit the large experimental database created thanks to these projects in order to study the generation and transformation mechanisms of these cutting aerosols for both thermal and mechanical cutting. An important source of aerosol seems to be partial evaporation/condensation, close to fractional distillation. A thermodynamic modelling shall be proposed, coupled with some kinetic effects. For mechanical cutting, aerosol analyses shall be compared to fuel debris block microstructure to quantify a preferential release of some phases.
After a bibliographic study, a synthesis of the experimental results will be carried out and completed, where necessary, by chemical or crystallographic analyses. The aim will be to propose a modelling of these aerosol generation and transformation mechanisms.
The postdoctoral researcher will work within an experimental laboratory of about 20 staff within CEA IRESNE institute (Cadarache site, Southern France).
Fuel cells are a key technology for clean and sustainable energy systems, particularly in hybrid configurations for transport and stationary applications. However, their durability under real-world conditions remains a critical challenge. This project aims to address these challenges by exploring advanced control strategies that leverage state-of-the-art prognostic algorithms for fuel cell health assessment.
This postdoc offer focalizes on Advanced Control and Optimization, specifically the design of Fault-Tolerant Control (FTC).
Building on prior work in machine learning-based prognostics for fuel cell health, the focus of this project is to develop methods to utilize this prognostic information to optimize the operation of the fuel cell system.
By incorporating both model-based, data-driven approaches and testing on real test-bench platforms, the project aims to create robust, deployable solutions that enhance fuel cell durability while reducing the complexity and cost of implementation.
District heating networks (DHNs) play a vital role in energy transition strategies due to their ability to integrate renewable and waste heat effectively. In France, the national low-carbon strategy emphasizes expanding and optimizing DHNs, including smaller networks with multiple heat sources like solar thermal and storage. Smart control systems, such as model-predictive control (MPC), aim to replace manual, expert-based practices to enhance efficiency. However, deploying advanced control systems on small DHNs remains challenging due to the cost and complexity of hardware and maintenance requirements.
Current industrial solutions for large DHNs leverage mixed-integer linear programming (MILP) for real-time optimization, while smaller networks often rely on rule-based systems. Research efforts focus on simplifying MPC models, utilizing offline pre-calculations, or incorporating machine learning to reduce complexity. Comparative studies assess various control strategies for adaptability, interpretability, and operational performance.
This postdoctoral project aims to advance DHN control strategies by developing, testing, and deploying innovative approaches on a real DHN experimental site. It involves creating and comparing control models, implementing them in a physical simulator, and deploying the most promising solutions. Objectives include optimizing operational costs, improving system robustness, and simplifying deployment while disseminating findings through conferences, publications, and potential patents. The researcher will have access to cutting-edge tools, computational resources, and experimental facilities.