In-situ measurement of liquid composition by digital in-line holography
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.
Optimization of energy transition scenarios through a dynamic Life Cycle Assessment approach
The modelling of the energy transition, with a projection until 2050 and adaptable to different countries or strategies, is complex in terms of LCA because it involves many parameters:
- a dozen possible energies, with evolutionary inventories of construction of electricity generation/storage infrastructure
- a difficulty to estimate the future of technologies for a given sector
- electricity generation in connexion with national consumption
- very contrasting scenarios, including more or less rapid increases in renewables and a decrease in nuclear power, offset or not by gas-fired combined cycle power plants
- a need to provide for several forms of electricity storage depending on the size of the unmanageable energy stock, with power levels depending on the storage time
- the correlation or not of storage power with the level of interconnection of European electricity networks.
The work will consist of analysing the inventories available in the Ecoinvent database linked to Simapro, modifying them according to the foreseeable technologies for the medium term, continuing modelling in Python language to include all the parameters.
The objective is to determine the best possible environmental trajectories for the French energy transition.
Modelling of uranium precipitation kinetics as a function of pH. Application to fluidized bed reactor
The Orano plant in Niger (Somaïr) precipitates its uranium concentrate in a fluidized bed reactor by adding sodium hydroxide. The concentrate obtained contains around 6% sodium which leads to converter penalties. Orano carried out tests at the end of 2019 on a fluidized bed in the laboratory to change the operating point of precipitation and preferentially form UO3 via a change in pH. To refine the management of the industrial unit, it is necessary to model the precipitation reactions of uranium. The candidate will have to propose and calibrate a competitive precipitation model for Na2U2O7 and UO3 based on the equilibrium constants and reaction kinetics, as a function of the pH within the reactor. In particular, the model should make it possible to understand the impact of pH on the distribution of the two main species identified in the concentrate: Na2U2O7 and UO3. This chemical model should serve as input to an existing physical model of the fluidized bed reactor. An extension of the model to other precipitation reagents, in particular magnesia, could also be studied.
Dynamic monitoring by light scattering of mass transfer between two phases in multiphase flows
The understanding and the modeling of recycling processes studied at CEA, require the measurement of both local and average properties of multiphase flows involved in chemical engineering devices. Moreover, as the R&D studies are generally conducted on small-scale experiments, access to these quantities is often difficult, especially considering that measurement methods should not disturb the observed system. In this context, optical methods, associated to extensive and rigorous physical simulations of light/particles interactions, are particularly relevant and, accordingly under specific developments since several years. Therefore, the DMRC/LGCI (CEA Marcoule), in collaboration with the laboratory IUSTI (CNRS and Aix-Marseille University), develops two optical interferometric techniques suitable for R&D studies: the Digital In-line Holography (DIH) and the Rainbow Refractometry (RR). Previous works have shown that DIH allows a simultaneous measurement of 3D-positions, shape and size of flowing particles, even considering astigmatic geometries, while RR gives access to the size and refractive index of each particle or of set of particles, which considering linear optics is directly linked to their composition. This study aims to go further in multiphase flows characterization with these two technics by following three main objectives: 1) propose original solutions for the characterization of materiel compositions thanks to DIH, 2) deepen inverse methods in RR to allow the study of clouds of particles with variable compositions and to take into account gradients of concentration around a sessile drop, 3) evaluate the relevance of these technics for lab on chip systems.
Crystalline materials for the selective extraction of monovalent metal cations: understanding the link between the crystalline structure and the selectivity
The selective extraction of monovalent metal cations from aqueous solutions have complex compositions is a key step in many energy-related fields. In this work, specific adsorbents for Cs, to decontaminate effluents produced by the nuclear industry, and for Li, to extract this strategic metal for the development of batteries, will be studied. Due to their modularity in terms of porosity and structure, crystalline oxides (as zeolites) are promising for the selective extraction of such cations. With a view to understand the role of their microstructure on their sorption/desorption performances and mechanisms, identify the selective sorption sites within these crystal structures is crucial.
For that purpose, the objective of this research work is, on the one hand, to synthesize crystal structures allowing the selective sorption of Cs or Li. Then, by using fine characterization techniques at the atomic scale as well as structures reconstruction effort, we will identify the location of selective sorption sites within these materials and, in this way, better understand their sorption mechanisms and properties.
For this post-doctoral position, we are looking for a PhD in material science with strong skills in synthesis and characterization of crystalline materials by X-ray diffraction. Experience in the study of crystalline oxides, such as zeolites, would be an advantage.
Post-doc: CNN neural network – managing data uncertainty in the learning database.
The aim is to develop algorithms able to take into account the uncertainty in the learning database of neural networks. The project fits into the context of the dynamic state estimation of liquid-liquid extraction and benefits of its knowledge-based simulator as well as industrial data. Indeed, the status of an industrial chemical process is accessible through operating parameters and available monitoring measures. However, the measures being inherently associated with uncertainty, it is necessary to make the data consistent with process knowledge. Therefore, the goal is to find the best data set of operational parameters (input of the knowledge-based simulator) to provide the model to estimate the real process state known through monitoring measures (output of the knowledge-based simulator). A convolutional neural network (CNN) is being developed in another postdoctoral project to solve the inverse problem to find the best input thanks to the measured output. A consistent set of operating parameters is going to be obtained and state of the process is going to be known during the dynamic regime of the liquid-liquid extraction process. This first step is to evaluate the impact of the uncertainty of operational parameters on the outputs of the knowledge-based model. This step will need to connect the knowledge-based model to URANIE, internal platform developed by CEA ISAS. This knowledge must be taken into account in the second part of the project. The uncertainty observed on the outputs should be taken into account in the learning loop to improve the estimation of the operational parameters by the CNN. The impact of these uncertainties on the CNN computed results must be assesed in order to trust the ability of the CNN to estimate the state of the process.
Through this project, we are at the heart of the thematic of digital simulation for the best control of complex systems.
Researcher in Artificial Intelligence applied to self-driven microfluidic
This postdoctoral position is part of the 2FAST project (Federation of Fluidic Autonomous labs to Speed-up material Tailoring), which is a part of the PEPR DIADEM initiative. The project aims to fully automate the synthesis and online characterization of materials using microfluidic chips. These chips provide precise control and leverage digital advancements to enhance materials chemistry outcomes. However, characterising nano/micro-materials at this scale remains challenging due to its cost and complexity. The 2FAST project aims to utilise recent advances in the automation and instrumentation of microfluidic platforms to develop interoperable and automatically controlled microfluidic chips that enable the controlled synthesis of nanomaterials. The aim of this project is to create a proof of concept for a microfluidic/millifluidic reactor platform that can produce noble metal nanoparticles continuously and at high throughput. To achieve this, feedback loops will be managed by artificial intelligence tools, which will monitor the reaction progress using online-acquired information from spectrometric techniques such as UV-Vis, SAXS, and Raman. The postdoctoral position proposed focuses on AI-related work associated with the development of feedback loop design, creation of a signal database tailored for machine learning, and implementation of machine learning methods to connect various data and/or control autonomous microfluidic devices.
Agglomerate breakage model and homogenisation by DEM simulations: Calibration with tomographic micro-compressions in X ray beam line Soleil
Context:
The reference ceramic fabrication process involves three main stages: grinding, pressing, and sintering. Pellet compaction during pressing relies on three main densification steps rearrangements by motion, compaction by strain, and agglomerate fractures by compression. This research project aims to explore the influence of the pressing step on the microstructure behavior during the sintering process. The study focuses on a powder composed of agglomerates with a microstructure based on a homogeneous mix of TiO2-Y2O3, TiO2 for surrogate UO2 and Y2O3 for surrogate PuO2. Each agglomerate consists of unbreakable elementary particles included in breakable aggregates, synthesized using the Cryogenic Granulation Synthesis Process (CGSP) [1].
Recent investigations at the Anatomix X-ray beam line in the synchrotron Soleil [2] have validated the results of tomographic micro-compressions, aligning with Kendall's theory, Fig 1. The experiments involved one-way cyclic micro-compression tests on agglomerates subjected to a simple load and unload cycle until breakage. Tomographic post-treatments provided insights into porosities, crack initiation, and propagation. Several DEM simulation studies have also been used to explore agglomerate behavior under dynamic or quasi-static loading with and without breakage, however without fully calibrating the breakage model [3], [4], [5].