Development of an innovative way of end-of-life plastics recycling by hydrothermal depolymerization
End of life plastics are scarcely recycled due to technical, health or structural constraints. To address this issue, a solvolysis route may be considered in order to recover monomers or other valuable molecules. Although good results are obtained after polymers sorting, this method remains sensitive to the composition of incoming flows, as well as the presence of contaminants. The Supercritical and Decontamination Processes Laboratory has developed an original depolymerization method in hydrothermal conditions (150 to 300°C and autogenous pressure) allowing to consider treatment of a mixture of end of life polymers (PET, PU, PC, PE, PVC). A parametric study will be carried out on a mixture of polymers of known composition by studying the influence of process parameters on the composition of the aqueous and organic phases, to define performance criteria such as conversion and depolymerization yields. Several end-of-life plastic wastes, alone or in a mixture, will be considered, to highlight a possible synergistic effect on the recovery of all or part of the recoverable monomers or products. Finally, an energy and mass balance will be implemented to study the complete life cycle of the process and to evaluate the relevance of the depolymerization process in hydrothermal conditions.
Recycable and biosourced resins for lightweight battery composites
Fibre reinforced polymers are high performance materials obtained from thermoset resins and a continuous fibre reinforcement that can be found in battery casing at different sublevels (modules, pack), and that are, as of today, petrosourced and hardly recycled.
Possible recycling paths for these materials consist in breaking covalent bonds from the reticulated resin though chemical (acids or strong oxydants) or thermal (cracking) treatments, leading to important energy and environmental costs. Despite of these disadvantages, such approaches are currently used to recover carbon fibers that represent most of composite cost, the resin matrix being lost in the process.
The post-doc position will consist in developing alternative resins to those currently used in composites and will be biosourced and recyclable. Biosourced monomers will be selected and/or modified and the recyclability will be obtained by incorporating dynamic bonds into the system. The formulation will be then optimized according to chemical and mechanical characterizations. Then, a foamed version of the resin will be developed and characterized.
The developed resins will then be used in the fabrication of fibre reinforced composites (carbon and/or natural fibers) that will also be characterized and optimized. At the end of the project, a composite prototype for the application in batteries will be fabricated using the developed knowledge.
Conception and deployment of innovative optimal control strategies for smart energy grids
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.
Post-doctoral position in Solid State Electrochemistry / Ceramic and metallic materials / High temperature corrosion
High-temperature electrochemical solid oxide (SOC) devices (650-850 °C) are considered as one of the most promising technologies thanks to various advantages such as a high efficiency, a relative low cost and a good reversibility in fuel cell (SOFC) and electrolysis (SOEC) operating modes. To better understand and limit metallic interconnect oxidation and chromium evaporation through the use of coatings remains a key challenge for the optimization of the system durability in SOFC and SOEC operation (degradation rate 3000 h). The post-doctoral work represents the main part of this project and is exclusively funded by it. The evaluation of protective coatings and a contact layer will be mainly performed thanks to electrochemical characterizations of performances and durability of the adjacent cell, and post-test microstructural characterizations as well compared to the bare steel. This work should lead to at least 1 publication and 1 presentation at EFCF conference in 2026.
Digital correction of the health status of an electrical network
Cable faults are generally detected when communication is interrupted, resulting in significant repair costs and downtime. Additionally, data integrity becomes a major concern due to the increased threats of attacks and intrusions on electrical networks, which can disrupt communication. Being able to distinguish between disruptions caused by the degradation of the physical layer of an electrical network and an ongoing attack on the energy network will help guide decision-making regarding corrective operations, particularly network reconfiguration and predictive maintenance, to ensure network resilience. This study proposes to investigate the relationship between incipient faults in cables and their impact on data integrity in the context of Power Line Communication (PLC). The work will be based on deploying instrumentation using electrical reflectometry, combining distributed sensors and AI algorithms for online diagnosis of incipient faults in electrical networks. In the presence of certain faults, advanced AI methods will be applied to correct the state of the health of the electrical network's physical layer, thereby ensuring its reliability.
MULTI-CRITERIA ANALYZES OF HYDROGEN PRODUCTION TECHNOLOGIES BY ELECTROLYSIS
LITEN, strongly involved in electrolysis technologies, wishes to compare via a multi-criteria analysis all electrolysis technologies currently available commercially (AEL, PEMEL), in the pre-industrialization phase (SOEL), or in R&D (AEMEL and PCCEL).
Our previous studies were based on specific use cases (fixed hypotheses on the size of the factory, the source of electricity, the technology, etc.).
The objective of this new work is to be able to position the different electrolysis technologies according to parameters which will be defined at the start of the project, these parameters being of a contextual type (e.g. number of operating hours, expected flexibility), technical ( ex yield, lifespan) or technical-economic (ex CAPEX OPEX) and environmental (ex GHG impacts, materials). The aim here will be to develop an original methodology which makes it possible to define the areas of relevance of each of the electrolysis technologies according to these parameters, depending for example on the cost of the hydrogen produced and its environmental impact
Advanced reconstruction methods for cryo-electron tomography of biological samples
Cryo-electron tomography (CET) is a powerful technique for the 3D structural analysis of biological samples in their near-native state. CET has seen remarkable advances in instrumentation in the last decade but the classical weighted back-projection (WBP) remains by far the standard CET reconstruction method. Due to radiation damage and the limited tilt range within the microscope, WBP reconstructions suffer from low contrast and elongation artifacts, known as ‘missing wedge’ (MW) artifacts. Recently, there has been a revival of interest in iterative approaches to improve the quality and hence the interpretability of the CET data.
In this project, we propose to go beyond the state-of-the-art in CET by (1) applying curvelet- and shearlet-based compressed sensing (CS) algorithms, and (2) exploring deep learning (DL) strategies with the aim to denoise et correct for the MW artifacts. These approaches have the potential to improve the resolution of the CET reconstructions and facilitate the segmentation and sub-tomogram averaging tasks.
The candidate will conduct a comparative study of iterative algorithms used in life science, and CS and DL approaches optimized in this project for thin curved structures.
Simulation of thermal transport at sub-Kelvin
Thermal management in quantum computers is an urgent and crucial task. As the number of qubits rapidly scales, more electric circuits are placed close to qubits to operate them. Joule-heating of these circuits could significantly warm the qubit device, degrading its fidelity. With intensive activity in quantum computing at Grenoble, we (CEA-LETI, Grenoble, France) are looking for an enthusiastic post-doc researcher to study thermal transport at cryogenic temperature (sub-Kelvin).
The post-doc will apply the finite-element non-equilibrium Green’s function [1], developed in the group of Natalio Mingo at CEA-Grenoble, to simulate phonon transport in various designed structures. The simulation result promotes comparison with on-going experiments and constructive discussions in order to optimize the thermal management.
[1] C. A. Polanco, A. van Roekeghem, B. Brisuda, L. Saminadayar, O. Bourgeois, and N. Mingo, Science Advances 9, 7439 (2023).
Exploring microfluidic solutions for manufacturing targets for fusion power generation
As part of a call for projects on "innovative nuclear reactors", the TARANIS project involves studying the possibility of energy production by a power laser-initiated inertial confinement fusion power plant. The current context, which encourages the development of low-carbon energies, and the fusion experiments carried out by the NIF's American teams, make it very attractive to conduct high-level research aimed at eventually producing an economically attractive energy source based on inertial fusion.
Among the many technical hurdles to be overcome, the production of fusion targets with a suitable reaction scheme compatible with energy production is a major challenge. The CEA has the know-how to produce batches of capsules containing the fusible elements of the reaction. However, the current process is not suitable for mass production of hundreds of thousands of capsules per day at an acceptable cost.
One high-potential avenue lies in the use of microfluidic devices, for which the Microfluidic Systems and Bioengineering Laboratory (LSMB) of the Health Technologies and Innovation Department (DTIS) of CEA's DRT has recognized expertise.
Development of noise-based artifical intellgence approaches
Current approaches to AI are largely based on extensive vector-matrix multiplication. In this postdoctoral project we would like to pose the question, what comes next? Specifically we would like to study whether (stochastic) noise could be the computational primitive that the a new generation of AI is built upon. This question will be answered in two steps. First, we will explore theories regarding the computational role of microscopic and system-level noise in neuroscience as well as how noise is increasingly leveraged in machine leaning and artificial intelligence. We aim to establish concrete links between these two fields and, in particular, we will explore the relationship between noise and uncertainty quantification.
Building on this, the postdoctoral researcher will then develop new models that leverage noise to carry out cognitive tasks, of which uncertainty is an intrinsic component. This will not only serve as an AI approach, but should also serve as a computational tool to study cognition in humans and also as a model for specific brain areas known to participate in different aspects of cognition, from perception to learning to decision making and uncertainty quantification.
Perspectives of the postdoctoral project should inform how future fMRI imaging and invasive and non-invasive electrophysiological recordings may be used to test theories of this model. Additionally, the candidate will be expected to interact with other activates in the CEA related to the development of noise-based analogue AI accelerators.