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.

Causal learning

As part of a project that concerns the creation of innovative materials, we wish to strengthen our platform in its ability to learn from little experimental data.

In particular, we wish to work firstly on the extraction of causal links between manufacturing parameters and properties. Causality extraction is a subject of great importance in AI today and we wish to adapt existing approaches to experimental data and their particularities in order to select the variables of interest. Secondly, we will focus on these causal links and their characterization (causal inference) using an approach based on fuzzy rules, that is to say we will create fuzzy rules adapted to their representation.

Modeling the corrosion behavior of stainless steels in a nitric acid media with temperature

Controlling the aging of equipment materials (mainly stainless steel) of the spent nuclear fuel reprocessing plant is the subject of constant attention. This control requires a better understanding of the corrosion phenomena of steels by nitric acid (oxidizing agent used during the recycling stages), and ultimately through their modeling.
The materials of interest are Cr-Ni austenitic stainless steels, with very low carbon content. A recent study on Si-rich stainless steel, which was developed with the aim of improving the corrosion resistance of these steels with respect to highly oxidizing environments [1 , 2 ]; showed that the corrosion of this steel was thermally activated between 40 °C and 142 °C with different behavior below and above the boiling temperature (107 °C) of the solution [3]. Indeed, between 40°C and 107°C, the activation energy is 77 kJ/mol and above boiling point, it is much lower and is worth 20 kJ/mol. This difference may be due to a lower energy barrier or a different kinetically limited step.
The challenge of this post-doctoral subject is to have a predictive corrosion model depending on the temperature (below and beyond boiling). With this objective, it will be important to analyze and identify the species involved in the corrosion process (liquid and gas phase) as a function of temperature but also to characterize the boiling regimes. This model will be able to explain the difference in activation energies of this Si-rich steel below and above the boiling temperature of a concentrated nitric acid solution but will also make it possible to optimize the processes of the factory where temperature and/or heat transfer play an important role.

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.

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.

Study of cleavage brittle crack initiation sites in low alloy bainitic steels with segregations

Macro-segregations of alloy elements and impurities in heavy forged 16-20 MND5 components or Pressurized Water nuclear Reactors induces significant fluctuations of these mechanical properties, and in particular, of dynamic and fracture toughness. Such a macro-segregation occurs during the solidification of the ingot and can still be observed in the final component, even after significant discarding performed on purpose during the fabrication process.
Recent results have confirmed the essential role played by specific carbides located close to grain boundaries, even for moderately segregated materials. The main objective of this Post-Doctoral internship is to precisely study some clivage initiation sites on these alloys to determine the types of carbides and the cristallographic conditions that promote crack initiation. A statistical analysis will then be performed to identify the population of these carbides within the microstructure of the material. The experimental results will be used as entries of a local approach to brittle fracture model.

Cryogenic separation of gas mixture

Generative AI for model driven engineering

Generative AI and large language models (LLMs), such as Copilot and ChatGPT can complete code based on initial fragments written by a developer. They are integrated in software development environments such as VS code. Many papers analyse the advantages and limitations of these approaches for code generation. Besides some deficiencies, the produced code is often correct and the results are improving.

However, a surprisingly small amount of work has been done in the context of software modeling. The paper from Cámara et al. concludes that while the performance of the current LLMs for software modeling is still limited (in contrast to code generation), there is a need that (in contrast to code generation) we should adapt our model-based engineering practices to these new assistants and integrate these into MBSE methods and tools.

The goal of this post-doc is to explore generative AI in the context of system modeling and associated tool support. For instance, AI assistance can support the completion, re-factoring and analysis (for instance identified design patterns or anti-patterns) at the model level. Propositions are discussed in the team and in a second step prototyped and evaluated the mechanism in the context of the open-source UML modeler Papyrus.

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.

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].

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