The objective of the DynaMoBat project is the analysis and quantification of the evolution of the 3D morphology of all-solid Li-ion batteries during their manufacture and their use in electrochemical cycling. The dynamic study in 3D morphology will be based on the use of X-ray tomography imaging techniques at different scales (? and nano). The non-destructive aspect, the possibility of coupling between imaging and spectroscopy and the recent improvements in terms of spatial resolution and rapid acquisition make X-ray tomography an ideal tool for the development of operando experiments. The project will be based on the optimization of electrochemical cells and compression and annealing cells dedicated to X-ray tomography (MATEIS and LRCS). The 3D data will be segmented using deep learning networks (ARMINES) to identify the different materials. The 4D dynamic data will be used to calculate 3D displacement maps allowing to follow the sintering phenomena and the propagation of cracks according to the experimental parameters. The quantification of these mechanical properties will be correlated with the evolution of electrochemical performance, to detect the origins of polarizations and capacity losses. Thus, the 3D analysis and quantification of the morphological evolution of the different constituents, depending on the state of charge in operation and the pressure applied for manufacturing, is the heart of this DynaMoBat project. Its ambition is to provide quantitative 4D data, making it possible to improve our understanding of limiting phenomena, and to open new avenues of optimization in the manufacture of these all-solid devices, which has an essential need to acquire quickly greater technological maturity.

Liquid argon (LAr) detectors have reached an unprecedented level of development and are now used in many areas of particle physics and astroparticles. Doping LAr with xenon can further extend the fields of application, as Xe acts as a wavelength shifter and speeds up the scintillation, keeping the scalability of LAr technology almost unchanged. For example, Xe-Ar technology can be of interest in the development of pediatric Xe-doped LAr PETs, or in the field of neutrinoless double beta decay, by mixing 136Xe-enriched Xe in LAr. This project is dedicated to provide an extensive characterization of the Xe-Ar mixture in terms of maximum solubility, thermodynamics, and scintillation and ionization properties as a function of the Xe concentration.

One of the European Union’s objectives in climate change consists of reaching net-zero greenhouse gas emissions by 2050. Such perspective puts the metallic materials industry, as a large contributor to carbon emissions, under tremendous pressure for change and requires the existence of robust and qualitative computational materials strategies to design, to enhance, to calibrate, with a very high degree of confidence, new metallic materials technologies with a limited environmental impact. From a more general perspective, the in-use properties and durability of metallic materials are strongly related to their microstructures, which are themselves inherited from the thermomechanical treatments. Hence, understanding and predicting microstructure evolutions are nowadays a key to the competitiveness of industrial companies, with direct economic and societal benefits in all major economic sectors. Multiscale materials modeling, and more precisely, simulations at the mesoscopic scale, constitute the more promising numerical framework for the next decades of industrial simulations as it compromises between the versatility and robustness of physically-based models, computation times, and accuracy. In this context, a breakthrough numerical strategy to describe the microstructure evolutions of metallic materials during complex industrial thermomechanical treatments has been developed through the ANR Industrial Chair DIGIMU (Oct.2016-Mar.2021). The outcoming DIGIMU® software is now available for the industry, and able for quantitative predictions of microstructure evolutions on material volumes in the range of one mm3, with typical computation times of a few days when performed on a simple laptop. Such simulations and computational efficiency were a dream ten years ago, a reality now with the DIGIMU developments. The purpose of the RealIMotion project is to push the limits of numerical metallurgy further and develop a promising new numerical framework coupled with a machine learning physically-based strategy to aim for massive computations, consideration of much larger material volumes, in connection with macroscopic simulations and still with reasonable computation times to be compatible with industrial daily uses. Such a leap in the models will open the door for industrial partners to tune numerically thermomechanical routes, build microstructure-targeted industrial processing maps and automatically propose new enhanced homogenized models. RealIMotion project brings the cutting-edge and exploding strategies of data science, physically-based models, and machine learning at the service of industrial metallurgy. Major advances regarding the concept of digital twins in metallurgy and a worldwide leading position of the RealIMotion partners concerning Integrated Computational Materials Engineering (ICME) developments are expected outcomes of the Chair program. The RealIMotion PI is a pioneer and a world leader in mesoscopic scale modeling of microstructure during hot metal forming. The French industrial consortium supporting the developments of digital materials in the context of hot metal forming has grown in the RealIMotion proposal. Constellium and Aperam are new partners. Framatome, Aubert&Duval, ArcelorMittal, CEA, and Safran brought new targeted applications on zirconium alloys, aluminum alloys, dual-phase steels, and new generation nickel based superalloys. The students recruited in the RealIMotion Chair will enjoy a perfect environment to become experts in computational metallurgy, digital twins, and IA and meet the metallurgical industry needs for the future. The expected benefits will be job-creating for all the partners involved. The RealIMotion Chair will contribute to the materials science teaching effort by offering to the universities concerned free access to DIGIMU® software for pedagogic purposes, and a turnkey tutorial set adapted for practicum.

Amid the evolution of corporate giants, the focus of researchers, policy makers, and the media shifted to understand how such increased concentration affects consumers and small producers. The grocery industry has received a lot of attention because of (i) higher concentration levels than in other sectors and (ii) its market structure which involves vertical relations with some large producers (Nestle, Unilever, etc.) selling to consumers through large retailers (Carrefour, Casino, etc.). Concentration in these markets can have detrimental effects for consumers and for small producers, such as farmers. The French EGALIM law is a prominent example of how governments worldwide establish laws to protect consumers and small producers from unfair trading practices of large retailers and large producers. In theory, however, instead of being negative for consumers, industrial concentration (dominant producers and retailers) could also be a sign of greater competition, where the most efficient firms, that have the lowest costs, gain market shares. This type of concentration is beneficial for consumers as, in that case, firms pass-on cost-savings to consumers in form of lower prices or new and improved products (i.e., more innovation). The economic literature, however, has not yet fully understood how producer concentration and retailer concentration affect prices, profits, profit-sharing, and innovation. Our project intends to shed new light on this question using innovative structural models and large data sets. We will investigate how retail concentration and producer concentration interact, and quantify the effects on prices, profits, profit-sharing and innovation. The aim is to provide empirical evidence for policy makers on how to regulate contracts between producers and retailers to (i) protect consumers and small producers and (ii) lift innovation constraints.

Fluid pressure perturbations induce earthquakes at different scales, both in natural seismic swarms or during anthropogenic activities in geological reservoirs. In both contexts, seismicity may either stop on its own or be the precursor to larger, damaging earthquakes. For seismic risk mitigation and for safer energy exploitation, it is of crucial importance to anticipate the evolution of swarms. With this aim, understanding the processes at depth that trigger and drive seismicity is key, but the complex interaction between fluid pressure, aseismic deformation and earthquakes is still an open question. Motivated by recent models that conciliate fluid pressure and aseismic processes, the INSeis project aims to shed new light on the driving mechanisms of both natural and artificially induced swarms. The final goal is to propose common interpreting models in order to better anticipate swarms evolution. This project focuses on a refined analysis of seismological data from three well-instrumented sites in Europe, with different contexts and scales: (1) geothermal activities in Alsace (France), (2) natural swarms in the Corinth Gulf (Greece), and (3) in-situ experiments of induced seismicity at a decameter scale (France, Switzerland). New physical models and interpretations will be tested and validated with the support of up-to-date hydro-mechanical simulations, that compute seismicity together with the full pressure and deformation history. Finally, we will take advantage of the differences in scale, geological settings and conditions to highlight similarities in the physical processes, in order to bridge the gap in interpretations among geological objects. Finally, through statistical means, we will test and evaluate which metrics and which strategies allow for the best anticipation of the swarm behaviors.