New Pt-containing nickel-based superalloys are currently under study to increase their mechanical resistance at high temperatures. Their environmental resistance should also be better. However, the Pt content in these alloys appears insufficient (Pt is expensive) and the influence of the remaining alloying elements is unknown, let alone in the new “biofuel” environments. This project gathers experts in the mechanics, corrosion, surface treatment and in situ characterization of superalloys to investigate the onset of degradation (chemical, mechanical and coupled). This settles the basis to study the impact of a coating (Al/Si/rare earth) on such degradation. The degradation at the gas/alloy and coating/alloy interfaces will be studied in model and real alloys under hot corrosion, oxidizing and fatigue conditions, which is quite original for these brand new Pt-containing superalloys. The impact on Science, Society and on the turbine industry could be thus impressive.

Subsurface modelling using geoscientific data is essential to understand the Earth and to sustainably manage natural resources. Geology and geophysics are two critical aspects of such modelling. Geological and geophysical models have different resolutions and are sensitive to different features. Considering only geological or geophysical aspects often leads to contradictions as creating an Earth model is a highly non-unique problem. In addition, the sensitivity of the data is limited and many objects cannot be differentiated by a single discipline. The only way to address this is solving the longstanding challenge of integrating of geological data and knowledge (orientation data, contacts and ontologies) and geophysical methods (physical fields). Recent techniques usually focus on features the data is sensitive to and merely use one discipline to falsify hypotheses from the other. Such approach prevents considering the full range of potential outcomes, and fails to exploit the sensitivity of both approaches. This project proposes a different philosophy to solve the challenge of connecting geological and geophysical modelling. It first involves the development of a novel method integrating the two model types in a single framework giving them equal importance. Geological and geophysical data will be modelled simultaneously through an implicit functional mapping one domain into the other by linking their respective models. This will allow the simultaneous recovery of compatible geological and geophysical models. Secondly, this project will use a new hybrid deterministic-stochastic optimisation technique to explore the range of subsurface scenarios to estimate the diversity of features that cannot be differentiated based on the available data. Thirdly, after proof-of-concept, the method will be applied to two cases: imaging of a mantle uplift in the Pyrenees Mountains (France/Spain), and study of potential new subsurface scenarios around the Kevitsa mine (Finland).

Plant proteins, particularly from oilseed meals, are promising renewable resources for texturizing ingredients in food and cosmetic matrices. Application of these ingredients would get rid of petroleum-based products in cosmetics, and to strengthen the use of animal proteins in food, responding to major socio-economic challenges. However, they have insufficient performance compared to currently used products. The functional limitations of plant proteins currently represent a major bottleneck to their industrial development, which must be improved to reach the quality standards of animal proteins and synthetic molecules. Enzymatic transformation processes can be implemented on protein isolates to improve their functional properties. Among these processes, proteolysis and enzymatic cross-linking are the most feasible, sustainable and compatible processes for the use of plant proteins in cosmetics and food industries. Nevertheless, the understanding and control of obtaining functional protein products by these two processes are limited by the lack of knowledge of the relationships between the product characteristics and their properties. Moreover, the process implementations result from laborious and empirical experimental approaches, leading to non-optimal production ways with regard to industrial technical, economic (cost, production duration) and environmental criteria. The main objective of PROSPER project is to develop a generic methodology for obtaining tailor-made plant protein ingredients for targeted applications in food and cosmetic, by controlling enzymatic transformation processes. Its purpose is to allow (i) an improvement in the level of understanding of the relationship between product properties and functionalities; (ii) wider use of plant proteins as functional ingredients; (iii) technological innovation to improve protein functionalities and open up new fields of application. To meet this objective, an original strategy of product engineering will be applied, structured around four main work packages. Reliable analytical tools for the characterization of the products obtained and the monitoring of proteolysis and enzymatic crosslinking processes will be developed initially. Then, the methodology aims to associate the characteristics of the products obtained with functional properties of interest using supervised machine learning methods. In a third step, relationships can be established between the characteristics of the products and a kinetic monitoring parameter of the process, as a representative criterion of a targeted functionality. Modeling/simulation tools for enzymatic transformation processes, based on experimental data regressions, will be developed. Obtained models will be coupled with the established correlations to numerically explore the influence of operating conditions sets on the targeted functionalities, and thus to identify in a rational way the original production routes of a product with targeted functionality. Then, in a last step, these models will be associated with multi-criteria optimization and decision-making tools, in order to establish the optimum functioning of these transformation routes on technical and economic criteria and to analyze these routes towards environmental impacts through life cycle analysis studies.

In the context of spintronics and its applications, manipulating the magnetization at fast rates is a major goal. The SPOTZ project aims to unveil the (sub)picosecond dynamics of the magnetization induced by ultrashort current pulses, and in particular, by spin-orbit torques. Next, magnetization switching will be attempted by using ultrashort current pulses and the magneto-resistance will be studied in the THz regime in order to ultimately demonstrate a fully electrical and ultrafast nano-sized magnetic memory.