Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Hierarchical multifunctional composites with thermoelectrically powered autonomous structural health monitoring for the aviation industry (HARVEST)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Action type: Research and Innovation actions
Countries involved: Italy,Spain,Germany,Spain,Germany,Belgium,Belgium,Greece,Greece,Greece,Denmark
Call: Breakthrough innovation (H2020-MG-2017-Two-Stages, 2017)
Start date: 2018-09-01
End date: 2021-11-30
Duration (months): 39.0
Total project cost (EUR): 3999921.25
EU funding (EUR): 3999921.25
Participants: 11
Project status: CLOSED

HARVEST will unleash the potential of breakthrough technologies by creating integrated multifunctional systems for Aeronautics via the development of i) Structural composites, comprised of hierarchical carbon fiber (CF) reinforcements and an innovative thermoset 3R (repair, recycle and reprocess) epoxy matrix with ThermoElectric Generation (TEG) and self-repair capabilities, ii) Autonomously TEG -driven integrated systems for on- and off-line structural health monitoring-(SHM) and iii) Wired and low-power wireless SHM data transmission and mining system. The innovative intelligent materials and parts, will be manufactured in purposefully developed pilot lines aiming at reducing production time and costs. CFs yarns or textiles will be coated with nanomaterials using facile & environmentally friendly deposition and doping methods in a Roll-to-Roll (R2R) pilot line targeting dramatically increased TEG performance compared to existing composites, carbon and organic based materials. Innovative TEG-hierarchical composites will be manufactured with new generation 3R thermoset matrix systems enabling out of autoclave manufacturing and self-repair. These will be interfaced with a purposely designed hardware to (i) power inherent functionalities (e.g. strain, damage or UV-exposure sensing), (ii) drive external elements (e.g. piezo electric sensors for SHM) and (iii) transmit sensing signals to a remote panel. The autonomous SHM systems will increase the safety of civil aviation; reduce emissions and maintenance & life cycle costs. The proposed technologies will be finally integrated in two aircraft demonstrator parts, targeting areas with temperature gradients (e.g. engine vs. environment, inside vs. outside fuselage during flight) or where quick heat dissipation is essential (e.g. landing gear after take-off). The location of suitable heat sinks in real structures will be established using advanced numerical tools to identify thermal gradients in operating environment.

Keywords: multifunctional structural materials,multi scale composites,thermoelectric energy,generation,curing monitoring,strain,damage,uv sensing,autonomous structural health monitoring
use of multiscale modElling to Minimize coke ProducTion during the methanol-to- HYdrocarbon process (EMPaTHY)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Chemistry
Countries involved: Italy
Call: Individual Fellowships (H2020-MSCA-IF-2018, 2018)
Start date: 2019-11-01
End date: 2021-10-31
Duration (months): 24.0
Total project cost (EUR): 171473.28
EU funding (EUR): 171473.28
Participants: 1
Project status: CLOSED

The methanol-to-hydrocarbon (MTH) process is a versatile catalytic process that are gradually playing a more important role in the economy. However, an important factor that is inhibiting the profitability of MTH is accumulation of coke in the pores of the catalyst during operations. To reduce or eliminate the coke formation during MTH operations, it is necessary to have a detailed mechanistic insight into its cause of formation. In this proposal, I will achieve this insight through a computational modelling strategy. I will study the mechanism of the MTH process at various time- and length scales, using various computational methodologies. I will use computational fluid dynamics (CFD) to study the fluid flow at the reactor scale and the diffusion in the macropores. I will use kinetic Monte Carlo (kMC) and molecular dynamics (MD) to study the diffusion in the meso- and micropores. Finally, I will use density functional theory (DFT) to study the reactions at the active sites. The processes studied at the various length scales will be coupled together through a multiscale methodology. Multiscale modelling has steadily evolved over the past decade, but the concept is still at the proof-of-principle stage where the methodology has been demonstrated for simple test systems such as CO oxidation. The methodologies that will provide data to the multiscale simulation, CFD, kMC, MD, and DFT have all reached a high level of maturity. Now is the right moment to use a multiscale methodology to couple these methodologies together and solve the problem of coke formation in the MTH process. The potential outcomes are the following: 1) an understanding of how coke is formed in the MTH process; 2) a larger acceptance in the catalysis community to use multiscale modelling in the design of new catalysts; and 3) tighter interdisciplinary collaborations.

Keywords: multiscale simulations,kinetic monte carlo simulations,model construction of catalyst particles
ADVANCE: Sophisticated experiments and optimisation to advance an existing CALPHAD database for next generation TiAl alloys (ADVANCE)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: Clean Sky 2
Action type: Research and Innovation actions
Partnership: CS2 (Art-187)
Countries involved: Austria,Germany,Germany,Sweden
Call: Optimization of TiAl CALPHAD databases of respective material systems (H2020-CS2-CFP07-2017-02, 2017)
Start date: 2018-10-01
End date: 2022-06-30
Duration (months): 45.0
Total project cost (EUR): 1112783.75
EU funding (EUR): 1112783.75
Participants: 4
Project status: CLOSED

To meet the Clean Sky 2 goals to reduce aircraft noise and CO2 and NOx emissions, an optimised titanium aluminide (TiAl) CALPHAD database is needed. Such a database is an integral step to meet these goals as it can be used in the aviation industry to improve existing alloys and create new alloys important in the manufacture of lightweight turbine engine components. The primary goal of this proposal is to experimentally determine critical binary and ternary phase data for several key systems and to incorporate this information into an existing TiAl thermodynamic database. An additional kinetic database will also be updated and provided at the end of the project. By optimising the phase data, this reduces or eliminates the need to conduct costly trial and error experiments, and ultimately speeds the time to market for the turbine engine components. To achieve the goals of this Call for Proposal, a critical analysis of existing state-of-the-art CALPHAD databases, supplied by partner Thermo-Calc Software AB (TCSAB), identified the missing or unreliable data for several important TiAl systems. Data for these systems will be collected through a series of sophisticated experiments on well-characterised high-purity alloys. The experiments will be carried out by the partners Max-Planck-Institut für Eisenforschung GmbH (MPIE), Helmholtz-Zentrum Geesthacht Zentrum für Material- Und Küstenforschung GmbH (HZG) and Montanuniversität Leoben (MUL), who are all recognised for their long-term and well-documented expertise in preparing and characterising TiAl alloys. At key milestones throughout the project, TCSAB, with the assistance of other partners, will critically evaluate the experimental data and conduct thermodynamic assessments of all the systems under investigation. The final analysis will result in two optimised and advanced CALPHAD databases that can immediately be used for alloy design by MTU Aero Engines, which will contribute to the overall Clean Sky 2 targets.

Keywords: tial alloys,tnm alloys,calphad,thermodynamic database,levitation melting,in situ synchrotron high energy x ray diffraction hexrd
Structural and functional characterization of the ICOS/ICOSL immune complex (SI)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Spain,Canada
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2018-08-01
End date: 2021-12-31
Duration (months): 36.0
Total project cost (EUR): 224683.2
EU funding (EUR): 224683.2
Participants: 2
Project status: CLOSED

T and B lymphocytes are effectors of adaptive immunity. Their dysregulation have dramatic health consequences, and can lead to autoimmunity and blood cancers. Recently, immune receptors involved in dictating the fate of B and T cells have expanded drastically. Once of these critical receptor/ligand immune pair is the inducible co-stimulator (ICOS) and its ligand ICOSL, which emerged as a novel T/B-cell co-signaling pathway. The multiple roles reported for this immune complex position ICOS/ICOSL at the center of attention for immunotherapy. Accordingly, several therapeutic antibodies targeting this immune complex are under clinical investigation. However, structure-function studies of ICOS/ICOSL interaction are critically lacking. This proposal aims to use integrative structural biology to provide molecular details into the function and targeting of the ICOS/ICOSL immune complex. These structural blueprints will contribute to the design of next-generation therapies against autoimmune diseases and cancer. This knowledge, will also serve as the basis to develop agonist antibodies that could act as adjuvants of B cell maturation for vaccine purposes. Therefore, this study will aim to apply fundamental mechanistic approaches to answer biomedical research questions and the outcome will result in advances to both molecular medicine and biology of disease.

Keywords: antibodies,b cells,biophysics,glycoproteins,protein engineering,structural biology,vaccine design
European Training Network on Grey-Box Models for Safe and Reliable Intelligent Mobility Systems (GREYDIENT)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Training Networks
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Germany,Germany,Italy,Switzerland,Greece,Belgium,France
Call: Innovative Training Networks (H2020-MSCA-ITN-2020, 2020)
Start date: 2021-01-01
End date: 2024-12-31
Duration (months): 48.0
Total project cost (EUR): 3938269.68
EU funding (EUR): 3938269.68
Participants: 10
Project status: SIGNED

The GREYDIENT innovative training network aims at training a next generation of Early Stage Researchers (ESR) to fully sustain the ongoing transition of European personal mobility towards safe and reliable intelligent mobility systems via the recently introduced framework of grey-box modelling approaches. One of the main challenges that we currently face in this context is the integration of the data captured from the plenitude of sensors that are involved in a particular road-traffic scenario, ranging from monitoring car-component loading situations to power network-reliability estimations. The aim is to fully exploit the potential of merging these data with advanced computational models of components and systems that are widely available in industry in order to fully assess the momentarily safety. Grey box models are an answer to this pressing issue, as they are aimed at optimally integrating (black-box) data driven machine learning tools with (white-box) simulation models to greatly surpass the performance of either framework separately. However, the training of professional profiles in Europe who combine knowledge and experience in state-of-the-art data-driven black box and numerical white box approaches with expertise in methods for reliability and safety estimation is scarce. Therefore, GREYDIENT will train its ESR’s in a wide spectrum of fields, including the modelling, propagation and quantification of the relevant variabilities, the application of big data and machine learning methods, as well as the optimal combination of data-driven approaches with numerical models. All our ESR’s will obtain a PhD from an internationally respected University, build experience in communicating and disseminating their work, applying their research skills in a non-academic context and receive in-depth training in transferable skills such commercialization, collaboration and entrepreneurship. This training will be organized in close cooperation with key industry stakeholders.

Keywords: reliability estimation,uncertainty quantification,grey box models
DNA-Based Modular Nanorobotics (DNA-Robotics)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Training Networks
Action type: Marie Skłodowska-Curie actions
Panel: Physics
Countries involved: Germany,Italy,Germany,Sweden,Sweden,France,United Kingdom,Denmark,United Kingdom,United Kingdom,Denmark
Call: Innovative Training Networks (H2020-MSCA-ITN-2017, 2017)
Start date: 2018-01-01
End date: 2022-06-30
Duration (months): 54.0
Total project cost (EUR): 3979632.96
EU funding (EUR): 3979632.96
Participants: 12
Project status: CLOSED

Robotic systems essentially comprise sensors and actuators connected to and coordinated by an information-processing unit. These principles apply to the human scale robotic systems that currently transform the way we work and live. With DNA-Based Modular Robotics (DNA-Robotics) we will take the first steps to scale advanced robotic behavior to the molecular scale for future medical and technological applications. DNA-Robotics brings together the leading research groups in DNA nanotechnology in Europe to collectively develop an integrated and modular robotic platform and demonstrate applications of the novel technology. DNA-based construction is a natural modular platform, which via the programmability of sequence design and self-assembly allows different structural and functional motifs to be flexibly combined. DNA modules will be developed with a range of novel functions for sensing, actuation and intra-system communication and computing, implementing all of the typical components required for a robotic system at the molecular level. Our overall goal is the creation of the first “industry standard” for modular robotics by the establishment of a modular “plug-and-play” platform that allows the construction of custom-made nanorobots for advanced applications in research and industry. The ESRs will be trained in and develop functional modules that in close collaboration among the ESRs and Partners will be assembled to advanced molecular robots. The highly interdisciplinary research and training program will exploit the synergy between the different competences of the Partners. The innovative and applied focus is supported by the involvement of the company ATDBio as Beneficiary and five companies as Partner Organization that will also provide complementary skills training and intersectoral exposure of the ESRs. This proposal takes the first steps for future application of nanoscale robots in research and industry and will train future generations of researchers in this field.

Keywords: dna nanotechnology,self assembly,nucleic acids,dna origami,biosensor,dna structure
aDverse wEather eNvironmental Sensing systEm (DENSE)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Theme: Public-private partnerships
Destination: Electronic Components and Systems
Action type: Research and Innovation actions
Partnership: ECSEL (Art-187)
Countries involved: Germany,Germany,Germany,Germany,Finland,Finland,Sweden,Belgium,Finland,France,France,France,France,Netherlands
Call: Smart Mobility (H2020-ECSEL-2015-1-RIA-two-stage, 2015)
Start date: 2016-06-01
End date: 2020-02-29
Duration (months): 45.0
Total project cost (EUR): 14312279.65
EU funding (EUR): 4221232.63
Participants: 20
Project status: CLOSED

Current driver assistance systems are not all-weather capable. They offer comfort and safety in sound environmental conditions. However, in adverse weather conditions where the accident risks are highest they malfunction or even fail. Now that we are progressing towards automated cars and work machines, the requirements of fully reliable environment perception are only accentuated. The project is focusing on automated driving and its key enabling technology, environment perception. Consequently, project’s main objective is to develop and validate an all-weather sensor suit for traffic services, driver assistance and automated driving. Extended driving environment perception capability with smart, reliable and cost-efficient sensing system is necessary to meet the targets of all future driver assistance system applications. These targets need to be met regardless of location, weather or time of the day. Only by means of reliable and robust sensing system upcoming automated driving will be possible. The new sensor suit is based on a smart integration of three different technologies: (i) Radio radar, 77 GHz-81 GHz, (MIMO Radar); (ii) Gated short wave infrared camera with pulsed laser illumination (SWIR camera)and (iii) Short-wave infrared LIDAR (SWIR Lidar). Such a full fusion approach has never been investigated before, so that the outcome will advance the state-of-the-art significantly and demonstrate the potential of all-weather environment perception. DENSE innovation lies in the provision of a brilliant restored enriched colour image from a degraded infrared image and consequently, this is followed by a variety of application fields for low cost solutions. An important aim is also to close the gap to US developments in the field and avoid their restrictions for selling components overseas for strategic reasons and strengthen the position of European industry in worldwide competition.

Keywords: all weather sensor system,automated driving,7 traffic services,deep neural networks on fpga or asic,swir,gated swir camera,swir lidar,mimo radar,swir laser,traction estimation
Genetics to understand cellular components of Alzheimer Disease pathogenesis (CELLPHASE_AD)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Neuroscience and Disorders of the Nervous System
Panel description: Nervous system development, homeostasis and ageing, nervous system function and dysfunction, systems neuroscience and modelling, biological basis of cognitive processes and of behaviour, neurological and mental disorders
Countries involved: Belgium
Call: ERC Advanced Grant (ERC-2018-ADG, 2018)
Start date: 2019-11-01
End date: 2024-10-31
Duration (months): 60.0
Total project cost (EUR): 2374997.5
EU funding (EUR): 2374997.5
Participants: 1
Project status: SIGNED

Alzheimer disease (AD) is a major health problem worldwide. New therapies require an accelerated translation of genetic information into mechanistic insights. Given limitations of rodent models, fully humanized models are needed to capture the complexity of the disease process. Human stem cells (iPS) provide great possibilities but are largely investigated in vitro with associated limitations. Many of the novel genetic risk factors for AD are expressed in microglia and astroglia, which remains an understudied population in this classically neuron-centric field. We propose here mouse-human chimeric mouse models to test the effects of AD-associated genetic risk factors on the phenotypes of transplanted microglia and astroglia derived from patients and from genomic engineered, isogenic stem cells. The cells will be followed during disease progression in brain of wild type and of mice developing Aβ- and Tau- pathology. Using single cell transcriptomics, a dynamic view of the cell states over time is generated. In a first arm of the project, we investigate how the genetic makeup of patient derived stem cells with high and low polygenic risk scores influences pathological cell states. In the second arm of the project, we generate inducible Crisper/CAS9 iPS isogenic cell lines to manipulate rapidly and specifically the expression of 4 selected AD associated genes linked to a putative cholesterol pathway but also affecting inflammation. These cell lines will be used also in the second phase of the project when validating hypotheses generated from the extensive bioinformatics analysis of the 600.000 single human cell profiles generated. We expect to identify and validate >5 novel drug targets in the astroglia-microglia axis of AD pathogenesis. Our work provides humanized models for AD, an answer on how genetic makeup affects microglia and astroglia in an AD relevant context, and establishes a highly versatile platform to explore human genetics in human cells in vivo.

Keywords: alzheimer s disease,neuroinflammation,microglia,astroglia,single cell biology,polygenic risk factor
AIRFRAME ITD (GAM AIR 2018)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: Clean Sky 2
Action type: Innovation actions
Partnership: CS2 (Art-187)
Countries involved: Germany,Spain,Italy,Italy,Italy,Germany,Germany,Germany,Germany,Spain,Spain,Spain,Spain,Spain,Spain,Spain,Spain,Italy,Italy,Israel,Germany,Spain,Spain,Italy,Germany,Spain,Belgium,Greece,Belgium,Belgium,Greece,Poland,Poland,Romania,Sweden,Poland,Greece,Poland,Poland,Poland,France,France,France,France,France,France,France,France,Czechia,Netherlands,Netherlands,Netherlands,Netherlands,United Kingdom,United Kingdom,United Kingdom,Denmark,United Kingdom,United Kingdom,United Kingdom,United Kingdom,United Kingdom,United Kingdom,United Kingdom,Portugal,Portugal,Portugal,United Kingdom,Portugal,Portugal
Call: Airframes (H2020-IBA-CS2-GAMS-2017, 2017)
Start date: 2014-01-02
End date: 2019-12-31
Duration (months): 60.0
Total project cost (EUR): 210183580.85
EU funding (EUR): 160974883.59
Participants: 98
Project status: CLOSED

The Airframe ITD aims at re-thinking and developing the technologies as building blocks and the “solution space” on the level of the entire or holistic aircraft: pushing aerodynamics across new frontiers, combining and integrating new materials and structural techniques – and integrating innovative new controls and propulsion architectures with the airframe; and optimizing this against the challenges of weight, cost, life-cycle impact and durability.

Keywords: aircraft,rotor craft,high permanence and energy efficiency,high versatility and cost efficiency,eco design