ERC FUNDED PROJECTS

"Two-dimensional (2D) nanosheets, which possess a high degree of anisotropy with nanoscale thickness and infinite length in other dimensions, hold enormous promise as a novel class of ultrathin 2D nanomaterials with various unique functionalities and properties, and exhibit great potential in energy storage and conversion systems that are substantially different from their respective 3D bulk forms. Here I propose a strategy for the synthesis and processing of various 2D nanosheets across a broad range of inorganic, organic and polymeric materials with molecular-level or thin thickness through both the top-down exfoliation of layered materials and the bottom-up assembly of available molecular building blocks. Further, I aim to develop the synthesis of various 2D-nanosheet based composite materials with thickness of less than 100 nm and the assembly of 2D nanosheets into novel hierarchal superstrucutures (like aerogels, spheres, porous particles, nanotubes, multi-layer films). The structural features of these 2D nanomaterials will be controllably tailored by both the used layered precursors and processing methodologies. The consequence is that I will apply and combine defined functional components as well as assembly protocols to create novel 2D nanomaterials for specific purposes in energy storage and conversion systems. Their unique characters will include the good electrical conductivity, excellent mechanical flexibility, high surface area, high chemical stability, fast electron transport and ion diffusion etc. Applications will be mainly demonstrated for the construction of lithium ion batteries (anode and cathode), supercapacitors (symmetric and asymmetric) and fuel cells. As the key achievements, I expect to establish the delineation of reliable structure-property relationships and improved device performance of 2D nanomaterials."

1 500 000 €

Start date: 2012-09-01, End date: 2017-08-31

"Quantum information was born from the merging of classical information and quantum physics. Its main objective consists of understanding the quantum nature of information and learning how to process it by using physical systems which operate by following quantum mechanics laws. Quantum simulation is a fundamental instrument to investigate phenomena of quantum systems dynamics, such as quantum transport, particle localizations and energy transfer, quantum-to-classical transition, and even quantum improved computation, all tasks that are hard to simulate with classical approaches. Within this framework integrated photonic circuits have a strong potential to realize quantum information processing by optical systems. The aim of 3D-QUEST is to develop and implement quantum simulation by exploiting 3-dimensional integrated photonic circuits. 3D-QUEST is structured to demonstrate the potential of linear optics to implement a computational power beyond the one of a classical computer. Such ""hard-to-simulate"" scenario is disclosed when multiphoton-multimode platforms are realized. The 3D-QUEST research program will focus on three tasks of growing difficulty. A-1. To simulate bosonic-fermionic dynamics with integrated optical systems acting on 2 photon entangled states. A-2. To pave the way towards hard-to-simulate, scalable quantum linear optical circuits by investigating m-port interferometers acting on n-photon states with n>2. A-3. To exploit 3-dimensional integrated structures for the observation of new quantum optical phenomena and for the quantum simulation of more complex scenarios. 3D-QUEST will exploit the potential of the femtosecond laser writing integrated waveguides. This technique will be adopted to realize 3-dimensional capabilities and high flexibility, bringing in this way the optical quantum simulation in to new regime."

1 474 800 €

Start date: 2012-08-01, End date: 2017-07-31

Nature has evolved ultimate chemical functions based on controlling and altering conformation of its molecular machinery. Prominent examples include enzyme catalysis and information storage/duplication in nucleic acids. These achievements are based on large and complex yet remarkably defined structures obtained through folding of polymeric chains and a subtle interplay of non-covalent forces. Nature uses a limited set of building blocks – e.g. twenty amino-acids and four nucleobases – with specific abilities to impart well-defined folds. In the last decade, chemists have discovered foldamers: non-natural oligomers and polymers also prone to adopt folded structures. The emergence of foldamers has far reaching implications. A new major long term prospect is open to chemistry: the de novo synthesis of artificial objects resembling biopolymers in terms of their size, complexity, and efficiency at achieving defined functions, yet having chemical structures beyond the reach of biopolymers amenable to new properties and functions. The PI of this project has shown internationally recognized leadership in the development of a class of foldamers, aromatic oligoamides, whose features arguably make them the most suitable candidates to systematically explore what folded structures beyond biopolymers give access to. This project aims at developing methods to allow the routine fabrication of 20-40 units long aromatic oligoamide foldamers (6-15 kDa) designed to fold into artificial molecular containers having engineerable cavities and surfaces for molecular recognition of organic substrates, in particular large peptides and saccharides, polymers, and proteins. The methodology rests on modelling based design, multistep organic synthesis of heterocyclic monomers and their assembly into long sequences, structural elucidation using, among other techniques, x-ray crystallography, and the physico-chemical characterization of molecular recognition events.

2 496 216 €

Start date: 2013-06-01, End date: 2018-05-31

"Combustion is essential to the world’s energy generation and transport needs, and will remain so for the foreseeable future. Mitigating its impact on the climate and human health, by reducing its associated emissions, is thus a priority. One significant challenge for gas-turbine combustion is combustion instability, which is currently inhibiting reductions in NOx emissions (these damage human health via a deterioration in air quality). Combustion instability is caused by a two-way coupling between unsteady combustion and acoustic waves - the large pressure oscillations that result can cause substantial mechanical damage. Currently, the lack of fast, accurate modelling tools for combustion instability, and the lack of reliable ways of suppressing it are severely hindering reductions in NOx emissions. This proposal aims to make step improvements in both fast, accurate modelling of combustion instability, and in developing reliable active control strategies for its suppression. It will achieve this by coupling low order combustor models (these are fast, simplified models for simulating combustion instability) with advances in analytical modelling, CFD simulation, reduced order modelling and control theory tools. In particular: * important advances in accurately incorporating the effect of entropy waves (temperature variations resulting from unsteady combustion) and non-linear flame models will be made; * new active control strategies for achieving reliable suppression of combustion instability, including from within limit cycle oscillations, will be developed; * an open-source low order combustor modelling tool will be developed and widely disseminated, opening access to researchers worldwide and improving communications between the fields of thermoacoustics and control theory. Thus the proposal aims to use analytical and computational methods to contribute to achieving low NOx gas-turbine combustion, without the penalty of damaging combustion instability."

1 489 309 €

Start date: 2013-01-01, End date: 2017-12-31

"Computer vision is concerned with the automated interpretation of images and video streams. Today's research is (mostly) aimed at answering queries such as ""Is this a picture of a dog?"", (classification) or sometimes ""Find the dog in this photo"" (detection). While categorisation and detection are useful for many tasks, inferring correct class labels is not the final answer to visual recognition. The categories and locations of objects do not provide direct understanding of their function i.e., how things work, what they can be used for, or how they can act and react. Such an understanding, however, would be highly desirable to answer currently unsolvable queries such as ""Am I in danger?"" or ""What can happen in this scene?"". Solving such queries is the aim of this proposal. My goal is to uncover the functional properties of objects and the purpose of actions by addressing visual recognition from a different and yet unexplored perspective. The main novelty of this proposal is to leverage observations of people, i.e., their actions and interactions to automatically learn the use, the purpose and the function of objects and scenes from visual data. The project is timely as it builds upon the two key recent technological advances: (a) the immense progress in visual recognition of objects, scenes and human actions achieved in the last ten years, as well as (b) the emergence of a massive amount of public image and video data now available to train visual models. ACTIVIA addresses fundamental research issues in automated interpretation of dynamic visual scenes, but its results are expected to serve as a basis for ground-breaking technological advances in practical applications. The recognition of functional properties and intentions as explored in this project will directly support high-impact applications such as detection of abnormal events, which are likely to revolutionise today's approaches to crime protection, hazard prevention, elderly care, and many others."

1 497 420 €

Start date: 2013-01-01, End date: 2018-12-31

The numerical simulation of complex compressible flow problem is still a challenge nowaday even for simple models. In our opinion, the most important hard points that need currently to be tackled and solved is how to obtain stable, scalable, very accurate, easy to code and to maintain schemes on complex geometries. The method should easily handle mesh refinement, even near the boundary where the most interesting engineering quantities have to be evaluated. Unsteady uncertainties in the model, for example in the geometry or the boundary conditions should represented efficiently.This proposal goal is to design, develop and evaluate solutions to each of the above problems. Our work program will lead to significant breakthroughs for flow simulations. More specifically, we propose to work on 3 connected problems: 1-A class of very high order numerical schemes able to easily deal with the geometry of boundaries and still can solve steep problems. The geometry is generally defined by CAD tools. The output is used to generate a mesh which is then used by the scheme. Hence, any mesh refinement process is disconnected from the CAD, a situation that prevents the spread of mesh adaptation techniques in industry! 2-A class of very high order numerical schemes which can utilize possibly solution dependant basis functions in order to lower the number of degrees of freedom, for example to compute accurately boundary layers with low resolutions. 3-A general non intrusive technique for handling uncertainties in order to deal with irregular probability density functions (pdf) and also to handle pdf that may evolve in time, for example thanks to an optimisation loop. The curse of dimensionality will be dealt thanks Harten's multiresolution method combined with sparse grid methods. Currently, and up to our knowledge, no scheme has each of these properties. This research program will have an impact on numerical schemes and industrial applications.

1 432 769 €

Start date: 2008-12-01, End date: 2013-11-30

I propose to establish a research group to develop completely new tools in order to solve three important problems on the relationships between automorphic forms and Galois representations, which lie at the heart of the Langlands program. The first is to prove Serre’s conjecture for real quadratic fields. I will use automorphic induction to transfer the problem to U(4) over the rational numbers, where I will use automorphy lifting theorems and results on the weight part of Serre's conjecture that I established in my earlier work to reduce the problem to proving results in small weight and level. I will prove these base cases via integral p-adic Hodge theory and discriminant bounds. The second is to develop a geometric theory of moduli spaces of mod p and p-adic Galois representations, and to use it to establish the Breuil–Mézard conjecture in arbitrary dimension, by reinterpreting the conjecture in geometric terms. This will transform the subject by building the first connections between the p-adic Langlands program and the geometric Langlands program, providing an entirely new world of techniques for number theorists. As a consequence of the Breuil-Mézard conjecture, I will be able to deduce far stronger automorphy lifting theorems (in arbitrary dimension) than those currently available. The third is to completely determine the reduction mod p of certain two-dimensional crystalline representations, and as an application prove a strengthened version of the Gouvêa–Mazur conjecture. I will do this by means of explicit computations with the p-adic local Langlands correspondence for GL_2(Q_p), as well as by improving existing arguments which prove multiplicity one theorems via automorphy lifting theorems. This work will show that the existence of counterexamples to the Gouvêa-Mazur conjecture is due to a purely local phenomenon, and that when this local obstruction vanishes, far stronger conjectures of Buzzard on the slopes of the U_p operator hold.

1 131 339 €

Start date: 2012-10-01, End date: 2017-09-30

A massive and ever growing amount of digital image and video content is available today, on sites such as Flickr and YouTube, in audiovisual archives such as those of BBC and INA, and in personal collections. In most cases, it comes with additional information, such as text, audio or other metadata, that forms a rather sparse and noisy, yet rich and diverse source of annotation, ideally suited to emerging weakly supervised and active machine learning technology. The ALLEGRO project will take visual recognition to the next level by using this largely untapped source of data to automatically learn visual models. The main research objective of our project is the development of new algorithms and computer software capable of autonomously exploring evolving data collections, selecting the relevant information, and determining the visual models most appropriate for different object, scene, and activity categories. An emphasis will be put on learning visual models from video, a particularly rich source of information, and on the representation of human activities, one of today's most challenging problems in computer vision. Although this project addresses fundamental research issues, it is expected to result in significant advances in high-impact applications that range from visual mining of the Web and automated annotation and organization of family photo and video albums to large-scale information retrieval in television archives.

2 493 322 €

Start date: 2013-04-01, End date: 2019-03-31

Most of the technological materials have been developed by very expensive and cumbersome trial and error methods. On the other hand, computer based theoretical design of advanced materials is an area where rapid and extensive developments are taking place. Within my group new theoretical tools have now been established which are extremely well suited to the study of complex materials. In this approach basic quantum mechanical theories are used to describe fundamental properties of alloys and compounds. The utilization of such calculations to investigate possible optimizations of certain key properties represents a major departure from the traditional design philosophy. The purpose of my project is to build up a new competence in the field of computer-aided simulations of advanced materials. The main goal will be to achieve a deep understanding of the behaviour of complex metallic systems under equilibrium and non-equilibrium conditions at the atomic level by studying their electronic, magnetic and atomic structure using the most modern and advanced computational methods. This will enable us to establish a set of materials parameters and composition-structure-property relations that are needed for materials optimization. The research will be focused on fundamental technological properties related to defects in advanced metallic alloys (high-performance steels, superalloys, and refractory, energy related and geochemical materials) and alloy phases (solid solutions, intermetallic compounds), which will be studied by means of parameter free atomistic simulations combined with continuum modelling. As a first example, we will study the Fe-Cr system, which is of great interest to industry as well as in connection to nuclear waste. The Fe-Cr-Ni system will form another large group of materials under the aegis of this project. Special emphasis will also be placed on those Fe-alloys which exist under extreme conditions and are possible candidates for the Earth core.

2 000 000 €

Start date: 2009-03-01, End date: 2014-02-28