ERC FUNDED PROJECTS

The goal of 2D-TOPSENSE is to exploit the remarkable stretchability of two-dimensional semiconductors to fabricate optoelectronic devices where strain is used as an external knob to tune their properties. While bulk semiconductors tend to break under strains larger than 1.5%, 2D semiconductors (such as MoS2) can withstand deformations of up to 10-20% before rupture. This large breaking strength promises a great potential of 2D semiconductors as ‘straintronic’ materials, whose properties can be adjusted by applying a deformation to their lattice. In fact, recent theoretical works predicted an interesting physical phenomenon: a tensile strain-induced semiconductor-to-metal transition in 2D semiconductors. By tensioning single-layer MoS2 from 0% up to 10%, its electronic band structure is expected to undergo a continuous transition from a wide direct band-gap of 1.8 eV to a metallic behavior. This unprecedented large strain-tunability will undoubtedly have a strong impact in a wide range of optoelectronic applications such as photodetectors whose cut-off wavelength is tuned by varying the applied strain or atomically thin light modulators. To date, experimental works on strain engineering have been mostly focused on fundamental studies, demonstrating part of the potential of 2D semiconductors in straintronics, but they have failed to exploit strain engineering to add extra functionalities to optoelectronic devices. In 2D-TOPSENSE I will go beyond the state of the art in straintronics by designing and fabricating optoelectronic devices whose properties and performance can be tuned by means of applying strain. 2D-TOPSENSE will focus on photodetectors with a tunable bandwidth and detectivity, light emitting devices whose emission wavelength can be adjusted, light modulators based on 2D semiconductors such as transition metal dichalcogenides or black phosphorus and solar funnels capable of directing the photogenerated charge carriers towards a specific position.

1 930 437 €

Start date: 2018-03-01, End date: 2023-02-28

Design of new thermoelectric devices based on layered and field modulated nanostructures of strongly correlated electron systems

1 427 190 €

Start date: 2010-11-01, End date: 2015-10-31

Imagine a technology for powering your smart devices by recovering energy from lights in your office, the random movements of your body while reading these lines or from small changes in temperature when you breathe or go out for a walk. This very technology will provide energy for wireless sensor networks monitoring the air in your city or the structural stability of buildings and large constructions remotely and sustainably, avoiding battery recharging or even replacing them. These are the challenges in micro energy harvesting from (local) ambient sources. Kinetic, thermal and solar energies are ubiquitous at our surroundings under diverse forms, but their relatively low intensity and intermittent availability limit their potential recovery by microscale devices. These restrictions call for multi-source energy harvesters working under two principles: 1) combining different single-source harvesters in one device, or 2) using multifunctional materials capable of simultaneously converting various energy sources into electricity. In 1), efficiency per unit volume can decrease compared to the individual counterparts; in 2), materials as semiconductors, polymeric and oxide ferroelectrics and hybrid perovskites may act as multisource harvesters but huge advances are required to optimize their functionalities and sustainable fabrication at large scale. I propose to fill the gap between these approaches offering an all-in-one solution to multisource energy scavenging, based on the nanoscale design of multifunctional three-dimensional materials. The demonstration of an industrially scalable one-reactor plasma/vacuum method will be crucial to integrate hybrid-scavenging components and to provide 3DScavengers materials with tailored microstructure-enhanced performance. My ultimate goal is to build nanoarchitectures for simultaneous and enhanced individual scavenging applying photovoltaic, piezo- and pyro-electric effects, minimizing the environmental cost of their synthesis

1 498 414 €

Start date: 2020-03-01, End date: 2025-02-28

It was early predicted by M. Green and coeval colleagues that dividing the solar spectrum into narrow ranges of colours is the most efficient manner to convert solar energy into electrical power. Multijunction solar cells are the current solution to this challenge, which have reached over 30% conversion efficiencies by stacking 3 junctions together. However, the large fabrication costs and time hinders their use in everyday life. It has been shown that highly mismatched alloy (HMA) materials provide a powerful playground to achieve at least 3 different colour absorption regions that enable optimised energy conversion with just one junction. Combining HMA-based junctions with standard Silicon solar cells will rocket solar conversion efficiency at a reduced price. To turn this ambition into marketable devices, several efforts are still needed and few challenges must be overcome. 4SUNS is a revolutionary approach for the development of HMA materials on Silicon technology, which will bring highly efficient multi-colour solar cells costs below current multijunction devices. The project will develop the technology of HMA materials on Silicon via material synthesis opening a new technology for the future. The understanding and optimization of highly mismatched alloy materials-using GaAsNP alloy- will provide building blocks for the fabrication of laboratory-size 4-colours/2-junctions solar cells. Using a molecular beam epitaxy system, 4SUNS will grow 4-colours/2-junctions structure as well as it will manufacture the final devices. Structural and optoelectronic characterizations will carry out to determine the quality of the materials and the solar cells characteristic to obtain a competitive product. These new solar cells are competitive products to breakthrough on the solar energy sector solar cells and allowing Europe to take leadership on high efficiency solar cells.

1 499 719 €

Start date: 2018-02-01, End date: 2023-01-31

"When I asked my seven-year-old daughter ""Who is the boy in your class who was also new in school last year, like you?"", she instantly replied ""Daniel"", using the descriptive content in my utterance to identify an entity in the real world and refer to it. The ability to use language to refer to reality is crucial for humans, and yet it is very difficult to model. AMORE breaks new ground in Computational Linguistics, Linguistics, and Artificial Intelligence by developing a model of linguistic reference to entities implemented as a computational system that can learn its own representations from data. This interdisciplinary project builds on two complementary semantic traditions: 1) Formal semantics, a symbolic approach that can delimit and track linguistic referents, but does not adequately match them with the descriptive content of linguistic expressions; 2) Distributional semantics, which can handle descriptive content but does not associate it to individuated referents. AMORE synthesizes the two approaches into a unified, scalable model of reference that operates with individuated referents and links them to referential expressions characterized by rich descriptive content. The model is a distributed (neural network) version of a formal semantic framework that is furthermore able to integrate perceptual (visual) and linguistic information about entities. We test it extensively in referential tasks that require matching noun phrases (“the Medicine student”, “the white cat”) with entity representations extracted from text and images. AMORE advances our scientific understanding of language and its computational modeling, and contributes to the far-reaching debate between symbolic and distributed approaches to cognition with an integrative proposal. I am in a privileged position to carry out this integration, since I have contributed top research in both distributional and formal semantics. "

1 499 805 €

Start date: 2017-02-01, End date: 2022-01-31

Computer animation has traditionally been associated with applications in virtual-reality-based training, video games or feature films. However, interactive animation is gaining relevance in a more general scope, as a tool for early-stage analysis, design and planning in many applications in science and engineering. The user can get quick and visual feedback of the results, and then proceed by refining the experiments or designs. Potential applications include nanodesign, e-commerce or tactile telecommunication, but they also reach as far as, e.g., the analysis of ecological, climate, biological or physiological processes. The application of computer animation is extremely limited in comparison to its potential outreach due to a trade-off between accuracy and computational efficiency. Such trade-off is induced by inherent complexity sources such as nonlinear or anisotropic behaviors, heterogeneous properties, or high dynamic ranges of effects. The Animetrics project proposes a modeling and animation methodology, which consists of a multi-scale decomposition of complex processes, the description of the process at each scale through combination of simple local models, and fitting the parameters of those local models using large amounts of data from example effects. The modeling and animation methodology will be explored on specific problems arising in complex mechanical phenomena, including viscoelasticity of solids and thin shells, multi-body contact, granular and liquid flow, and fracture of solids.

1 277 969 €

Start date: 2012-01-01, End date: 2016-12-31

In our quest to fully understand the processes that shape the genomic variation of species, describing variation of the past is a fundamental objective. However, the origins and the extent of great ape variation, the genomic description of extinct primate species and the genomic footprints of introgression events all remain unknown. Even today, and in contraposition to human evolutionary biology, the almost null presence of ancient great ape samples has precluded a comprehensive exploration of such diversity. Here, I present two approaches that will expose great ape diversity throughout time and will allow me to compare the genomic impact of introgression events across lineages. First, I would like to take advantage of ancient ape samples that will provide us with a direct view of the genomes of extinct populations. Second, I would like to exploit current and recent diversity to indirectly access the parts of extinct ape genomes that became hybridized with current species in the past. For the latter, we will analyse hundreds of non-invasive samples taken from present-day great apes as well as historical specimens. Altogether, this information will enable me to decipher novel genomes that until now have been lost in time. In this way, I will be able to properly understand the origins and dynamics of genomic variants and to study how admixture has contributed to today´s adaptive landscape. By completing this proposal and performing analogies to the human lineage, fundamental insights will be revealed about (i) the spatial-temporal history of our closest species and (ii) the functional consequences of introgressed events. On top of that, these results will help to annotate functional consequences of novel mutations in the human genome. In so doing, a fundamental insight will be provided into the evolutionary history of these regions and into human mutations with multiple repercussions in the understanding of evolution and human biology.

1 896 875 €

Start date: 2020-06-01, End date: 2025-05-31

Based on the APMPAL (ERC) project we continue to develop the frameworks of internal rationality (IR) and optimal signal extraction (OSE). Under IR investors/consumers behave rationally given their subjective beliefs about prices, these beliefs are compatible with data. Under OSE the government has partial information, it knows how policy influences observed variables and signal extraction. We develop further the foundations of IR and OSE with an emphasis on heterogeneous agents. We study sovereign bond crisis and heterogeneity of beliefs in asset pricing models under IR, using survey data on expectations. Under IR the assets’ stochastic discount factor depends on the agents’ decision function and beliefs; this modifies some key asset pricing results. We extend OSE to models with state variables, forward-looking constraints and heterogeneity. Under IR agents’ prior beliefs determine the effects of a policy reform. If the government does not observe prior beliefs it has partial information, thus OSE should be used to analyse policy reforms under IR. If IR heterogeneous workers forecast their productivity either from their own wage or their neighbours’ in a network, low current wages discourage search and human capital accumulation, leading to low productivity. This can explain low development of a country or social exclusion of a group. Worker subsidies redistribute wealth and can increase productivity if they “teach” agents to exit a low-wage state. We build DSGE models under IR for prediction and policy analysis. We develop time-series tools for predicting macro and asset market variables, using information available to the analyst, and we introduce non-linearities and survey expectations using insights from models under IR. We study how IR and OSE change the view on macro policy issues such as tax smoothing, debt management, Taylor rule, level of inflation, fiscal/monetary policy coordination, factor taxation or redistribution.

1 524 144 €

Start date: 2018-09-01, End date: 2023-08-31

Air transport has by and large been studied as a transportation process, in which different elements, e.g. aircraft or passengers, move within the system. While intuitive, this approach entails several drawbacks, including the need for large-scale simulations, the reliance on real data, and the difficulty of extracting macro-scale conclusions from large quantities of micro- scale results. The lack of a better approach is in part responsible for our inability to fully understand delay propagation, one of the most important phenomena in air transport. ARCTIC proposes an ambitious program to change the conceptual framework used to analyse air transport, inspired by the way the brain is studied in neuroscience. It is based on understanding air transport as an information processing system, in which the movement of aircraft is merely a vehicle for information transfer. Airports then become computational units, receiving information from their neighbours through inbound flights under the form of delays; processing it in a potentially non-linear way; and redistributing the result to the system as outbound delays. In this proposal I show how, as already common in neuroscience, such computation can be made explicit by using a combination of information sciences and statistical physics techniques: from the detection of information movements through causality metrics, up to the representation of the resulting transfer structures through complex networks and their topological properties. The approach also entails important challenges, e.g. the definition of appropriate metrics or the translation of the obtained insights into implementable policies. In the main text of the proposal I present a number of preliminary results that point towards a radically new way of thinking about the dynamics of air transport. ARCTIC’s methodology will be used over the next five years to characterize and model delay propagation, as well as to limit its societal and economic impact.

1 297 024 €

Start date: 2020-03-01, End date: 2025-02-28