ERC FUNDED PROJECTS

Applied electrochemistry plays a key role in many technologies, such as batteries, fuel cells, supercapacitors or solar cells. It is therefore at the core of many research programs all over the world. Yet, fundamental electrochemical investigations remain scarce. In particular, electrochemistry is among the fields for which the gap between theory and experiment is the largest. From the computational point of view, there is no molecular dynamics (MD) software devoted to the simulation of electrochemical systems while other fields such as biochemistry (GROMACS) or material science (LAMMPS) have dedicated tools. This is due to the difficulty of accounting for complex effects arising from (i) the degree of metallicity of the electrode (i.e. from semimetals to perfect conductors), (ii) the mutual polarization occurring at the electrode/electrolyte interface and (iii) the redox reactivity through explicit electron transfers. Current understanding therefore relies on standard theories that derive from an inaccurate molecular-scale picture. My objective is to fill this gap by introducing a whole set of new methods for simulating electrochemical systems. They will be provided to the computational electrochemistry community as a cutting-edge MD software adapted to supercomputers. First applications will aim at the discovery of new electrolytes for energy storage. Here I will focus on (1) ‘‘water-in-salts’’ to understand why these revolutionary liquids enable much higher voltage than conventional solutions (2) redox reactions inside a nanoporous electrode to support the development of future capacitive energy storage devices. These selected applications are timely and rely on collaborations with leading experimental partners. The results are expected to shed an unprecedented light on the importance of polarization effects on the structure and the reactivity of electrode/electrolyte interfaces, establishing MD as a prominent tool for solving complex electrochemistry problems.

1 588 769 €

Start date: 2018-04-01, End date: 2023-03-31

Cognition improves an animal’s ability to tune its responses to environmental conditions. In group living animals, communication works to form a collective cognition that expands the group’s abilities beyond those of individuals. Despite much research, to date, there is little understanding of how collective cognition emerges within biological ensembles. A major obstacle towards such an understanding is the rarity of comprehensive multi-scale empirical data of these complex systems. We have demonstrated cooperative load transport by ants to be an ideal system to study the emergence of cognition. Similar to other complex cognitive systems, the ants employ high levels of emergence to achieve efficient problem solving over a large range of scenarios. Unique to this system, is its extreme amenability to experimental measurement and manipulation where internal conflicts map to forces, abstract decision making is reflected in direction changes, and future planning manifested in pheromone trails. This allows for an unprecedentedly detailed, multi-scale empirical description of the moment-to-moment unfolding of sophisticated cognitive processes. This proposal is aimed at materializing this potential to the full. We will examine the ants’ problem solving capabilities under a variety of environmental challenges. We will expose the underpinning rules on the different organizational scales, the flow of information between them, and their relative contributions to collective performance. This will allow for empirical comparisons between the ‘group’ and the ‘sum of its parts’ from which we will quantify the level of emergence in this system. Using the language of information, we will map the boundaries of this group’s collective cognition and relate them to the range of habitable environmental niches. Moreover, we will generalize these insights to formulate a new paradigm of emergence in biological groups opening new horizons in the study of cognitive processes in general.

2 000 000 €

Start date: 2018-06-01, End date: 2023-05-31

Digital signal processing (DSP) is a revolutionary paradigm shift enabling processing of physical data in the digital domain where design and implementation are considerably simplified. However, state-of-the-art analog-to-digital convertors (ADCs) preclude high-rate wideband sampling and processing with low cost and energy consumption, presenting a major bottleneck. This is mostly due to a traditional assumption that sampling must be performed at the Nyquist rate, that is, twice the signal bandwidth. Modern applications including communications, medical imaging, radar and more use signals with high bandwidth, resulting in prohibitively large Nyquist rates. Our ambitious goal is to introduce a paradigm shift in ADC design that will enable systems capable of low-rate, wideband sensing and low-rate DSP. While DSP has a rich history in exploiting structure to reduce dimensionality and perform efficient parameter extraction, current ADCs do not exploit such knowledge. We challenge current practice that separates the sampling stage from the processing stage and exploit structure in analog signals already in the ADC, to drastically reduce the sampling and processing rates. Our preliminary data shows that this allows substantial savings in sampling and processing rates --- we show rate reduction of 1/28 in ultrasound imaging, and 1/30 in radar detection. To achieve our overreaching goal we focus on three interconnected objectives -- developing the 1) theory 2) hardware and 3) applications of sub-Nyquist sampling. Our methodology ties together two areas on the frontier of signal processing: compressed sensing (CS), focused on finite length vectors, and analog sampling. Our research plan also inherently relies on advances in several other important areas within signal processing and combines multi-disciplinary research at the intersection of signal processing, information theory, optimization, estimation theory and hardware design.

2 400 000 €

Start date: 2015-08-01, End date: 2020-07-31

Fidelity during information transfer is essential for life, but it pays to be unfaithful if it provides an evolutionary advantage. The immune system continuously generates diversity to put up with recurrent pathogen challenges, and many viruses, in its turn, have evolved mechanisms to generate diversity to evade immune restrictions, even at the cost of enduring high mutation rates. Synonymous codons are not used at random and are not translated with similar efficiency. A large proportion of viruses infecting humans, especially those causing chronic infections, display a poor adaptation to the codon usage preferences of their host. This observation is a paradox, as viral genes completely depend upon the cellular translation machinery for protein synthesis. The poor match between codon usage preferences of virus and host negatively affects speed and accuracy of viral protein translation. We propose here that maladaptation of codon usage preferences in human viruses may have an adaptive value as it decreases translational fidelity, results in the synthesis of an ill-defined population of viral proteins and provides a way to escape immune surveillance. We will address the fitness effects of codon usage bias at the molecular and cellular levels, and later at the organism level in a rabbit model of papillomavirus infection. We will apply experimental evolution to analyse genotypic changes by means of next generation sequencing and will monitor phenotypic changes through real-time cell monitoring techniques, comparative proteomics, and anatomopathological analysis of virus-induced lesions. Our results will help solve the evolutionary puzzle of codon usage bias, and will have implications for the development of therapeutic vaccines to guide the immune response towards the identification and targeting of the main protein species, avoiding the chemical noise generated by protein mistranslation.

1 997 100 €

Start date: 2016-01-01, End date: 2020-12-31