ERC FUNDED PROJECTS

The suite of graphene’s unique properties and applications can be enormously enhanced by its functionalization. As non-covalently functionalized graphenes do not target all graphene’s properties and may suffer from limited stability, covalent functionalization represents a promising way for controlling graphene’s properties. To date, only a few well-defined graphene derivatives have been introduced. Among them, fluorographene (FG) stands out as a prominent member because of its easy synthesis and high stability. Being a perfluorinated hydrocarbon, FG was believed to be as unreactive as the two-dimensional counterpart perfluoropolyethylene (Teflon®). However, our recent experiments showed that FG is not chemically inert and can be used as a viable precursor for synthesizing graphene derivatives. This surprising behavior indicates that common textbook grade knowledge cannot blindly be applied to the chemistry of 2D materials. Further, there might be specific rules behind the chemistry of 2D materials, forming a new chemical discipline we tentatively call 2D chemistry. The main aim of the project is to explore, identify and apply the rules of 2D chemistry starting from FG. Using the knowledge gained of 2D chemistry, we will attempt to control the chemistry of various 2D materials aimed at preparing stable graphene derivatives with designed properties, e.g., 1-3 eV band gap, fluorescent properties, sustainable magnetic ordering and dispersability in polar media. The new graphene derivatives will be applied in sensing, imaging, magnetic delivery and catalysis and new emerging applications arising from the synergistic phenomena are expected. We envisage that new applications will be opened up that benefit from the 2D scaffold and tailored properties of the synthesized derivatives. The derivatives will be used for the synthesis of 3D hybrid materials by covalent linking of the 2D sheets joined with other organic and inorganic molecules, nanomaterials or biomacromolecules.

1 831 103 €

Start date: 2016-06-01, End date: 2021-05-31

This proposal will determine the technical and economic viability of scaling up ultra-thin film deposition processes for exfoliated single atomic layers. The PI has developed methods to produce exfoliated nanosheets from a range of layered materials such as graphene, transition metal chalcogenides and transition metal oxides. These 2D materials have immediate and far-reaching potential in several high-impact technological applications such as microelectronics, composites and energy harvesting and storage. 2DNanoCaps (ERC ref: 278516) has already demonstrated that lab-scale ultra-thin graphene-based supercapacitor electrodes for energy storage result in unusually high power performance and extremely long device life-time (100% capacitance retention for 5000 charge-discharge cycles at the high power scan rate of 10,000 mV/s). This performance is remarkable- an order of magnitude better than similar systems produced with more conventional methods, which cause materials restacking and aggregation. 2D nanosheets also offer the chance of exploring the unique possibility of manufacturing conductive, robust, thin, easily assembled electrode and solid electrolytes to realize highly flexible and all-solid-state supercapacitors. This opportunity is particularly relevant from the industrial point of view especially in relation to the flammability issues of the electrolytes used for commercial energy storage devices at present. In order to develop and exploit any of the applications listed above, it will be imperative to develop deposition methods and techniques capable of obtaining industrial-scale “sheet-like” coverage, where flake re-aggregation is avoided. We believe our combination of unique material properties and cost effective, robust and production-scalable process of ultra-thin deposition will enable us to compete for significant global market opportunities in the energy-storage space

148 021 €

Start date: 2014-01-01, End date: 2014-12-31

The applicant will collaborate with Irish, European and U.S.-based colleagues to develop a sustainable biorefinery and bioenergy industry in Ireland and Europe. The focus of this ERC Starting Grant will be the application of classical microbiological, physiological and real-time polymerase chain reaction (PCR)-based assays, to qualitatively and quantitatively characterize microbial communities underpinning novel and innovative, low-temperature, anaerobic waste (and other biomass) conversion technologies, including municipal wastewater treatment and, demonstration- and full-scale biorefinery applications. Anaerobic digestion (AD) is a naturally-occurring process, which is widely applied for the conversion of waste to methane-containing biogas. Low-temperature (<20 degrees C) AD has been applied by the applicant as a cost-effective alternative to mesophilic (c. 35C) AD for the treatment of several waste categories. However, the microbiology of low-temperature AD is poorly understood. The applicant will work with microbial consortia isolated from anaerobic bioreactors, which have been operated for long-term experiments (>3.5 years), and include organic acid-oxidizing, hydrogen-producing syntrophic microbes and hydrogen-consuming methanogens. A major focus of the project will be the ecophysiology of psychrotolerant and psychrophilic methanogens already identified and cultivated by the applicant. The project will also investigate the role(s) of poorly-understood Crenarchaeota populations and homoacetogenic bacteria, in complex consortia. The host organization is a leading player in the microbiology of waste-to-energy applications. The applicant will train a team of scientists in all aspects of the microbiology and bioengineering of biomass conversion systems.

1 499 797 €

Start date: 2011-05-01, End date: 2016-04-30

Making accurate predictions is a crucial factor in many systems (such as in modelling energy consumption, power load forecasting, traffic networks, process industry, environmental modelling, biomedicine, brain-machine interfaces) for cost savings, efficiency, health, safety and organizational purposes. In this proposal we aim at realizing a new generation of more advanced black-box modelling techniques for estimating predictive models from measured data. We will study different optimization modelling frameworks in order to obtain improved black-box modelling approaches. This will be done by specifying models through constrained optimization problems by studying different candidate core models (parametric models, support vector machines and kernel methods) together with additional sets of constraints and regularization mechanisms. Different candidate mathematical frameworks will be considered with models that possess primal and (Lagrange) dual model representations, functional analysis in reproducing kernel Hilbert spaces, operator splitting and optimization in Banach spaces. Several aspects that are relevant to black-box models will be studied including incorporation of prior knowledge, structured dynamical systems, tensorial data representations, interpretability and sparsity, and general purpose optimization algorithms. The methods should be suitable for handling larger data sets and high dimensional input spaces. The final goal is also to realize a next generation software tool (including symbolic generation of models and handling different supervised and unsupervised learning tasks, static and dynamic systems) that can be generically applied to data from different application areas. The proposal A-DATADRIVE-B aims at getting end-users connected to the more advanced methods through a user-friendly data-driven black-box modelling tool. The methods and tool will be tested in connection to several real-life applications.

2 485 800 €

Start date: 2012-04-01, End date: 2017-03-31