ERC FUNDED PROJECTS

Currently fuels, plastics, and drugs are predominantly manufactured from oil. A transition towards renewable resources critically depends on new catalysts, for instance to convert small molecules (such as solar or biomass derived hydrogen, carbon monoxide, water and carbon dioxide) into more complex ones (such as oxygenates, containing oxygen atoms in their structure). Catalyst development now often depends on trial and error rather than rational design, as the heterogeneity of these composite systems hampers detailed understanding of the role of each of the components. I propose 3D model catalysts as a novel enabling tool to overcome this problem. Their well-defined nature allows unprecedented precision in the variation of structural parameters (morphology, spatial distribution) of the individual components, while at the same time they mimic real catalysts closely enough to allow testing under industrially relevant conditions. Using this approach I will address fundamental questions, such as: * What are the mechanisms (structural, electronic, chemical) by which non-metal promoters influence the functionality of copper-based catalysts? * Which nanoalloys can be formed, how does their composition influence the surface active sites and catalytic functionality under reaction conditions? * Which size and interface effects occur, and how can we use them to tune the actitivity and selectivity towards desired products? Our 3D model catalysts will be assembled from ordered mesoporous silica and carbon support materials and Cu-based promoted and bimetallic nanoparticles. The combination with high resolution characterization and testing under realistic conditions allows detailed insight into the role of the different components; critical for the rational design of novel catalysts for a future more sustainable production of chemicals and fuels from renewable resources.

1 999 625 €

Start date: 2015-09-01, End date: 2020-11-30

The goal of the AI4REASON project is a breakthrough in what is considered a very hard problem in AI and automation of reasoning, namely the problem of automatically proving theorems in large and complex theories. Such complex formal theories arise in projects aimed at verification of today's advanced mathematics such as the Formal Proof of the Kepler Conjecture (Flyspeck), verification of software and hardware designs such as the seL4 operating system kernel, and verification of other advanced systems and technologies on which today's information society critically depends. It seems extremely complex and unlikely to design an explicitly programmed solution to the problem. However, we have recently demonstrated that the performance of existing approaches can be multiplied by data-driven AI methods that learn reasoning guidance from large proof corpora. The breakthrough will be achieved by developing such novel AI methods. First, we will devise suitable Automated Reasoning and Machine Learning methods that learn reasoning knowledge and steer the reasoning processes at various levels of granularity. Second, we will combine them into autonomous self-improving AI systems that interleave deduction and learning in positive feedback loops. Third, we will develop approaches that aggregate reasoning knowledge across many formal, semi-formal and informal corpora and deploy the methods as strong automation services for the formal proof community. The expected outcome is our ability to prove automatically at least 50% more theorems in high-assurance projects such as Flyspeck and seL4, bringing a major breakthrough in formal reasoning and verification. As an AI effort, the project offers a unique path to large-scale semantic AI. The formal corpora concentrate centuries of deep human thinking in a computer-understandable form on which deductive and inductive AI can be combined and co-evolved, providing new insights into how humans do mathematics and science.

1 499 500 €

Start date: 2015-09-01, End date: 2020-10-31

The threads produced by velvet worms are remarkably sticky and stiff; the beak of a jumbo squid is extremely hard; and spider silk is incredibly tough. The extraordinary material properties found in these natural systems have been of interest to researchers for a long time. However, only recently, biologists discovered that a crucial element in the processing of many of these materials are coacervates, which are concentrated macromolecular phases that form upon liquid liquid phase separation from the initial solution. An understanding is emerging that the liquid coacervate phases enable extrusion of the material and allow for conformational changes within the material before solidification. Thus, the coacervate nature is crucial for obtaining extraordinary property profiles in these natural materials. Here I propose to mimic this environmentally benign processing of coacervate extrusion for the development of completely new synthetic materials. Previous work in my group has led to the development of bio-inspired synthetic coacervates with well-controlled architecture and composition, and of various tools to study their mechanics. Here, I will take advantage of this expertise to develop unique material systems by extruding synthetic coacervates and by using the induced mechanical stress to obtain alignment and conformational changes. Analogous to the wide variety of materials found in natural systems that commence as a coacervate, this processing principle may be applicable to a wide variety of synthetic material classes. In this research program coacervate extrusion will be used to produce fibers, rods or scaffolds composed of: polyelectrolyte complexes, liquid-crystal elastomers, peptide-polymers, protein-polymers and nanocomposites. This bio-inspired processing principle of coacervate extrusion will lead to materials with unexplored property profiles and holds great promise for the development of novel high performance materials obtained by green processing.

2 000 000 €

Start date: 2020-09-01, End date: 2025-08-31

Most of the developments in catalyst are still based on serendipitous and trial-and-error approaches, in which potential systems can be overlooked simply because of the sub-optimal conditions of the initial activity assessment. Mechanistic and kinetic studies could provide a framework for a more adequate assessment of new catalysts, but such rigorous experiments are not practical for general catalyst discovery. Modern chemical theory and computations hold a promise to be employed in new efficient theory-guided approaches for rational catalyst and process development. The main aim of DeLiCat is to formulate a hierarchical computational strategy for the design and synthesis of new non-critical metal-based catalysts for sustainable chemical transformations. New, durable and cheap, yet, highly active and selective tailor-made catalyst for hydrogenation of carboxylic acids and their esters as well as for acceptorless dehydrogenation of alcohols will be developed. The research will follow an innovative strategy combining advanced chemical theory, computational screening and experimental approaches from the fields of homogeneous and heterogeneous catalysis in an efficient knowledge exchange loop. Computer simulations will reveal complex reaction networks that determine the “death” and the “life” of catalyst systems. These insights will be used in targeted design of novel multifunctional catalyst systems to direct the selectivity of the reaction network and to prevent deactivation paths. Complementary experimental studies will guide and validate the theoretical predictions. DeLiCAT represents a leap forward in unified first principles-guided catalyst design for liquid phase chemical transformations. The new theoretical concepts, methodological advances as well as the novel superior catalyst systems developed here will be applicable in various areas including biomass valorization, homogeneous and heterogeneous catalysis as well as hydrogen technology.

1 999 524 €

Start date: 2017-05-01, End date: 2022-04-30