ERC FUNDED PROJECTS

We use IR spectroscopy of trapped ions in a cryogenic FT-ICR spectrometer to probe non-covalent, “dispersion” interactions in large, gas-phase molecular ions. We will measure conformational equilibria by N-H frequency shifts, and correlate gas-phase IR frequency to the N-H-N bond angle in an ionic H-bond. Substituents on “onium” cations can adopt various conformations, whose energies map interaction potentials. Substituents on their proton-bound dimers interact non-covalently through dispersion forces, whose quantitative evaluation in large molecules has remained difficult despite dispersion becoming increasingly cited as a design principle in the construction of catalysts and materials. The non-covalent interactions bend the N-H-N bond, leading to large shifts in the IR frequency. The proton-bound dimer acts like a molecular balance where the non-covalent interaction, is set against the bending potential in an ionic hydrogen bond. Despite encouragingly accurate calculations for small molecules, experimental benchmarks for large molecules in the gas phase remain scarce, and there is evidence that the good results for small molecules may not extrapolate reliably to large molecules. The present proposal introduces a new experimental probe of non-covalent interactions, providing a sensitive test of the diverging results coming from various computational methods and other experiments. The experiment must be done on isolated molecules in the gas phase, as previous work has shown that solvation substantially cancels out the attractive potential. Accordingly, the proposed experimental design, which involves a custom-built spectrometer, newly available tunable IR sources, chemical synthesis of custom substrates, and quantum calculations up to coupled-cluster levels of theory, showcases how an interdisciplinary approach combining physical and organic chemistry can solve a fundamental problem that impacts how we understand steric effects in organic chemistry.

2 446 125 €

Start date: 2019-05-01, End date: 2024-04-30

Curli are functional amyloid fibers that constitute the major protein component of the extracellular matrix in pellicle biofilms formed by Bacteroidetes and Proteobacteria. Unlike the protein misfolding and aggregation events seen in pathological amyloid diseases such as Alzheimer’s and Parkinson’s disease, curli are the product of a dedicated protein secretion machinery. Curli formation requires a specialised and mechanistically unique transporter in the bacterial outer membrane, as well as two soluble accessory proteins thought to facilitate the safe guidance of the curli subunits across the periplasm and to coordinate their self-assembly at cell surface. In this interdisciplinary research program we will study the structural and molecular biology of E. coli curli biosynthesis and address the fundamental questions concerning the molecular processes that allow the spatially and temporally controlled transport and deposition of these pro-amyloidogenic polypeptides. We will structurally unravel the secretion machinery, trap and analyse critical secretion intermediates and through in vitro reconstitution, assemble a minimal, self-sufficient peptide transport and fiber assembly system. The new insights gained will set the stage for targeted interventions in curli -mediated biofilm formation and this research project will develop a new framework to harness the unique properties found in curli structure and biosynthesis for biotechnological applications as in patterned functionalized nanowires and directed, selective peptide carriers.

1 989 489 €

Start date: 2015-06-01, End date: 2020-05-31

The development of longer lasting, higher energy density and cheaper rechargeable batteries represents one of the major technological challenges of our society, batteries representing the limiting components in the shift from gasoline-powered to electric vehicles. They are also required to enable the use of more (typically intermittent) renewable energy, to balance demand with generation. This proposal seeks to develop and apply new NMR metrologies to determine the structure and dynamics of the multiple electrode-electrolyte interfaces and interphases that are present in these batteries, and how they evolve during battery cycling. New dynamic nuclear polarization (DNP) techniques will be exploited to extract structural information about the interface between the battery electrode and the passivating layers that grow on the electrode materials (the solid electrolyte interphase, SEI) and that are inherent to the stability of the batteries. The role of the SEI (and ceramic interfaces) in controlling lithium metal dendrite growth will be determined in liquid based and all solid state batteries. New DNP approaches will be developed that are compatible with the heterogeneous and reactive species that are present in conventional, all-solid state, Li-air and redox flow batteries. Method development will run in parallel with the use of DNP approaches to determine the structures of the various battery interfaces and interphases, testing the stability of conventional biradicals in these harsh oxidizing and reducing conditions, modifying the experimental approaches where appropriate. The final result will be a significantly improved understanding of the structures of these phases and how they evolve on cycling, coupled with strategies for designing improved SEI structures. The nature of the interface between a lithium metal dendrite and ceramic composite will be determined, providing much needed insight into how these (unwanted) dendrites grow in all solid state batteries. DNP approaches coupled with electron spin resonance will be use, where possible in situ, to determine the reaction mechanisms of organic molecules such as quinones in organic-based redox flow batteries in order to help prevent degradation of the electrochemically active species. This proposal involves NMR method development specifically designed to explore a variety of battery chemistries. Thus, this proposal is interdisciplinary, containing both a strong emphasis on materials characterization, electrochemistry and electronic structures of materials, interfaces and nanoparticles, and on analytical and physical chemistry. Some of the methodology will be applicable to other materials and systems including (for example) other electrochemical technologies such as fuel cells and solar fuels and the study of catalysts (to probe surface structure).

3 498 219 €

Start date: 2019-10-01, End date: 2024-09-30

This project aims to improve evidence-based decision-making. What makes it radical is that it plans to do this in situations (common for critical risk assessment problems) where there is little or even no data, and hence where traditional statistics cannot be used. To address this problem Bayesian analysis, which enables domain experts to supplement observed data with subjective probabilities, is normally used. As real-world problems typically involve multiple uncertain variables, Bayesian analysis is extended using a technique called Bayesian networks (BNs). But, despite many great benefits, BNs have been under-exploited, especially in areas where they offer the greatest potential for improvements (law, medicine and systems engineering). This is mainly because of widespread resistance to relying on subjective knowledge. To address this problem much current research assumes sufficient data are available to make the expert’s input minimal or even redundant; with such data it may be possible to ‘learn’ the underlying BN model. But this approach offers nothing when there is limited or no data. Even when ‘big’ data are available the resulting models may be superficially objective but fundamentally flawed as they fail to capture the underlying causal structure that only expert knowledge can provide. Our solution is to develop a method to systemize the way expert driven causal BN models can be built and used effectively either in the absence of data or as a means of determining what future data is really required. The method involves a new way of framing problems and extensions to BN theory, notation and tools. Working with relevant domain experts, along with cognitive psychologists, our methods will be developed and tested experimentally on real-world critical decision-problems in medicine, law, forensics, and transport. As the work complements current data-driven approaches, it will lead to improved BN modelling both when there is extensive data as well as none.

1 572 562 €

Start date: 2014-04-01, End date: 2018-03-31