ERC FUNDED PROJECTS

Phase diagrams revolutionized materials development by predicting the conditions for phase stability and transformations, providing a thermodynamic concept for materials design including synthesis, processing and application. Similarly, surface science has established thermodynamic concepts for surface states and transitions, but the analogon for grain boundaries (GB) is just emerging due to their complexity. GB are among the most prominent microstructure defects separating grains in polycrystalline materials spanning a multidimensional space. Unlocking control of GB phases and their transitions will enable a new level of materials design allowing to tailor functional & structural properties. This proposal targets on (i) predicting and resolving GB phase transitions, (ii) establishing guidelines for GB phase transitions and GB phase diagrams, (iii) correlating GB phase transitions with property changes, (iv) providing compositional-structural design criteria for GB engineering, (v) which will be tested by demonstrators with tailored GB strength and GB mobility. GB-CORRELATE focusses on Cu and Al alloys in form of thin films as this allows to implement a hierarchical strategy expanding from individual special GB to GB networks and a transfer of the GB concepts to thin film applications. The infinite number of GB requires also statistical approaches; combinatorial thin film deposition will be used to establish Cu and Al alloy films with substitutional (Ag, Al, Cu, Si, Ni) and interstitial (B) solute elements. High throughput grain growth experiments will be employed to detect GB phase transitions by changes in GB mobility. Advanced atomic resolved correlated microscopy and spectroscopy supported by powerful computational approaches will identify GB phases and correlate them with transport properties. Sophisticated in-situ micromechanical studies lay the ground for interlinking GB phases and GB mechanics, finally harvested to create mechanically exceptional materials.

2 500 000 €

Start date: 2018-08-01, End date: 2023-07-31

Due to the intricate process of transferring diagnostic imaging data into patient-specific models, simulation workflows involving complex physiological geometries largely rely on the manual intervention of specially trained analysts. This constitutes a significant roadblock for a wider adoption of predictive simulation in clinical practice, as the associated cost and response times are incompatible with tight budgets and urgent decision-making. Therefore, a new generation of imaging-through-analysis tools is needed that can be run autonomously in hospitals and medical clinics. The overarching goal of ImageToSim is to make substantial progress towards automation by casting image processing, geometry segmentation and physiology-based simulation into a unifying finite element framework that will overcome the dependence of state-of-the-art procedures on manual intervention. In this context, ImageToSim will fill fundamental technology gaps by developing a series of novel comprehensive variational multiscale methodologies that address robust active contour segmentation, upscaling of voxel-scale parameters, transition of micro- to macro-scale failure and flow through vascular networks of largely varying length scales. Focusing on osteoporotic bone fracture and liver perfusion, ImageToSim will integrate the newly developed techniques into an imaging-through-analysis prototype that will come significantly closer to automated operation than any existing framework. Tested and validated in collaboration with clinicians, it will showcase pathways to new simulation-based clinical protocols in osteoporosis prevention and liver surgery planning. Beyond its technical scope, ImageToSim will help establish a new paradigm for patient-specific simulation research that emphasizes full automation as a key objective, accelerating the much-needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, and person-centered.

1 555 403 €

Start date: 2019-01-01, End date: 2023-12-31

Crystalline defects in metals and semiconductors are responsible for a wide range of mechanical, optical and electronic properties. Controlling the evolution of dislocations, i.e. line-like defects and the carrier of plastic deformation, interacting both among themselves and with other microstructure elements allows tailoring material behaviors on the micro and nanoscale. This is essential for rational design approaches towards next generation materials with superior mechanical properties. For nearly a century, materials scientists have been seeking to understand how dislocation systems evolve. In-situ microscopy now reveals complex dislocation networks in great detail. However, without a sufficiently versatile and general methodology for extracting, assembling and compressing dislocation-related information the analysis of such data often stays at the level of “looking at images” to identify mechanisms or structures. Simulations are increasingly capable of predicting the evolution of dislocations in full detail. Yet, direct comparison, automated analysis or even data transfer between small scale plasticity experiments and simulations is impossible, and a large amount of data cannot be reused. The vision of MuDiLingo is to develop and establish for the first time a Unifying Multiscale Language of Dislocation Microstructures. Bearing analogy to audio data conversion into MP3, this description of dislocations uses statistical methods to determine data compression while preserving the relevant physics. It allows for a completely new type of high-throughput data mining and analysis, tailored to the specifics of dislocation systems. This revolutionary data-driven approach links models and experiments on different length scales thereby guaranteeing true interoperability of simulation and experiment. The application to technologically relevant materials will answer fundamental scientific questions and guide towards design of superior structural and functional materials.

1 499 145 €

Start date: 2017-11-01, End date: 2022-10-31

Quorum sensing (QS) is a bacterial cell–cell communication process involving the production, release, and detection of extracellular signal molecules called autoinducers. QS is key to all microbiology as it enables otherwise solitary bacteria to coordinate complex cooperative tasks such as biofilm formation and pathogenesis. Consequently, targeting QS is a promising new concept for antimicrobial therapy. However, for this concept to become reality, we must first identify QS systems in pathogenic bacteria, discover the relevant autoinducers and study the underlying regulatory principles. I recently identified a new QS pathway in Vibrio cholerae, the causative agent of cholera disease. The autoinducer of the system is DPO (3,5-dimethylpyrazin-2-ol), a new molecule to biology and the first pyrazine involved in QS. DPO production is widespread among microbes including pathogenic and commensal bacteria. V. cholerae synthesizes DPO from host mucins and our preliminary data show that DPO controls collective phenotypes, such as biofilm formation and toxin production in this major human pathogen. I therefore hypothesize that DPO connects virulence, QS and communication with the host microbiota in V. cholerae and related bacteria. The overarching goal of this project is to understand the roles of DPO in host-microbe interaction and collective behaviours. To this end, we will pursue three key research goals. First, we will study the molecular parameters underlying DPO-signalling and probe the global effects of DPO on gene expression. Second, we will focus on the role of DPO in virulence of V. cholerae and other pathogens. Third, we will probe the effect of DPO on microbial behaviours, such as swarming and biofilm formation. This combined work will provide a comprehensive model for DPO-signalling in bacteria, which will not only advance the fundamental understanding of QS-based communication strategies, but might also provide the framework for QS-inspired anti-infectives.

1 499 250 €

Start date: 2018-01-01, End date: 2022-12-31