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Technical University of Munich
Country: Germany
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738 Projects, page 1 of 148
  • Open Access mandate for Publications
    Funder: EC Project Code: 101021526
    Overall Budget: 2,499,160 EURFunder Contribution: 2,499,160 EUR
    Partners: TUM

    While the human lung is undoubtedly an essential organ, and respiratory diseases are leading causes of death and disability in the world, there still exist a lot of mysteries wrt vital processes. The main reason for this is the complete lack of measurement methods or medical imaging techniques that would allow to study dynamic processes in essential parts of a living human lung. While this would be a perfect setup for computational modeling, existing models suffer from severe constraints disabling them to unveil those essential secrets. This project aims to build on a number of most promising recent advances in modeling and high-performance simulation to present the first comprehensive computational model of the respiratory system. For this purpose, it builds upon a recent exascale-ready incompressible flow solver, toughen it up for lung specific challenges and enrich it with multiphysics capabilities to capture tissue interaction and gas transport. Parts of the respiratory zone will be represented by multiphase poroelastic media and novel pleural boundary conditions will be developed. The coupled pulmonary circulation will be included and represented by an embedded reduced dimensional network and additional phases. In order to appropriately individualize the model and also being able to adapt it during disease progression, a novel physics-based probabilistic learning approach will be developed. This will allow to use most of the very diverse and scarce data in clinical settings. Finally, special models will be developed to bridge to the micro scale. The models developed and studied here will provide unprecedented insights for biomedical scientists, and practitioners at the same time, and will help to substantially reduce elaborate animal and multicenter studies. This will be a crucial step in order to establish a shift of paradigm in health care. Novel models/tools developed here will also be very useful in other areas of biomedical engineering and beyond.

  • Open Access mandate for Publications
    Funder: EC Project Code: 850764
    Overall Budget: 1,499,380 EURFunder Contribution: 1,499,380 EUR
    Partners: TUM

    From fine chemical synthesis over combustion control to electrode design – the majority of chemical reactions rely on catalysts to improve energy and material efficiency. Yet, the atomic-scale processes underlying a catalytic reaction at elevated pressures are far less well-understood than one might expect. Indeed, the successful optimization of industrial catalysts is typically achieved by ‘trial and error’. If we precisely understood the correlation between catalyst dynamics and activity, we could instead design stable, yet intrinsically dynamic (i.e. structurally fluxional) catalysts, drastically reduce our waste of noble metals by using only the most active particles and replace rare and toxic materials. This project constitutes a fundamental and systematic investigation of heterogeneous catalysis in action. My aim is to map the pressure and temperature range in which supported particle catalysts are stable, and correlate particle size and support morphology with dynamics and stability. To do so, I will combine my experience with surface dynamics studies, video-rate scanning tunneling microscopy (STM), ambient pressure (AP) surface science and cluster research. State-of-the-art video-rate APSTM will enable me to observe catalyst dynamics such as sintering, adsorbate spillover onto the support, dynamic structural fluxionality of clusters and support roughening as a function of reactant partial pressure and temperature. The novelty of this project lies in the direct observation of catalyst particles, defined to the exact number of atoms, under realistic reaction conditions in order to tune reactivity by controlling their dynamics and stability on structurally and electronically optimized oxide supports. AP X-ray photoelectron spectroscopy (APXPS) will supply complementary information about chemical changes occurring in cluster and support. The knowledge gained will contribute to the targeted design of more active and efficient catalysts for specific applications.

  • Open Access mandate for Publications
    Funder: EC Project Code: 947630
    Overall Budget: 1,499,900 EURFunder Contribution: 1,499,900 EUR
    Partners: TUM

    Fluid flows through tubular networks are crucial for life as they are the dominant means of substance and signal transport. In living networks – across organisms as disparate as animals and fungi, alterations of flows drive dynamic adaptation of tube diameters which in turn alters transport performance. In effect, local transient stimuli that affect flows are memorized as long-lived alterations to tube diameters across the network. I aim to identify the physical principles behind fluid flows driving dynamic memory storage in network morphology. I will thereby uncover how to control network morphology and performance by applied flow-altering stimuli, which promises significant advances in important challenges of the future: treatment of vascular diseases and tumour development, encoding complex behaviour in soft robotics and self-optimizing porous media. The dynamic nature of flows and networks’ complex morphologies requires a combined experimental and theoretical approach to address: What are the physical mechanisms of how flows in living tubular networks can encode and store information about stimuli? How do memories impact network performance? As experimental model system I choose the slime mould Physarum polycephalum. It is ideally suited as a starting point, as it reduces the problem in its complexity to just a tubular network. This model allows me to follow with unprecedented level of detail how stimuli transiently perturb network-wide flows – flows that subsequently drive long-term changes in network morphology. Theoretical models will verify mechanisms and allow investigation of impact on network function. Identified principles of dynamic memory formation will be applied to study consequences of mini-stroke stimuli and possible treatment in brain microvasculature and to design self-optimizing porous media. I will develop general principles advancing physics and biology with far-reaching implications in medicine and engineering.

  • Funder: EC Project Code: 240168
    Partners: TUM
  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 101045008
    Overall Budget: 1,997,990 EURFunder Contribution: 1,997,990 EUR
    Partners: TUM

    Global climate and energy challenges require efficient, robust and scalable catalysts for the conversion of renewable energies. Nature has evolved extremely active catalysts (enzymes) for the conversion of small molecules relevant to energy (H2, CO2, N2). The scalability of these enzymes offers distinct advantages over the rare, precious metals that are currently used in energy conversion. Unfortunately, the enzymes are unable to tolerate the extreme conditions of operating fuel cells or electrolyzers. Directed evolution is a powerful approach for improving enzymes, but is mostly restricted to natural amino acids and biological conditions, with limited compatibility for evolving enzymes toward enhanced resistance in abiotic systems. Here, I aim to establish directed evolution in fully abiotic systems, using artificial amino acids to make artificial enzymes that are stable even in extreme conditions. Towards this, I will establish new electrochemical peptide synthesis platforms to enable the generation of enzyme-length peptides using both natural and artificial amino acids. Extended libraries of artificial enzyme variants will be produced and screened directly on electrode microarrays. Top enzyme candidates for the conversion of H2 will be selected using fuel cell/electrolyzer conditions as the evolutionary criteria. By the end, I will have a new procedure for synthesizing libraries of full-length artificial proteins, enabling the creation of thousands of enzyme variants using artificial building blocks. The generation of high-quality datasets will be transformative to drive future machine learning-based evolution steps for both full size enzymes and small-molecule catalysts with applications beyond H2 evolution. We will have discovered highly active catalysts able to sustain conditions of large-scale energy conversion devices, accelerating breakthroughs toward the economically competitive use of renewable energies for fuel and chemical production.