The increasing level of carbon dioxide (CO2) in the atmosphere presents a critical factor for climate change and action must be taken urgently to minimise its impact. Using CO2 as a carbon source in the chemical industry for its conversion into valuable chemicals is an advantageous strategy to reduce CO2 emissions and provide a sustainable and cheap source of raw materials to help combat raw material scarcity. Interfacing CO2 reducing enzyme/enzymatic cascades with electrocatalysis present a particular approach to directly power product generation from CO2 with renewable electricity. This was achieved for a few enzymatic cascades; however, these proof-of-concept demonstrations are far from practical use due to lack of efficient method to guide the rational design of these complex multi-enzymes cascades on electrodes, resulting in high costs and low yields. Within this project (TransCO2), my overarching aim is to apply quantitative analysis and rational assembly of enzymatic cascades to enable a breakthrough in bioelectrocatalytic-technology to transform carbon dioxide (CO2) directly into high value mevalonate (C5) at high isolated yields, using electricity as energy source. Specifically, CO2 is firstly converted to formate by the enzyme Formate dehydrogenase (FDH), which is then further converted to mevalonate via 8 steps enzymatic conversions. The whole system will function in an electrochemical cell to make use of electricity as power input. I will build a kinetic model for this enzymatic cascade and implement it to guide the design and optimisation towards highest efficiency with minimal utilization of expensive cofactors.The overall technology in TransCO2 will be a generally applicable breakthrough for efficient production of high-value chemicals from CO2, enabling the large-scale use of CO2 utilisation.
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.
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.
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.