ERC FUNDED PROJECTS

"Two-dimensional (2D) nanosheets, which possess a high degree of anisotropy with nanoscale thickness and infinite length in other dimensions, hold enormous promise as a novel class of ultrathin 2D nanomaterials with various unique functionalities and properties, and exhibit great potential in energy storage and conversion systems that are substantially different from their respective 3D bulk forms. Here I propose a strategy for the synthesis and processing of various 2D nanosheets across a broad range of inorganic, organic and polymeric materials with molecular-level or thin thickness through both the top-down exfoliation of layered materials and the bottom-up assembly of available molecular building blocks. Further, I aim to develop the synthesis of various 2D-nanosheet based composite materials with thickness of less than 100 nm and the assembly of 2D nanosheets into novel hierarchal superstrucutures (like aerogels, spheres, porous particles, nanotubes, multi-layer films). The structural features of these 2D nanomaterials will be controllably tailored by both the used layered precursors and processing methodologies. The consequence is that I will apply and combine defined functional components as well as assembly protocols to create novel 2D nanomaterials for specific purposes in energy storage and conversion systems. Their unique characters will include the good electrical conductivity, excellent mechanical flexibility, high surface area, high chemical stability, fast electron transport and ion diffusion etc. Applications will be mainly demonstrated for the construction of lithium ion batteries (anode and cathode), supercapacitors (symmetric and asymmetric) and fuel cells. As the key achievements, I expect to establish the delineation of reliable structure-property relationships and improved device performance of 2D nanomaterials."

1 500 000 €

Start date: 2012-09-01, End date: 2017-08-31

This proposal brings together two fields in biology, namely the emerging field of phase-separated assemblies in cell biology and state-of-the-art cellular cryo-electron tomography, to advance our understanding on a fundamental, yet illusive, question: the molecular organization of the cytoplasm. Eukaryotes organize their biochemical reactions into functionally distinct compartments. Intriguingly, many, if not most, cellular compartments are not membrane enclosed. Rather, they assemble dynamically by phase separation, typically triggered upon a specific event. Despite significant progress on reconstituting such liquid-like assemblies in vitro, we lack information as to whether these compartments in vivo are indeed amorphous liquids, or whether they exhibit structural features such as gels or fibers. My recent work on sample preparation of cells for cryo-electron tomography, including cryo-focused ion beam thinning, guided by 3D correlative fluorescence microscopy, shows that we can now prepare site-specific ‘electron-transparent windows’ in suitable eukaryotic systems, which allow direct examination of structural features of cellular compartments in their cellular context. Here, we will use these techniques to elucidate the structural principles and cytoplasmic environment driving the dynamic assembly of two phase-separated compartments: Stress granules, which are RNA bodies that form rapidly in the cytoplasm upon cellular stress, and centrosomes, which are sites of microtubule nucleation. We will combine these studies with a quantitative description of the crowded nature of cytoplasm and of its local variations, to provide a direct readout of the impact of excluded volume on molecular assembly in living cells. Taken together, these studies will provide fundamental insights into the structural basis by which cells form biochemical compartments.

1 228 125 €

Start date: 2018-02-01, End date: 2023-01-31

4D reconstruction, the camera-based dense dynamic scene reconstruction, is a grand challenge in computer graphics and computer vision. Despite great progress, 4D capturing the complex, diverse real world outside a studio is still far from feasible. 4DRepLy builds a new generation of high-fidelity 4D reconstruction (4DRecon) methods. They will be the first to efficiently capture all types of deformable objects (humans and other types) in crowded real world scenes with a single color or depth camera. They capture space-time coherent deforming geometry, motion, high-frequency reflectance and illumination at unprecedented detail, and will be the first to handle difficult occlusions, topology changes and large groups of interacting objects. They automatically adapt to new scene types, yet deliver models with meaningful, interpretable parameters. This requires far reaching contributions: First, we develop groundbreaking new plasticity-enhanced model-based 4D reconstruction methods that automatically adapt to new scenes. Second, we develop radically new machine learning-based dense 4D reconstruction methods. Third, these model- and learning-based methods are combined in two revolutionary new classes of 4DRecon methods: 1) advanced fusion-based methods and 2) methods with deep architectural integration. Both, 1) and 2), are automatically designed in the 4D Real World Reconstruction Loop, a revolutionary new design paradigm in which 4DRecon methods refine and adapt themselves while continuously processing unlabeled real world input. This overcomes the previously unbreakable scalability barrier to real world scene diversity, complexity and generality. This paradigm shift opens up a new research direction in graphics and vision and has far reaching relevance across many scientific fields. It enables new applications of profound social pervasion and significant economic impact, e.g., for visual media and virtual/augmented reality, and for future autonomous and robotic systems.

1 977 000 €

Start date: 2018-09-01, End date: 2023-08-31

Mammalian organisms possess the remarkable ability to maintain internal body temperature (Tcore) within a narrow range close to 37°C despite wide environmental temperature variations. The brain’s neural “thermostat” is made up by central circuits in the hypothalamic preoptic area (POA), which orchestrate peripheral thermoregulatory responses to maintain Tcore. Thermogenesis requires metabolic fuel, suggesting intricate connections between the thermoregulatory centre and hypothalamic circuits controlling energy balance. How the POA detects and integrates temperature and metabolic information to achieve thermal balance is largely unknown. A major question is whether this circuitry could be harnessed therapeutically to treat obesity. We have recently identified the first known molecular temperature sensor in thermoregulatory neurons of the POA, transient receptor potential melastatin 2 (TRPM2), a thermo-sensitive ion channel. I aim to use TRPM2 as a molecular marker to gain access to and probe the function of thermoregulatory neurons in vivo. I propose a multidisciplinary approach, combining local, in vivo POA temperature stimulation with optogenetic circuit-mapping to uncover the molecular and cellular logic of the hypothalamic thermoregulatory centre and to assess its medical potential to counteract metabolic syndrome. Acclimation is a beneficial adaptive process that fortifies thermal responses upon environmental temperature challenges. Thermoregulatory neuron plasticity is thought to mediate acclimation. Conversely, maladaptive thermoregulatory changes affect obesity. The cell-type-specific neuronal plasticity mechanisms underlying these changes within the POA, however, are unknown. Using ex-vivo slice electrophysiology and in vivo imaging, I propose to characterize acclimation- and obesity-induced plasticity of thermoregulatory neurons. Ultimately, I aim to manipulate thermoregulatory neuron plasticity to test its potential counter-balancing effect on obesity.

1 902 500 €

Start date: 2018-09-01, End date: 2023-08-31