ERC FUNDED PROJECTS

The realization of “the internet of things” is inevitably constrained at the level of miniaturization that can be achieved in the electronic devices. A variety of technologies are now going through a process of miniaturization from micro-electromechanical systems (MEMS) to biomedical sensors, and actuators. The ultimate goal is to combine several components in an individual multifunctional platform, realizing on-chip technology. Devices have to be constrained to small footprints and exhibit high performance. Thus, the miniaturization process requires the introduction of new manufacturing processes to fabricate devices in the 3D space over small areas. 3D printing via robocasting is emerging as a new manufacturing technique, which allows shaping virtually any materials from polymers to ceramic and metals into complex architectures. The goal of this research is to establish a 3D printing paradigm to produce miniaturized complex shape devices with diversified functions for on-chip technologies adaptable to “smart environment” such as flexible substrates, smart textiles and biomedical sensors. The elementary building blocks of the devices will be two-dimensional nanomaterials, which present unique optical, electrical, chemical and mechanical properties. The synergistic combination of the intrinsic characteristics of the 2D nanomaterials and the specific 3D architecture will enable advanced performance of the 3D printed objects. This research programme will demonstrate 3D miniaturized energy storage and energy conversion units fabricated with inks produced using a pilot plant. These units are essential components of any on-chip platform as they ensure energy autonomy via self-powering. Ultimately, this research will initiate new technologies based on miniaturized 3D devices.

1 999 968 €

Start date: 2019-09-01, End date: 2024-08-31

Large protein complexes carry out some of the most complex functions in biology. Such structures are often assembled spontaneously from individual components through the process of self-assembly. If self-assembled protein complexes could be engineered from first principle it would enable a wide range of applications in biomedicine, nanotechnology and materials science. Recently, approaches to rationally design proteins to self-assembly into predefined structures have emerged. The highlight of this work is the design of protein cages that may be engineered into protein containers. However, current approaches for self-assembly design does not result in the assemblies with the required structural complexity to encode many of the sophisticated functions found in nature. To move forward, we have to learn how to engineer protein subunits with more than one designed interface that can assemble into tightly interacting complexes. In this proposal we propose a new protein design paradigm, shape directed protein design, in order to address shortcomings of the current methodology. The proposed method combines geometric shape matching and computational protein design. Using this approach we will de novo design assemblies with a wide variety of structural states, including protein complexes with cyclic and dihedral symmetry as well as icosahedral protein capsids built from novel protein building blocks. To enable these two design challenges we also develop a high-throughput assay to measure assembly stability in vivo that builds on a three-color fluorescent assay. This method will not only facilitate the screening of orders of magnitude more design constructs, but also enable the application of directed evolution to experimentally improve stable and assembly properties of designed containers as well as other designed assemblies.

2 325 292 €

Start date: 2018-06-01, End date: 2023-05-31

Currently fuels, plastics, and drugs are predominantly manufactured from oil. A transition towards renewable resources critically depends on new catalysts, for instance to convert small molecules (such as solar or biomass derived hydrogen, carbon monoxide, water and carbon dioxide) into more complex ones (such as oxygenates, containing oxygen atoms in their structure). Catalyst development now often depends on trial and error rather than rational design, as the heterogeneity of these composite systems hampers detailed understanding of the role of each of the components. I propose 3D model catalysts as a novel enabling tool to overcome this problem. Their well-defined nature allows unprecedented precision in the variation of structural parameters (morphology, spatial distribution) of the individual components, while at the same time they mimic real catalysts closely enough to allow testing under industrially relevant conditions. Using this approach I will address fundamental questions, such as: * What are the mechanisms (structural, electronic, chemical) by which non-metal promoters influence the functionality of copper-based catalysts? * Which nanoalloys can be formed, how does their composition influence the surface active sites and catalytic functionality under reaction conditions? * Which size and interface effects occur, and how can we use them to tune the actitivity and selectivity towards desired products? Our 3D model catalysts will be assembled from ordered mesoporous silica and carbon support materials and Cu-based promoted and bimetallic nanoparticles. The combination with high resolution characterization and testing under realistic conditions allows detailed insight into the role of the different components; critical for the rational design of novel catalysts for a future more sustainable production of chemicals and fuels from renewable resources.

1 999 625 €

Start date: 2015-09-01, End date: 2020-11-30

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