ERC FUNDED PROJECTS

This proposal aims to develop a generic synthesis approach for core-shell nanoparticles by unravelling the relevant mechanisms. Core-shell nanoparticles have high potential in heterogeneous catalysis, energy storage, and medical applications. However, on a fundamental level there is currently a poor understanding of how to produce such nanostructured particles in a controllable and scalable manner. The main barriers to achieving this goal are understanding how nanoparticles agglomerate to loose dynamic clusters and controlling the agglomeration process in gas flows during coating, such that uniform coatings can be made. This is very challenging because of the two-way coupling between agglomeration and coating. During the coating we change the particle surfaces and thus the way the particles stick together. Correspondingly, the stickiness of particles determines how easy reactants can reach the surface. Innovatively the project will be the first systematic study into this multi-scale phenomenon with investigations at all relevant length scales. Current synthesis approaches – mostly carried out in the liquid phase – are typically developed case by case. I will coat nanoparticles in the gas phase with atomic layer deposition (ALD): a technique from the semi-conductor industry that can deposit a wide range of materials. ALD applied to flat substrates offers excellent control over layer thickness. I will investigate the modification of single particle surfaces, particle-particle interaction, the structure of agglomerates, and the flow behaviour of large number of agglomerates. To this end, I will apply a multidisciplinary approach, combining disciplines as physical chemistry, fluid dynamics, and reaction engineering.

1 409 952 €

Start date: 2011-12-01, End date: 2016-11-30

Lithium ion batteries offer tremendous potential as an enabling technology for sustainable transportation and development. However, their widespread usage as the energy storage solution for electric mobility and grid-level integration of renewables is impeded by the fact that current state-of-the-art lithium ion batteries have energy densities that are too small, charge- and discharge rates that are too low, and costs that are too high. Highly publicized instances of catastrophic failure of lithium ion batteries raise questions of safety. Understanding the limitations to battery performance and origins of the degradation and failure is highly complex due to the difficulties in studying interrelated processes that take place at different length and time scales in a corrosive environment. In the project, we will (1) develop and implement quantitative methods to study the complex interrelations between structure and electrochemistry occurring at the nano-, micron-, and milli-scales in lithium ion battery active materials and electrodes, (2) conduct systematic experimental studies with our new techniques to understand the origins of performance limitations and to develop design guidelines for achieving high performance and safe batteries, and (3) investigate economically viable engineering solutions based on these guidelines to achieve high performance and safe lithium ion batteries.

1 500 000 €

Start date: 2016-05-01, End date: 2021-04-30

Meta-materials, best known for their extraordinary properties (e.g. negative stiffness), are halfway from both materials and structures: their unusual properties are direct results of their complex 3D structures. This project introduces a new class of meta-materials called meta-biomaterials. Meta-biomaterials go beyond meta-materials by adding an extra dimension to the complex 3D structure, i.e. complex and precisely controlled surface nano-patterns. The 3D structure gives rise to unprecedented or rare combination of mechanical (e.g. stiffness), mass transport (e.g. permeability, diffusivity), and biological (e.g. tissue regeneration rate) properties. Those properties optimize the distribution of mechanical loads and the transport of nutrients and oxygen while providing geometrical shapes preferable for tissue regeneration (e.g. higher curvatures). Surface nano-patterns communicate with (stem) cells, control their differentiation behavior, and enhance tissue regeneration. There is one important problem: meta-biomaterials cannot be manufactured with current technology. 3D printing can create complex shapes while nanolithography creates complex surface nano-patterns down to a few nanometers but only on flat surfaces. There is, however, no way of combining complex shapes with complex surface nano-patterns. The groundbreaking nature of this project is in solving that deadlock using the Origami concept (the ancient Japanese art of paper folding). In this approach, I first decorate flat 3D-printed sheets with nano-patterns. Then, I apply Origami techniques to fold the decorated flat sheet and create complex 3D shapes. The sheet knows how to self-fold to the desired structure when subjected to compression, owing to pre-designed joints, crease patterns, and thickness/material distributions that control its mechanical instability. I will demonstrate the added value of meta-biomaterials in improving bone tissue regeneration using in vitro cell culture assays and animal models

1 499 600 €

Start date: 2016-02-01, End date: 2021-01-31

Many questions in science, policy making and everyday life are of a causal nature: how would changing A influence B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such causal predictions is of great value for practical applications in a variety of disciplines. Over the last two decades, remarkable progress has been made in the field. However, even though state-of-the-art causal inference algorithms work well on simulated data when all their assumptions are met, there is still a considerable gap between theory and practice. The goal of CAFES is to bridge that gap by developing theory and algorithms that will enable large-scale applications of causal inference in various challenging domains in science, industry and decision making. The key challenge that will be addressed is how to deal with cyclic causal relationships ("feedback loops"). Feedback loops are very common in many domains (e.g., biology, economy and climatology), but have mostly been ignored so far in the field. Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems. Extensions to stationary and non-stationary processes will be developed to advance the state-of-the-art in causal analysis of time-series data. In order to optimally use available resources, computationally efficient and statistically robust algorithms for causal inference from observational and interventional data in the context of confounders and feedback will be developed. The work will be done with a strong focus on applications in molecular biology, one of the most promising areas for automated causal inference from data.

1 405 652 €

Start date: 2015-09-01, End date: 2020-08-31