ERC FUNDED PROJECTS

Imagine a technology for powering your smart devices by recovering energy from lights in your office, the random movements of your body while reading these lines or from small changes in temperature when you breathe or go out for a walk. This very technology will provide energy for wireless sensor networks monitoring the air in your city or the structural stability of buildings and large constructions remotely and sustainably, avoiding battery recharging or even replacing them. These are the challenges in micro energy harvesting from (local) ambient sources. Kinetic, thermal and solar energies are ubiquitous at our surroundings under diverse forms, but their relatively low intensity and intermittent availability limit their potential recovery by microscale devices. These restrictions call for multi-source energy harvesters working under two principles: 1) combining different single-source harvesters in one device, or 2) using multifunctional materials capable of simultaneously converting various energy sources into electricity. In 1), efficiency per unit volume can decrease compared to the individual counterparts; in 2), materials as semiconductors, polymeric and oxide ferroelectrics and hybrid perovskites may act as multisource harvesters but huge advances are required to optimize their functionalities and sustainable fabrication at large scale. I propose to fill the gap between these approaches offering an all-in-one solution to multisource energy scavenging, based on the nanoscale design of multifunctional three-dimensional materials. The demonstration of an industrially scalable one-reactor plasma/vacuum method will be crucial to integrate hybrid-scavenging components and to provide 3DScavengers materials with tailored microstructure-enhanced performance. My ultimate goal is to build nanoarchitectures for simultaneous and enhanced individual scavenging applying photovoltaic, piezo- and pyro-electric effects, minimizing the environmental cost of their synthesis

1 498 414 €

Start date: 2020-03-01, End date: 2025-02-28

Sporadic Alzheimer’s Disease (AD) accounts for the overwhelming majority of all AD cases and exclusively affects people at old age. However, mechanistic links between aging and AD pathology remain elusive. We recently discovered that in contrast to iPSC models, direct conversion of human fibroblasts into induced neurons (iNs) preserves signatures of aging, and we have started to develop a patient-based iN model system for AD. Our preliminary data suggests that AD iNs show a neuronal but de-differentiated transcriptome signature. In this project, we first combine cellular neuroscience assays and epigenetic landscape profiling to understand how neurons in AD fail to maintain their fully mature differentiated state, which might be key in permitting disease development. Next, using metabolome analysis including mass spec metabolite assessment, we explore a profound metabolic switch in AD iNs that shows surprisingly many aspects of aerobic glycolysis observed also in cancer. While this link might represent an interesting connection between two age-dependent and de-differentiation-associated diseases, it also opens new avenues to harness knowledge from the cancer field to better understand sporadic AD. We further focus on identifying and manipulating key metabolic regulators that appear to malfunction in an age-dependent manner, with the ultimate goal to define potential targets and treatment strategies. Finally, we will focus on early AD mechanisms by extending our model to mild cognitive impairment (MCI) patients. An agnostic transcriptome and epigenetic landscape approach of glutamatergic and serotonergic iNs will help to determine the earliest and probably most treatable disease mechanisms of AD, and to better understand the contribution of neuropsychiatric risk factors. We anticipate that this project will help to illuminate the mechanistic interface of cellular aging and the development of AD, and help to define new strategies for AD.

1 499 565 €

Start date: 2020-02-01, End date: 2025-01-31

"Digital geometry representations are nowadays a fundamental ingredient of many applications, as for instance CAD/CAM, fabrication, shape optimization, bio-medical engineering and numerical simulation. Among volumetric discretizations, the ""holy grail"" are hexahedral meshes, i.e. a decomposition of the domain into conforming cube-like elements. For simulations they offer accuracy and efficiency that cannot be obtained with alternatives like tetrahedral meshes, specifically when dealing with higher-order PDEs. So far, automatic hexahedral meshing of general volumetric domains is a long-standing, notoriously difficult and open problem. Our main goal is to develop algorithms for automatic hexahedral meshing of general volumetric domains that are (i) robust, (ii) scalable and (iii) offer precise control on regularity, approximation error and element sizing/anisotropy. Our approach is designed to replicate the success story of recent integer-grid map based algorithms for 2D quadrilateral meshing. The underlying methodology offers the essential global view on the problem that was lacking in previous attempts. Preliminary results of integer-grid map hexahedral meshing are promising and a breakthrough is in reach. We identified five challenges that need to be addressed in order to reach practically sufficient hexahedral mesh generation. These challenges have partly been resolved in 2D, however, the solutions do not generalize to 3D due to the increased mathematical complexity of 3D manifolds. Nevertheless, with our experience in developing and evaluating the 2D techniques, we identified the key properties that are necessary for success and accordingly propose novel volumetric counterparts that will be developed in the AlgoHex project."

1 482 156 €

Start date: 2020-02-01, End date: 2025-01-31

Amazon forests contribute vital ecosystem services, including maintaining biodiversity (>10,000 tree species) and storing large amounts of carbon. Amazonia also features prominently in global climate, carbon, and vegetation models, which assume tropical forests are effectively pristine and that past human disturbance mimicked natural processes. It is now evident that recurrent human disturbance of Amazonia, like fire and deforestation, were significant in some areas. Since those disturbances likely modify subsequent vegetation dynamics - including temporarily increasing forest capacity to absorb carbon - the emerging paradigm of human disturbance is a challenge to global ecological understanding. The focus of my project is thus to reliably determine whether human disturbances occurred in locations that form the basis of global models. A key expected outcome is to either legitimize or force revision to these models of carbon sequestration potential in Amazonia. I will innovatively integrate ecological, paleoecological, archaeological, chemical and biogeographic analyses to assess the degree to which past human disturbance drives the diversity patterns and carbon dynamics observed in modern Amazonian forests. For key long-term sites across Amazonia, I will quantify the: i) time since the last fire, ii) past fire frequency, extent and intensity, iii) past vegetation change in the presence and absence of human activity, and iv) continuity of past human activity over the last 1000 years. My results will provide the first quantification of local-scale recovery processes exceeding 100 years in tropical forests, and will determine if observed forest dynamics are driven by disturbances that occurred before modern ecological surveys began. I will then quantify the extent to which past disturbances create an overestimation of carbon storage potential, driving a profound reexamination of carbon sequestration and biodiversity patterns in Amazonia.

1 481 378 €

Start date: 2020-01-01, End date: 2024-12-31