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This chapter outlines the development of an initial sustainable business and funding framework for EMPHASIS, a distributed ESFRI Research Infrastructure dedicated to multi-scale plant phenotyping, at the end of its Preparatory Phase. The developed framework is intended to provide decision-making support for ministries representing potential members of EMPHASIS in its Operational Phase. [...]

Proton-conducting electrolysis cells (PCECs) are critical to sustainable energy technologies, as they enable the production of undiluted hydrogen that can be readily compressed and stored. These cells are essential for establishing a hydrogen economy. [...]

Recently, a novel solid oxide cell was developed for the production of green hydrogen via water electrolysis and electricity from hydrogen respectively. The cell is fuel electrode supported and utilizes a Ni-CGO composite as fuel electrode instead of Ni-YSZ as used in state-of-the-art cells. [...]

The electrochemical reduction of carbon dioxide, ECR, fueled by renewable energy sources, holds promise for addressing escalating greenhouse emissions and closing the carbon cycle. This thesis focuses on developing cost-effective catalysts to facilitate the electrochemical conversion of CO2 to CO. [...]

Lightweight solar modules are essential for diversifying the solar energy supply due to their lower weight compared with traditional glass solar modules. This opens up vast new scenarios for solar modules, especially for roofs with low load capacity, and vehicleintegrated photovoltaic (VIPV), significantly boosting the capacity of renewable energy. [...]

The growing complexity arising from new technologies, particularly their interconnections, enabling dedicated planning, monitoring, and control of modern energy systems, raises the need for powerful concepts that provide detailed insights about the particular energy system and its components. In contrast to classical models that provide a generalized system description, this thesis investigates the gap between the physical and digital worlds, yielding a replica of the physical reality of energy systems and their components [...]

We applied and evaluated a physics-informed Deep Operator Network (PI-DeepONet) for modeling incompressible two-dimensional steady flows under varying Reynolds numbers and inlet boundary conditions. By combining the generalization capability of DeepONets with the physical constraints imposed by physics-informed neural networks (PINNs), the framework enables flow field prediction without relying on labeled data. [...]