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To date, new rechargeable and safe batteries are urgently being developed to address the energy and power demands of our society, ranging from mobile communication to electric and hybrid vehicles. Nevertheless, the further development of safe, high-performance all-solid-state-batteries requires the understanding and control of the relevant chemical reactions taking place during cycling. We use correlative microscopy and 3D tomography techniques to resolve the formation of solid-electrolyte interphases in energy storage devices with highly Li-ion conductive electrolytes, where we perform in-operando experiments to probe lithiation/delithiation behavior of all-solid-state Li-ion batteries.



The objective of this research is to develop a new class of materials with the tunable optical response, for applications ranging from metamaterials to catalysis. The modulation of the dielectric function of metallic structures can enable the unprecedented control of the surface plasmon propagation in thin-films and the plasmon resonances in nanostructures. However, the fixed optical properties of metals severely constraint their use in photonic devices that operate at optical frequencies. To overcome the limitations imposed by the pre-defined dielectric function of metals we develop alloys formed by Ag, Au, Cu, Mg and Al to access materials with tunable optical responses, not found in nature. We combined simulations/calculations and experiments to design, fabricate, and characterize the optical properties of alloyed thin films and nanostructures. The development of these optical materials has a potentially transformative effect on future nanophotonic devices by the complete control of their dielectric function and, therefore, superior optical performance.



We aim at advancing the state-of-knowledge of organic-inorganic materials for solar cells through experimental work assisted by machine learning (ML). The performance of novel high-efficiency and low-cost photovoltaics is currently limited by their long-term stability (under environmental conditions such as light, temperature, bias, oxygen, and humidity). We investigate halide perovskite materials and explore degradation behaviors using scanning probe and optical microscopy methods. We also examine the effects of mixed-site perovskites, where we add elements such as Cs and Rb to enhance solar cell stability and efficiency. ML algorithms such as neural networks help forecast solar cell performance over time and can extrapolate to unseen combinations of environmental conditions, enabling predictions hours, days, or even weeks into the future. By merging data science (ML) with materials science (experiments), we work at the forefront of a new, accelerated approach to scientific research.