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Title [sv]
Mjukröntgenspektroskopi för ultra-utspädda in-operandi katalysatorer: Medels maskininlärning för spektrometerutveckling
Title [en]
Soft x-ray spectroscopy for ultra-dilute in operando catalysts: Using machine learning for spectrometer developments
Abstract [en]
New forms of energy from clean sources and sustainable production are needed on the global scale. A promising route makes use of sunlight towards scalable approaches to renewable solar fuels. Artificial inorganic molecular catalysts and nanoparticles for the necessary catalytic reactions have been found and metalloproteins often serve as examples for understanding catalytic function. While the structures of these materials are often known down to the atomic scale and their function can be measured and tuned, information on how catalytic activity is determined at the orbital level is largely missing. The purpose of this project is to remove this gap and to develop the instrumentation necessary to enable systematic soft x-ray spectroscopic studies of homogeneous catalysts at in operandoconditions. The aim is to realize a new approach for transition-metal L-edge absorption spectroscopy with unprecedented sensitivity. We will use machine learning techniques to develop and optimize a new soft x-ray spectrometer and we will deliver a set up for the described experiments. We will demonstrate the new capabilities of our approach with applications to functional inorganic molecular catalysts, nanoparticles and metalloproteins in solution. Our project addresses fundamental questions in materials science with societal relevance, train the next generation of researchers and enable new ways of using the x-ray facilities BESSY II, MAX IV and the European XFEL in a complementary way.
Principal InvestigatorWernet, Philippe
Coordinating organisation
Uppsala University
Funder
Period
2020-01-01 － 2023-12-31
National Category
Atom and Molecular Physics and OpticsAccelerator Physics and InstrumentationMaterials Chemistry
Identifiers
DiVA, id: project:6495Project, id: 2019-06093_VR
v. 2.47.0
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