TY - CONF A1 - Radtke, Martin A1 - Buzanich, Ana A1 - Cakir, C.T. T1 - Enhancing efficiency at bamline: employing data science and machine learning for x-ray research N2 - This talk discusses how data science and machine learning techniques are being applied at the BAM Federal Institute for Materials Research and Testing to enhance efficiency and automation at the BAMLine synchrotron facility. The methods presented include Gaussian processes and Bayesian optimization for beamline adjustment and optimization of X-ray measurements. These statistical techniques allow automated alignment of beamline components and active learning scanning to reduce measurement time. Additional machine learning methods covered are neural networks for quantification of X-ray fluorescence (XRF) data and decoding coded apertures. T2 - 17th International Work-Conference on Artificial Neural Networks (IWANN2023) CY - Ponta Delgada, Portugal DA - 19.06.2023 KW - Bayesian Optimization KW - Gaussian Process KW - BAMline PY - 2023 AN - OPUS4-58605 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radtke, Martin A1 - Guilherme Buzanich, Ana A1 - Cakir, C.T. A1 - Yusenko, Kirill A1 - Emmerling, Franziska T1 - Insights into materials with hard x-rays: capabilities of the bamline N2 - This contribution provides an overview of the BAMline synchrotron radiation beamline, which specializes in hard X-ray spectroscopy techniques for materials research. The BAMline offers X-ray absorption spectroscopy (XAS), x-ray fluorescence spectroscopy (XRF), and tomography to study materials' electronic structure, chemical composition, and structure. Key capabilities include standard and dispersive XAS for electronic structure, micro-XRF for elemental mapping, coded aperture imaging, and depth-resolved grazing exit XAS. The BAMline enables in situ characterization during materials synthesis and functions for energy, catalysis, corrosion, biology, and cultural heritage applications. Ongoing developments like the implementation of machine learning techniques for experiment optimization and data analysis will be discussed. For instance, Bayesian optimization is being used to improve beamline alignment and scanning. An outlook to the future, where the BAMline will continue pioneering dynamic and multi-scale characterization, aided by advanced data science methods, to provide unique insights into materials research, will be given. T2 - μ-XRF at Elettra 2.0: challenges and opportunities CY - Trieste, Italy DA - 11.09.2023 KW - Synchrotron KW - XRF KW - XANES KW - Bayes PY - 2023 AN - OPUS4-58607 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Peters, Stefan A1 - Kunkel, Benny A1 - Cakir, Cafer Tufan A1 - Kabelitz, Anke A1 - Witte, Steffen A1 - Bernstein, Thomas A1 - Bartling, Stephan A1 - Radtke, Martin A1 - Emmerling, Franziska A1 - Abdel-Mageed, Ali Mohamed A1 - Wohlrab, Sebastian A1 - Guilherme Buzanich, Ana T1 - Time-, space- and energy-resolved in situ characterization of catalysts by X-ray absorption spectroscopy JF - Chemical Communications N2 - A novel setup for dispersive X-ray absorption spectroscopy (XAS) with simultaneous resolution of space, time and energy for in situ characterization of solid materials is demonstrated. KW - Dispersive XAS KW - Catalysis KW - In situ KW - Structure analysis PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-584924 DO - https://doi.org/10.1039/d3cc03277a SN - 1359-7345 SP - 1 EP - 4 PB - Royal Society of Chemistry (RSC) AN - OPUS4-58492 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Guerra, M.F. A1 - Neri, E. A1 - Radtke, Martin T1 - Gold leaf tesserae: tracing the origins of gold using synchrotron-based techniques JF - The European Physical Journal Plus N2 - To gain insight into the possible origin of the gold used in the production of tesserae containing gold leaf less than 0.5 μm thick placed between two layers of glass, we propose a non-destructive synchrotron radiation (SR) XRF protocol based on sequential analysis under optimised analytical conditions. Using this protocol, trace element analysis is achieved with detection limits of 1–6 mg/kg. As Pt and Au have adjacent fluorescence energies, we tested the most challenging situation, when Pt is present in very low concentrations in gold. Data obtained by double-dispersive XRF (D2XRF) and μXRF for fourth–ninth-century mosaics decorating nine Eastern and Western religious buildings show that the Eastern and Western tesserae are made from different alloys. However, these alloys are identical to those used to make gold leaf for gilding, because plastic deformation requires the use of gold alloys with high ductility and malleability. Although trace element composition of gold used in the concerned period is only available for coins, by comparing the amounts of Pt contained in the tesserae and in the coins we show that Roman tesserae are made from Roman gold, as described in the documentary sources. We observe for the Byzantine period the use of a Byzantine gold and of gold supposedly from different stages of recycling, and we suggest the use of Umayyad and Abbasid gold for the production of Islamic tesserae. KW - Gold KW - XRF KW - Synchrotron KW - BAMline KW - D2XRF KW - Tesserae PY - 2023 DO - https://doi.org/10.1140/epjp/s13360-022-03638-y SN - 2190-5444 VL - 138 IS - 2 SP - 1 EP - 15 AN - OPUS4-57208 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Cakir, Cafer Tufan A1 - Bogoclu, Can A1 - Emmerling, Franziska A1 - Streli, Christina A1 - Guilherme Buzanich, Ana A1 - Radtke, Martin T1 - Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach JF - Machine Learning: Science and Technology N2 - In materials science, traditional techniques for analyzing layered structures are essential for obtaining information about local structure, electronic properties and chemical states. While valuable, these methods often require high vacuum environments and have limited depth profiling capabilities. The grazing exit x-ray absorption near-edge structure (GE-XANES) technique addresses these limitations by providing depth-resolved insight at ambient conditions, facilitating in situ material analysis without special sample preparation. However, GE-XANES is limited by long data acquisition times, which hinders its practicality for various applications. To overcome this, we have incorporated Bayesian optimization (BO) into the GE-XANES data acquisition process. This innovative approach potentially reduces measurement time by a factor of 50. We have used a standard GE-XANES experiment, which serve as reference, to validate the effectiveness and accuracy of the BO-informed experimental setup. Our results show that this optimized approach maintains data quality while significantly improving efficiency, making GE-XANES more accessible to a wider range of materials science applications. KW - Machine Learning KW - GE-XANES KW - Bayesian Optimization PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-603955 DO - https://doi.org/10.1088/2632-2153/ad4253 VL - 5 IS - 2 SP - 1 EP - 12 PB - IOP Publishing AN - OPUS4-60395 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -