TY - JOUR A1 - Fittschen, U.E.A. A1 - Möckel, R. A1 - Schreiner, M. A1 - klinger, M. A1 - Radtke, Martin A1 - Meyer, B. A1 - Guhl, S. A1 - Renno, A. A1 - Godinho, J. A1 - Gloaguen, R. A1 - Gutzmer, J. T1 - Bundling analytical capacities to understand phase formation in recycling of functional materials JF - Materials Science Forum N2 - Transitioning from combustion engine-driven transportation to e-mobility demands a paradigm shift – from a system geared to maximize energy efficiency (i.e. fuel consumption) to a system that may be constrained by the availability of high technology (critical) metals required for electrical energy storage systems and drives. In the wake of these developments efforts in securing new resources of these metals from recycling of end-of-life products are increasing steadily. Recycling of Li-Ion batteries has recently been evaluated. The results pinpoint to a critical need for understanding slag Formation and its dependence on metal components like Mn under extreme conditions. This will allow researchers to predict optimal Operation setting and to react quickly to changing market demands (which may be Li or Co at one point but may also shift to Ni or rare earth elements (REE)). The long-term goal is to control the formation of specific phases in slags allowing for a Maximum yield of elements of interest and optimal recovery in the separation processes that follows. The combination of data on the physical micro structure and local chemistry of the multi-Phase products during and after processing will help to understand and derive thermodynamic and kinetic data on its formation. In this paper we are giving an overview on the analytical challenges and approaches to provide robust data on local element concentration and species (especially Mn which is a common component of next generation Li-ion batteries cathodes), spanning the dimensions from the nanometer scale to the bulk material. The complementary interactions of X-rays and electrons make them ideal probes to collect Interface and “in-depth” information. Before- and -after studies as well as in situ structural changes and Phase (trans)formation, changes in elemental and elemental species (e.g. oxidation state) distribution may be tracked by X-ray diffraction (XRD), X-ray fluorescence microscopy and X-ray Absorption spectroscopy. The application of such advanced analytical tools will not only provide essential clues during early lab-based experiments towards the development of new recycling technologies, but may also be deployed for on-line and in-line monitoring of industrial processes. KW - Synchrotron KW - XANES KW - Slags KW - Battery PY - 2019 DO - https://doi.org/10.4028/www.scientific.net/MSF.959.183 SN - 1662-9752 VL - 959 SP - 183 EP - 190 PB - Trans Tech Publ. AN - OPUS4-48900 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kulow, Anicó A1 - de Oliveira Guilherme Buzanich, Ana A1 - Reinholz, Uwe A1 - Emmerling, Franziska A1 - Hampel, S. A1 - Fittschen, UEA A1 - Streli, C. A1 - Radtke, Martin T1 - Reconstruction for coded aperture full-field x-ray fluorescence imaging JF - Advances in x-ray analysis N2 - X-ray fluorescence imaging is a well-established tool in materials characterization. In this work, we present the adaption of coded aperture imaging to full-field X-ray fluorescence imaging at the synchrotron. Coded aperture imaging has its origins in astrophysics, and has several advantages: Coded apertures are relatively easy to fabricate, achromatic, allow a high photon throughput, and high angular acceptance. Coded aperture imaging is a two-step-process, consisting of the measurement process and a reconstruction step. Different programs have been written, for the raytracing/forward projection and the reconstruction. Experiments with coded aperture in combination with a Color X-ray Camera and an energy-dispersive area detector, have been conducted at the BAMline. Measured samples were successfully reconstructed, and gave a 9.1-fold increase in count rate compared to a polycapillary optic. KW - Synchrotron KW - BAMline KW - Coded Aperture PY - 2022 SN - 1097-0002 VL - 65 SP - 57 EP - 70 PB - Cambridge University Press CY - Cambridge AN - OPUS4-56350 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lutz, C. A1 - Hampel, S. A1 - Beuermann, S. A1 - Turek, T. A1 - Kunz, U. A1 - de Oliveira Guilherme Buzanich, Ana A1 - Radtke, Martin A1 - Fittschen, U. E. A. T1 - Investigation on Vanadium Species Distribution in Nafion™ 117 after Cyclization in a Vanadium Redox Flow Battery JF - Advances in X-Ray Analysis N2 - The vanadium redox flow battery (VRFB) is currently a potential candidate for stationary energy storage. A major challenge is the unintended vanadium transport through the separator, which results in a fade of capacity. To overcome this issue, it is necessary to understand the transport processes in the membrane on a more fundamental level. In this work, the vanadium species distribution in Nafion™ 117 after cyclization was investigated. Two membranes, one from a charged VRFB and another from a discharged VRFB, were analyzed using ultraviolet–visible spectroscopy (UV/VIS) and X-ray absorption near edge structure spectroscopy (XANES). Little difference between the two membranes was recognizable according to the UV/VIS results. In comparison, the XANES results showed that the membrane from the charged VRFB contains more V3+ than VO2+, whereas for the discharged case, more VO2+ is present in the membrane. KW - Synchrotron KW - BAMline KW - XANES KW - Vanadium redox flow battery PY - 2021 VL - 64 SP - 1 EP - 8 AN - OPUS4-54144 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Radtke, Martin A1 - Rakotondrajoa, A. T1 - Machine learning based quantification of synchrotron radiation-induced X-ray fluorescence measurements - a case study JF - Machine Learning: Science and Technology N2 - In this work, we describe the use of artificial neural networks for the quantification of X-ray fluorescence measurements. The training data were generated using Monte Carlo simulation, which avoided the use of adapted reference materials. The extension of the available data set by means of an ANN to generate additional data was demonstrated. Particular emphasis was put on the comparability of simulated and experimental data and how the influence of deviations can be reduced. The search for the optimal hyperparameter, manual and automatic, is also described. For the presented case, we were able to train a network with a mean absolute error of 0.1 weight percent for the synthetic data and 0.7 weight percent for a set of experimental data obtained with certified reference materials. KW - Machine learning KW - BAMline KW - XRF KW - Synchrotron KW - Neural network PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-519008 DO - https://doi.org/10.1088/2632-2153/abc9fb SP - 1 EP - 16 PB - IOP Publishing AN - OPUS4-51900 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -