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Total Reflection X-ray Fluorescence (TXRF) is a small footprint, ressource efficient micro-analytical tool for trace elemental determination. However, depending on the matrix TXRF is also challenging in several ways: the preparation of a representative aliquot maybe difficult for slurries, shading effects and matrix effects may occur and the applicability of an IS maybe hampered by interferences (fitting may be impaired as well) or inhomogeneities. It is therefore crucial to understand and if applicable mitigate the influence of the before mentioned phenomena. We have used the small volume approach using pL droplets to study shading in TXRF previously.Using this approach thin specimens in a favorable geometry are prepared with a well defined morphology; this way minimizing matrix effects and shading. To be used as standard it is also necessary to determine the delivered elemental amounts. Here we will present on the performance of a commercial ink-jet printer cartridge to deliver defined volumes and elemental amounts. The microscopic specimens obtained have been successfully applied to determine relative sensitivities in TXRF and prepare references to study coded apertures in grazing incidence full field micro-XRF.The homogeneous lateral distribution of analyte and IS may be probed by micro-XRF, to obtain information on alikeness of analyte and IS. To study the alikeness of the in depth distribution (film-like or particle-like) angle scans can deliver valuable data. Here we present first results on angle scans using a prototype GIXRF set up and a commercial TXRF instrument.
Polymer electrolyte membranes (PEM) are polymers which act as separator in an electrochemical cell and allow ionic charge flow to close the circuit. Widely used membranes are perfluorinated sulfonic-acid (PFSA) ionomers (e.g. Nafion™), a class of ion-conducting polymers with remarkable ion conductivity and chemical-mechanical stability. A nanoscopic water system around the sulfonic acid capped side-chains of the otherwise hydrophobic polymer allows the ion transport. Only approx. 14 molecules of water per sulfonic group are present in the fully hydrated state. In vanadium redox flow batteries (VRFB) ideally only protons are transported. The weak ion selectivity of Nafion™ is the main cause for the transportation of vanadium, briefly vanadium crossover, in VRFBs a system we have investigated more closely, lately. The consequences of crossover are a concentration imbalance and a self-discharge of the battery, which leads to a decrease of the capacity. The development of efficient energy storage systems is crucial for the transformation towards a renewable energy based economy. The VRFB has a great potential as a commercial electrochemical energy storage system due to properties including, but not limited to, no cross-contamination, a long cycle-life and a theoretically unlimited capacity. VRFB consists of two half-cells, which are linked to electrolyte tanks and separated by a membrane. The membrane plays a major role in overall cell performance. So far, vanadium transportation models, which include diffusion, migration, electroosmotic convection and pressure gradients, are inconsistent. There is no agreement in the literature on the diffusion coefficients of vanadium species (e.g. published diffusion coefficients of V2+ are located between 3.13·10-12 m²s-1 and 9.44·10-12 m²s-1) indicate that thevanadium crossover is not well understood and there is a lack on a more fundamental level. Since the membrane transport is the rate-determining step of the crossover as well as of the proton exchange it is extremely important to understand these phenomena on a fundamental level. This will eventually allow us to design better membranes. Alternative materials need to show equivalent performance compared with Nafion™ and ideally be more selective regarding ion cross-membrane transport.There are several options to approach the chemistry i.e. the interaction of dissolved and bound ions inside the confined water body of ionomeric membranes. Vanadium ions are a versatile model as UV/VIS data can be used to distinguish between the 5 species V2+, V3+, VO2+, VO2+ and V2O33+ of the electrolyte. Infrared spectroscopy has been applied to study the interaction of sulfonic groups and the ions. Molecular dynamic modeling is another approach to study the distribution and distances of ions. We now introduce X-ray absorption near edge structure spectroscopy (XANES) to study species and species changes inside Nafion™ and a novel membrane based on poly(1,1-difluoroethylene) (PVDF). We evaluated the methods and investigated the influence of irradiation, temperature and hydration on the measurements.
Vanadium redoxflow batteries (VRFB)are currently one of the most promising candidates for stationary energys torage.For large scale applications the ion conducting membranes currently in use need to be improved. Ideally,they need to become more cost efficient and selective regarding the vanadium crossover.For a better understanding of the vanadium crossover, the development of reliable analytical methods and procedures, that elucidate uptake and transport of vanadium ions in the membrane, is necessary. First, we present the uptake of V2+,V3+,VO2+, VO2+ and V2O33+ in Nafion™ and in a novel membrane based onpoly(1,1-difluoroethylene)(PVDF). In preliminary discharge/charge experiments the ETFE-based membrane, the precursor of PVDF-based membrane, performed comparable to Nafion™. The methods of choice for speciation are UV/Vis and X-ray absorption near edge structure spectroscopy (XANES). According to the results, V2O33+, formed from VO2+ and VO2+, diffuses also into the membrane. In present models, the diffusion of V2O33+ is neglected. In addition,we study whether reactions could take place inside the membranes’ nanoscopic water body using XANES. Exposing Nafion™ from one site with V3+a nd from the other site with VO2+ realized the experiment. The results verified that VO2+ was formed inside the membrane. However,in present models reactions inside the membrane are neglected, too.
Various applications for artificial intelligence in the context of spectroscopy will be presented. in particular, examples from BAMline will be presented. After a short introduction to synchrotron radiation, artificial intelligence algorithms for the quantification of X-ray fluorescence measurement are discussed. In the second example, information retrieval by natural language processing is discussed.
Synchrotron radiation sources with their unique properties in terms of intensity, polarization and adjustability offer a wide range of possibilities in materials research. A basic introduction about the creation and special properties of synchrotron radiation will be given. Examples of current work at BAMline, the high-energy measuring facility of the Federal Institute for Materials Research and Testing at the synchrotron BESSY, are used to illustrate the possibilities and limitations of existing measuring methods.
In this talk, the features of X-ray fluorescence analysis with synchrotron radiation will be presented. First, the basics of the origin of synchrotron radiation and X-ray fluorescence analysis and the experimental setup will be discussed. Then, examples of trace element detection, micrometer resolution, and application of the X-ray color camera will be shown.
Gold ist eines der sieben schon im Altertum bekannten Metalle und wurde wg. seines Glanzes und seiner Seltenheit von alters her als Tauschmittel und zur Herstellung von Schmuck benutzt. Außerdem ist es einfach bearbeitbar und weitestgehend gegen chemische Einflüsse resistent. Die Untersuchungen von Gold mit synchrotronstrahlungsangeregter Röntgenfluoreszenzanalyse sind zerstörungsfrei und geben Auskunft über die in der untersuchten Probe vorhandenen chemischen Elemente. Bei den hier vorgestellten Untersuchungen an der BAMline stehen Fragestellungen wie Herkunft, Herstellungsverfahren und Zusammengehörigkeit von Goldfunden im Vordergrund. Die verschiedenen Fragestellungen werden an einer Reihe von Beispielen erläutert die vom Wikingerschatz aus Hiddensee über die Himmelsscheibe von Nebra bis hin zu Funden aus Ägypten langen. Der Fund von Bernstorf wird ausführlich diskutiert.
Various applications for artificial intelligence in the context of spectroscopy will be presented. in particular, examples from bamline will be featured. After a short introduction to synchrotron radiation, artificial intelligence algorithms for the quantification of X-ray fluorescence measurement are discussed. In the second example, information retrieval by natural language processing is reviewed. As a last example the reconstruction of measurements with the X-ray color camera and coded apertures is presented.
Trace elements are chemical elements whose concentration in a material is very low. The exact definition depends on the application and varies for example between 100 micrograms per gram in analytical chemistry and 1000 micrograms per gram in geology. The ability to detect trace elements fast and quantitatively is of great importance in many areas of science and technology. With its high brilliance and flexibility in the excitation conditions, synchrotron radiation is an ideal tool for detecting traces even in small sample quantities. In this contribution I will report about the use of X-ray fluorescence(XRF)for qualitative and quantitative element sensitiveanalysis. In addition to the fundamentals of XRF and its quantification methods, the advantages and problems of different geometries like e.g. microXRF, Total Reflection X-ray Fluorescence (TXRF)or Double Dispersive XRF (D²XRF) will be discussed. Practical examples from BAMline from the research fields of medicine, geology and archaeometry will complete the lecture.
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.