6 Materialchemie
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Functional materials for energy conversion are important technology drivers needed for the implementation of low carbon energy. Therefore, researchers commonly focus on improving the intrinsic properties of a functional material. However, for applications, the extrinsic properties are at least as important as the intrinsic ones. Consequently, it is important to investigate and understand the external and internal structure of semi-finished products and especially defect dependent properties. The extrinsic properties may change during application and the life cycle of the material as well as through processing and molding steps. Our studies show how X-ray tomographic (XCT) investigations can contribute to structure investigations in composites and massive samples using the example of magnetic materials for energy conversion.
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.
The hexapeptide hIAPP22–27 (NFGAIL) is known as a crucial amyloid core sequence of the human islet amyloid polypeptide (hIAPP) whose aggregates can be used to better understand the wild‐type hIAPP′s toxicity to β‐cell death. In amyloid research, the role of hydrophobic and aromatic‐aromatic interactions as potential driving forces during the aggregation process is controversially discussed not only in case of NFGAIL, but also for amyloidogenic peptides in general. We have used halogenation of the aromatic residue as a strategy to modulate hydrophobic and aromatic‐aromatic interactions and prepared a library of NFGAIL variants containing fluorinated and iodinated phenylalanine analogues. We used thioflavin T staining, transmission electron microscopy (TEM) and small‐angle X‐ray scattering (SAXS) to study the impact of side‐chain halogenation on NFGAIL amyloid formation kinetics. Our data revealed a synergy between aggregation behavior and hydrophobicity of the phenylalanine residue. This study introduces systematic fluorination as a toolbox to further investigate the nature of the amyloid self‐assembly process.
This is the repository of all experimental raw data used in the Scientific Reports publication "Specific adsorption sites and conditions derived by thermal decomposition of activated carbons and adsorbed carbamazepine" by Daniel Dittmann, Paul Eisentraut, Caroline Goedecke, Yosri Wiesner, Martin Jekel, Aki Sebastian Ruhl, and Ulrike Braun.
It includes
- overview_measurements.xlsx and overview_measurements.ods containing a list of all TGA experiments (TGA, TGA-FTIR, TED-GC-MS, and ramp-kinetics)
- TED-GC-MS.zip containing gas chromatography-mass spectrometry experimtent files for the Chemstation and OpenChrom
- TGA.zip containing thermogravimetric analyses raw data on evolved gas analyses experiments (TGA-FTIR and TED-GC-MS)
- TGA_kinetics.zip containing thermogravimetric analyses raw data on decomposition kinetic experiments (ramp-kinetics)
- TGA-FTIR.zip containing Fourier-transform infrared spectroscopy series files for OMNIC
- XRF.zip containing x-ray flourescence data on elemental composition