Chemische Charakterisierung und Spurenanalytik
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Eingeladener Vortrag
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Die Probenpräparation ist ein wichtiger Schritt für die Qualität der Massenspektren in MALDI-TOF MS. Es werden Polymerproben, die lösungsmittelbasiert und lösungsmittelfrei präpariert worden sind, verglichen. Dabei werden die Intensitätsverhältnisse von ternären Mischungen von PEG-, PS- und PMMA-Polymeren beachtet.
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.
The structure and composition of ancient gold objects retain information about their long history of manufacture, from the exploitation of the ore to the finishing touches, as well as evidence of their use, deposition, and degradation. By developing an efficient analytical strategy, it is possible to retrieve that information. This chapter sets the necessary foundation 131to explore fully the analytical results presented in the following chapters of this volume. The techniques employed in the analyses of the Egyptian jewellery are described and the analytical parameters provided. For more established techniques, only brief introductions are presented, while more recent developments are presented in greater detail.
Enhancing efficiency at bamline: employing data science and machine learning for x-ray research
(2023)
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.
Mechanochemistry provides polymorphs that are difficult to obtain by conventional solution-based methods. Based on the nicotinamide and pimelic acid cocrystal, we demonstrated that the ball size, frequency, and temperature are essential parameters. Our results indicate that fine-tuning the energy input during a mechanochemical reaction can provide control over polymorphism.
With the online coupling (or hyphenation) of chemically-resolved spectroscopy detection with classical size-exclusion chromatography (SEC) it is possible to correlate polymer molecular size with chemical structure in a single in-situ or on-flow measurement. Medium-resolution 1H-NMR (60 MHz) spectroscopy (i.e., “desktop NMR”) and Fouriertransform Infrared Spectroscopy (FTIR) have both been developed as coupled methods (SEC-MR-NMR, SEC-FTIR) for the detection of polymer analytes by monitoring e.g. alkene or carbonyl functionality.1,2 Here, we have investigated the potential for the detection of polymer end groups, using a slightly higher field-strength spectrometer (80 MHz 1H Larmor frequency) and taking advantage of a columnless injection method that provides bulk spectra for reference. End-functionalized polymers were synthesized by reaction of PEG with toluene isocyanate, yielding ,-functionalized polymers with exactly 2 IR- and NMR-detectable functional groups per chain, as proven by MALD-TOF mass spectrometry. We have investigated the sensitivity (limit of detection) and spectral resolution for these functional groups in chloroform, THF, and water. Results have demonstrated a good feasibility for the simultaneous online detection of PEG backbone (M~4000 g/mol) and these end groups at the level of ca. 2 mol%.
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.
Our aim is to develop a simple and inexpensive method for full field X-ray fluorescence imaging.We combine an energydispersive array detector with a coded aperture to obtain high resolut ion images. To obtain the information from the recorded image a reconstruction step is necessary. The reconstruction methods we have developed, were tested on simulated data and then applied to experimental data. The first tests were carried out at the BAMline @BESSY II. This method enables the simultaneous detection of multiple elements,which is important e.g. in the field of catalysis.
Getting more efficient – The use of Bayesian optimization and Gaussian processes at the BAMline
(2022)
For more than 20 years, BAM is operating the BAMline at the synchrotron BESSY II in Berlin Adlershof. During this time, the complexity of the setup and the amount of data generated have multiplied. To increase the effectiveness and in preparation for BESSY III, algorithms from the field of machine learning are increasingly used.
After a short introduction to BO and GP, the first example is the automatic alignment of our double multilayer monochromator (DMM).
The second example is the optimization of measurement time in XRF scanning.
News from the BAMline
(2022)