Chemische Charakterisierung und Spurenanalytik
Filtern
Dokumenttyp
- Vortrag (19)
- Zeitschriftenartikel (10)
- Posterpräsentation (3)
- Buchkapitel (1)
Schlagworte
- Synchrotron (33) (entfernen)
Organisationseinheit der BAM
- 6 Materialchemie (33)
- 6.3 Strukturanalytik (33)
Eingeladener Vortrag
- nein (19)
X-ray absorption fine structure spectroscopy (XAFS) is a frequently employed technique in order to investigate structural composition and change of chemical compounds such as catalytic species. These structural properties are essential (i) to understand underlying reaction mechanism and (ii) to further improve the design of efficient catalysts.
This investigation is based on a newly developed XAFS setup comprising both time- and lateral-resolved XAFS information simultaneously in a single-shot (S2XAFS). The primary broadband beam is generated by a filter/X-ray-mirror combination (bandpass). The transmitted beam through the sample is diffracted by a convexly bent Si (111) crystal, producing a divergent beam. This, in turn, is collected by an area sensitive detector with a theta to 2 theta geometry. This facile, stable and scanningless setup was tested at the BAMline @ BESSY-II (Berlin, Germany).
This contribution focuses on further experimental optimizations allowing the characterization of supported vanadium oxide (VOx) based catalysts at the lower hard X-ray regime (5 to 6 keV). First S2XAFS measurements of these catalysts are presented herein. Supported VOx catalysts show promising results in the oxidation of methane to formaldehyde.
S2XAFS allows determining the structural composition of the metal (i.e. vanadium) based on a fast and smart setup. It is therefore an ideal tool to identify crucial roles of chemical compounds in catalytic reactions.
To better study the impact of nanoparticles on both in vitro and in vivo models, tissue distribution and cellular doses need to be described more closely. Here silver nanoparticles were visualized in alveolar macrophages by means of synchrotron radiation micro X-ray fluorescence spectroscopy (SR-μXRF) with high spatial resolution of 3 × 3 μm2. For the spatial allocation of silver signals to cells and tissue structures, additional elemental labeling was carried out by staining with eosin, which binds to protein and can be detected as bromine signal with SR-μXRF. The method was compatible with immunostaining of macrophage antigens. We found that the silver distribution obtained with SR-μXRF was largely congruent with distribution maps from a subsequent laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) of the same tissue sites. The study shows a predominant, though not exclusive uptake of silver into alveolar macrophages in the rat lung, which can be modeled by a similar uptake in cultured alveolar macrophages. Advantages and limitations of the different strategies for measuring nanoparticle uptake at the single cell level are discussed.
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.
Quantitative Analysis and 2D/3D Elemental Imaging of Cocoa Beans Using X‑ray Fluorescence Techniques
(2023)
As an important raw material for the confectionery industry, the cocoa bean (Theobroma cacao L.) has to meet certain legal requirements in terms of food safety and maximum contaminant levels in order to enter the cocoa market. Understanding the enrichment and distribution of essential minerals but also toxic metals is of utmost importance for improving the nutritional quality of this economically important raw food material. We present three X-ray fluorescence (XRF) techniques for elemental bio-imaging of intact cocoa beans and one additional XRF technique for quantitative analysis of cocoa pellets. The interrelation of all the methods presented gives a detailed picture of the content and 3D-resolved distribution of elements in complete cocoa beans for the first time.
News from the BAMline
(2022)
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. It will be shown how the formation of corrosion layers can be tracked, how the abrasion of implants leads to the introduction of heavy metals into the surrounding bone matrix and how the detection of smallest impurities in gold is possible by optimizing the measuring conditions. Finally, an outlook at the hardware and software developments to be expected in the coming years is given.
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.
In this talk I’ll describe the use of artificial neural networks (ANN) for quantifying X-ray fluorescence (XRF) measurements. The main idea of this talk is to give an overview of the process needed to generate a model that can then be applied to a specific problem.
In XRF, a sample is excited with X-rays and the resulting characteristic radiation is detected to determine elements quantitatively and qualitatively. This is traditionally done in several time-consuming steps. I’ll show the possibilities and problems of using a neural network to realise a "one-click" quantification. This includes generating training data using Monte Carlo simulation and augmenting the existing data set with an ANN to generate more data. The search for the optimal hyperparameters, manually and automatically, is also described. For the case presented, we were able to train a network with a mean absolute error of 0.1% by weight for the synthetic data and 0.7% by weight for a set of experimental data obtained with certified reference materials.
In X-ray fluorescence (XRF), a sample is excited with X-rays, and the resulting characteristic radiation is detected to detect elements quantitatively and qualitatively. Quantification is traditionally done in several steps:
1. Normalization of the data
2. Determination of the existing elements
3. Fit of the measured spectrum
4. Calculation of concentrations with fundamental parameters / MC simulations / standard based
The problem with standard based procedures is the availability of corresponding standards. The problem with the calculations is that the measured intensities for XRF measurements are matrix-dependent. Calculations must, therefore, be performed iteratively (= time consuming) in order to determine the chemical composition.
First experiments with gold samples have shown the feasibility of machine learning based quantification in principle. A large number of compositions were simulated (> 10000) and analyzed with a deep learning network. For first experiments, an ANN (Artificial Neural Network) with 3 hidden layers and 33x33x33 neurons was used. This network learned the mapping of spectra to concentrations using supervised learning by multidimensional regression. The input layer was formed by the normalized spectrum, and the output layer directly yielded the searched values. The applicability for real samples was shown by measurements on certified reference materials.
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.