Chemische Charakterisierung und Spurenanalytik
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Mechanochemistry is a fast and efficient method applicable for the synthesis of new organic, metal-organic, and inorganic compounds. The direct monitoring of milling reactions is still challenging. The underlying reaction mechanisms often remain unclear. In the last years, we have established a tandem in situ approach for investigating mechanochemical reactions using time-resolved in situ XRD coupled with Raman spectroscopy. A further development is the in situ coupling of synchrotron XRD, Raman spectroscopy, and thermography allowing the observation of mechanochemical reactions in real time. [5] Information on the crystalline, molecular, and temperature state of the materials during grinding could be collected. The chemical composition of the reaction mixture was found to be directly correlated with changes in the temperature profile of the reaction. Furthermore, the presented setup allows the detection of crystalline, amorphous, eutectic as well as liquid intermediates. Based on these data, metastable polymorphs of cocrystals and coordination polymorphs could be isolated and struc-turally characterized. A multi-step diffusion mechanism was identified for most systems. Crystalline phases were obtained intermediately, suggesting that the synthesis is following Ostwald’s rules of stages. The resulting deeper kinetic and thermodynamic understanding of milling processes is the key for future optimization of mechanochemical syntheses.
MALDI time-of-flight mass spectrometry (MALDI-TOF MS) has become a widely used tool for the classification of biological samples. The complex chemical composition of pollen grains leads to highly specific, fingerprint-like mass spectra, with respect to the pollen species. Beyond the species-specific composition, the variances in pollen chemistry can be hierarchically structured, including the level of different populations, of environmental conditions or different genotypes. We demonstrate here the sensitivity of MALDI-TOF MS regarding the adaption of the chemical composition of three Poaceae (grass) pollen for different populations of parent plants by analyzing the mass spectra with partial least squares discriminant analysis (PLS-DA) and principal component analysis (PCA). Thereby, variances in species, population and specific growth conditions of the plants were observed simultaneously. In particular, the chemical pattern revealed by the MALDI spectra enabled discrimination of the different populations of one species. Specifically, the role of environmental changes and their effect on the pollen chemistry of three different grass species is discussed. Analysis of the Group formation within the respective populations showed a varying influence of plant genotype on the classification, depending on the species, and permits conclusions regarding the respective rigidity or plasticity towards environmental changes.
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.
The comparison required the assignment of the mass fraction of folic acid present as the main component in the comparison sample. Performance in the comparison is representative of a laboratory's measurement capability for the purity assignment of organic compounds of medium structural complexity [molecular weight range 300–500] and high polarity (pKOW < −2).
Methods used by the eighteen participating NMIs or DIs were based on a mass balance (summation of impurities) or qNMR approach, or the combination of data obtained using both methods. The qNMR results tended to give slightly lower values for the content of folic acid, albeit with larger associated uncertainties, compared with the results obtained by mass balance procedures. Possible reasons for this divergence are discussed in the report, without reaching a definitive conclusion as to their origin.
The comparison demonstrates that for a structurally complex polar organic compound containing a high water content and presenting a number of additional analytical challenges, the assignment of the mass fraction content property value of the main component can reasonably be achieved with an associated relative standard uncertainty in the assigned value of 0.5%
The characterization of a catalyst often occurs by averaging over large areas of the catalyst material. On the other hand, optical probing is easily achieved at a resolution at the micrometer scale, specifically in microspectroscopy. Here, using surface-enhanced Raman scattering (SERS) mapping of larger areas with micrometer-sized spots that contain tens to hundreds of supported gold nanoparticles each, the photoinduced dimerization of p-aminothiophenol (PATP) to 4,4′-dimercaptoazobenzene (DMAB) was monitored. The mapping data reveal an inhomogeneous distribution of catalytic activity in the plasmon-catalyzed reaction in spite of a very homogeneous plasmonic enhancement of the optical signals in SERS. The results lead to the conclusion that only a fraction of the nanostructures may be responsible for a high catalytic activity. The high spot-to-spot variation in catalytic activity is also demonstrated for DMAB formation by the plasmon-catalyzed reduction from p-nitrothiophenol (PNTP) and confirms that an improvement of the accuracy and reproducibility in the characterization of catalytic reactions can be achieved by microspectroscopic probing of many positions. Using SERS micromapping during the incubation of PATP, we demonstrate that the reaction occurs during the incubation process and is influenced by different parameters, leading to the conclusion of dimerization in a gold-catalyzed, nonphotochemical reaction as an alternative to the plasmon-catalyzed process. The results have implications for the future characterization of new catalyst materials as well as for optical sensing using plasmonic materials.
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 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 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.