Analytische Chemie
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The small-pore framework sodium stannosilicate AV-10, chemical composition Na2SnSi3O9⋅2H2O and known crystallographic structure, was synthesized by hydrothermal crystallization. This stannosilicate is built up of a three-dimensional network of corner-shared SiO4 tetrahedra and SnO6 octahedra. The SnO6 sites are linked to six SiO4 tetrahedra (Sn(6Si)) while each of the two crystallographically different SiO4 units are connected to two SnO6 and SiO4 units (Si(2Si,2Sn)). This material was used as model compound for developing a solid-state MAS NMR strategy aimed on the challenges and possibilities for structural studies, particularly considering the short and medium range order to verify the connectivity of SiO4 and SnO6 of such compounds despite the low natural abundances of 4.68% for 29Si and 8.59% for 119Sn nuclei as a real challenge. 29Si{119Sn} and 119Sn{29Si} REDOR (Rotational-Echo Double-Resonance) NMR measurements after 1H cross-polarization (CP) were carried out. The REDOR curves show a significant change after the “normal” quadratic short time evolution from which both (i) the shortest internuclear 29Si – 119Sn distances (and vice versa) and (ii) the number of corner-sharing SiO4 tetrahedra around the SnO6 octahedra (and vice versa) can be obtained. Based on these data, optimized 29Si {119Sn} and 119Sn{29Si} REPT-HMQC (Recoupled Polarization Transfer-Heteronuclear Multiple-Quantum Correlation, again after 1H CP) experiments were implemented, which directly show those heterogroup connectivity as correlation peaks in a 2D spectrum. This information was also obtained using 2D29Si{119Sn}-J-Coupling NMR experiments. Furthermore, 2D29Si INADEQUATE NMR experiments are also feasible, showing the connectivity of SiO4 tetrahedra. The combination of REDOR, REPT-HMQC, J-Coupling and INADEQUATE experiments yielded a complete analysis of the short and medium range structure of this microporous stannosilicate, in agreement with the previously published structure obtained Ab Initio from powder X-Ray diffraction data (XRD).
Terrestrial mud volcanoes are unique structures driven by tectonic pressure and fluids from the deep subsurface. These structures are mainly found in active tectonic zones, such as the area near the Los Bajos Fault in Trinidad. Here we report a chemical and microbiological characterization of three mud volcanoes, which included analyses of multiple liquid and solid samples from the mud volcanoes. Our study confirms previous suggestions that at least some of the mud volcano fluids are a mixture of deeper salt-rich water and surficial/precipitation water. No apparent water quality differences were found between sampling sites north and south of a major geological fault line. Microbiological analyses revealed diverse communities, both aerobic and anaerobic, including sulfate reducers, methanogens, carbon dioxide fixing and denitrifying bacteria. Several identified species were halophilic and likely derived from the deeper salt-rich subsurface water, while we also cultivated pathogenic species from the Vibrionaceae, Enterobacteriaceae, Shewanellaceae, and Clostridiaceae. These microorganisms were likely introduced into the mud volcano fluids both from surface water or shallow ground-water, and perhaps to a more minor degree by rain water. The identified pathogens are a major health concern that needs to be addressed.
Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.
The very strong Lewis acid aluminium chlorofluo-ride (ACF) was loaded with anhydrous HF. The interactionbetween the surface of the catalyst and HF was investigatedusing a variety of characterization methods, which revealed he formation of polyfluorides. Moreover, the reactivity ofthe HF-loaded ACF towards the hydrofluorination of alkyneswas studied.
The presence of microplastic (MP) particles in aquatic environments raised concern About possible enrichment of organic and inorganic pollutants due to their specific surface and chemical properties. In particular the role of metals within this context is still poorly understood. Therefore, the aim of this work was to develop a fully validated acid digestion protocol for metal analysis in different polymers, which is a prerequisite to study such interactions.
The proposed digestion protocol was validated using six different certified reference materials in the microplastic size range consisting of polyethylene, polypropylene, acrylonitrile butadiene styrene and polyvinyl chloride. As ICP-MS/MS enabled time-efficient, sensitive and robust analysis of 56 metals in one measurement, the method was suitable to provide mass fractions for a multitude of other elements beside the certified ones (As, Cd, Cr, Hg, Pb, Sb, Sn and Zn). Three different microwaves, different acid mixtures as well as different temperatures in combination with different hold times were tested for optimization purposes.
With the exception of Cr in acrylonitrile butadiene styrene, recovery rates obtained using the optimized protocol for all six certified reference materials fell within a range from 95.9% ± 2.7% to 112% ± 7%. Subsequent optimization further enhanced both precision and recoveries ranging from 103% ± 5% to 107 ± 4% (U; k = 2 (n = 3)) for all certified metals (incl. Cr) in acrylonitrile butadiene styrene. The results clearly show the analytical challenges that come along with metal analysis in chemically resistant plastics. Addressing specific analysis Tools for different sorption scenarios and processes as well as the underlying kinetics was beyond this study’s scope. However, the future application of the two recommended thoroughly validated total acid digestion protocols as a first step in the direction of harmonization of metal analysis in/on MP will enhance the significance and comparability of the generated data. It will contribute to a better understanding of the role of MP as vector for trace metals in the environment.
Small-area/spot photoelectron spectroscopy (SAXPS) is a powerful tool for the investigation of small surface features like microstructures of electronic devices, sensors or other functional surfaces, and so forth. For evaluating the quality of such microstructures, it is often crucial to know whether a small signal in a spectrum is an unwanted contamination of the field of view (FoV), defined by the instrument settings, or it originated from outside. To address this issue, the d80/20 parameter of a line scan across a chemical edge is often used. However, the typical d80/20 parameter does not give information on contributions from the long tails of the X-ray beam intensity distribution or the electron-optical system as defined by apertures. In the VAMAS TWA2 A22 project “Applying planar, patterned, multi-metallic samples to assess the impact of analysis area in surface-chemical analysis,” new test specimen was developed and tested. The here presented testing material consists of a silicon wafer substrate with an Au-film and embedded Cr circular and square spots with decreasing dimensions from 200 μm down to 5 μm. The spot sizes are traceable to the length unit due to size measurements with a metrological SEM. For the evaluation of the FoV, we determined the Au4f intensities measured with the center of the FoV aligned with the center of the spot and normalized to the Au4f intensity determined on the Au-film. With this test specimen, it was possible to characterize, as an example, the FoV of a Kratos AXIS Ultra DLD XPS instrument.
An extensive set of information about the diffracting volume is carried by EBSD patterns: the crystal lattice, the reciprocal lattice, the crystal structure, the crystal symmetry, the mean periodic number of the diffracting phase, the source point from where it has been projected (projection centre), the crystal orientation, the sample topography (local tilt), the (preparation) quality of defect density of the crystal, and possible pattern overlaps. Some of this information is used regularly in conventional EBSD analyses software while others are still waiting for a more widespread application. Despite the wealth of information available, the accuracy and precision of the data that are presently extracted from conventional EBSD patterns are often well below the actual physical limits. Using a selection of example applications, we will demonstrate the gain in angular resolution possible using relatively low-resolution patterns of approximately 20k pixels in combination with pattern matching (PM) approaches. In this way, fine details in a microstructure can be revealed which would otherwise be hidden in the orientation noise.
The quantitative analysis of microstructural features is a key to understanding the micromechanical behavior of metal matrix composites (MMCs), which is a premise for their use in practice. Herein, a 3D microstructural characterization of a five-phase MMC is performed by synchrotron X-ray computed tomography (SXCT). A workflow for advanced deep learning-based segmentation of all individual phases in SXCT data is shown using a fully convolutional neural network with U-net architecture. High segmentation accuracy is achieved with a small amount of training data. This enables extracting unprecedently precise microstructural parameters (e.g., volume fractions and particle shapes) to be input, e.g., in micromechanical models.
Nickel nanoparticles are an active research area due to their multiple applications as catalysts in different processes. A variety of preparation techniques have been reported for the synthesis of these nanoparticles, including solvothermal, microwave-assisted, and emulsion techniques. The well-studied solvothermal oleylamine synthesis route comes with the drawback of needing standard air-free techniques and often space-consuming glassware. Here, we present a facile and straightforward synthesis method for size-controlled highly monodisperse nickel nanoparticles avoiding the use of, e.g., Schlenk techniques and space-consuming labware. The nanoparticles produced by this novel synthetic route were investigated using small-angle X-ray scattering, transmission electron microscopy, X-ray diffraction, and X-ray spectroscopy. The nanoparticles were in a size range of 4–16 nm, show high sphericity, no oxidation, and no agglomeration after synthesis.
Air-coupled ultrasound was used for assessing natural defects in wood boards by through-transmission scanning measurements. Gas matrix piezoelectric (GMP) and ferroelectret (FE) transducers were studied. The study also included tests with additional bias voltage with the ferroelectret receivers. Signal analyses, analyses of the measurement dynamics and statistical analyses of the signal parameters were conducted. After the measurement series, the samples were cut from the measurement regions and the defects were analyzed visually from the cross sections. The ultrasound responses were compared with the results of the visual examination of the cross sections. With the additional bias voltage, the ferroelectret measurement showed increased signal-to-noise ratio, which is especially important for air-coupled measurement of high-attenuation materials like wood. When comparing the defect response of GMP and FE sensors, it was found that FE sensors had more sensitive dynamic range, resulting from better s/n ratio and short response pulse. Classification test was made to test the possibility of detecting defects in sound wood. Machine learning methods including decision trees, k-nearest neighbor and support vector machine were used. The classification accuracy varied between 72 and 77% in the tests. All the tested machine learning methods could be used efficiently for the classification.