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Paper des Monats
- ja (3)
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.
The precise analysis of cation diffusion profiles through corrosion scales is an important aspect to evaluate corrosion phenomena under multicomponent chemical load, as during high‐temperature corrosion under deposits and salts. The present study shows a comprehensive analysis of cation diffusion profiles by electron microprobe analysis and microbeam X‐ray absorption near edge structure (µ‐XANES) spectroscopy in mixed oxide/sulfide scales grown on Fe–Cr model alloys after exposing them to 0.5% SO2. The results presented here correspond to depth‐dependent phase identification of oxides and sulfides in the corrosion scales by µ‐XANES and the description of oxidation‐state‐dependent diffusion profiles. Scales grown on low‐ and high‐alloyed materials show both a well‐pronounced diffusion profile with a high concentration of Fe3+ at the gas and a high concentration of Fe2+ at the alloy interface. The distribution of the cations within a close‐packed oxide lattice is strongly influencing the lattice diffusion phenomena due to their different oxidation states and therefore different crystal‐field preference energies. This issue is discussed based on the results obtained by µ‐XANES analysis.
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.
A gray cast iron disc, which had been submitted to a heavy duty automotive brake test, was examined with energy filtered transmission electron microscopy. A graphite flake in a convenient angular position showed the shear interaction of graphite layers with the iron matrix in nano-scale resolution. Atomic layers of graphite were wedged into the ferritic bulk, allowing the entrance of oxygen and the subsequent formation of magnetite. The exfoliated few-layer graphene batches deformed heavily when forced into the matrix. When Raman spectra from the disc surface, which show distinctive carbonaceous bands, were compared with Raman spectra from graphite subjected to deformation in a shaker mill with different milling times, it could be seen that the shear stress on the brake surface was much more effective to induce disorder than the milling, where compressive and impact forces had been additionally exerted on the sample. During shear load the high anisotropy of elastic modulus in the graphite crystalline structure and the low adhesion between graphite basal planes allowed the exfoliation of wrinkled few-layer grapheme batches, causing the formation of more defect related Raman bands than the mechanical stress during high-energy milling.
Our research focused on Inconel 625 weld overlays on 16Mo3 steel boiler pipes. The Investigation focused on the characterization of changes in the microstructure and chemical composition after annealing. The annealing was performed for ten hours at temperatures from 600 to 1000°C. Changes in the microstructure were observed with a scanning and transmission electron microscope (SEM and TEM). The investigation was supplemented by hardness measurements.
Approximant-based orientation determination of quasicrystals using electron backscatter diffraction
(2020)
Orientation mapping of quasicrystalline materials is demonstrated using crystalline approximant structures in the technique of electron backscatter diffraction (EBSD). The approximant-based orientations are symmetrised according to the rotational point group of the quasicrystal, including the visualization of orientation maps using proper colour keys for quasicrystal symmetries. Alternatively, approximant-based orientation data can also be treated using pseudosymmetry post-processing options in the EBSD system software, which enables basic grain size estimations. Approximant-based orientation analyses are demonstrated for icosahedral and decagonal quasicrystals.
(Bio)geochemical cycling of gold (Au) has been demonstrated in present-day (semi)-arid, (sub)-tropical and temperate environment. Hereby biofilms on Au-bearing mineral- and Au-particle surfaces drive Au dispersion and reconcentration, thereby (trans)forming the particles. However, it is unknown if biogeochemical cycling of Au occurs in polar environments, where air temperatures can reach −40 °C and soils remain frozen for much of the year. Therefore, placer Au-particles, soils and waters were collected at two placer mining districts in arctic Finland, i.e., the Ivalojoki and Lemmenjoki goldfields. Sites were chosen based on contrasting settings ((glacio)-fluvial vs. glacial-till deposits) and depths (surface to 5m below current surface). Gold particles were studied using a combination of tagged 16S rRNA gene next generation sequencing and electron microscopic/microanalytical techniques. Across all sites a range of Au-particle morphologies were observed, including morphotypes indicative of Au dissolution and aggregation. Elevated Au concentrations indicative of Au mobility were detected in placer particle bearing soils at both districts. Typically Au-particles were coated by polymorphic biofilm layers composed of living and dead cells embedded in extracellular polymeric substances. Intermixed were biominerals, clays and iron-sulfides/oxides and abundant secondary Au morphotypes, i.e., nano-particles, microcrystals, sheet-like Au, branched Au networks and overgrowths and secondary rims. Biofilms communities were composed of Acidobacteria (18.3%), Bacteroidetes (15.1%) and Proteobacteria (47.1%), with β-Proteobacteria (19.5%) being the most abundant proteobacterial group. Functionally, biofilms were composed of taxa contributing to biofilm establishment, exopolymer production and nutrient cycling, abundant taxa capable of Au mobilization, detoxification and biomineralization, among them Cupriavidus metallidurans, Acinetobacter spp. and Pseudomonas spp., were detected. In conclusion, these results demonstrate that placer Au-particle transformation and Au dispersion occur in cold, arctic environments. This corroborates the existence of biogeochemical Au cycling in present-day cold environments.
The Bravais lattices and their lattice parameters are blindly determined using electron backscatter diffraction (EBSD) patterns of materials with cubic or tetragonal crystal structures. Since the geometric relationships in a single EBSD pattern are overdetermined, the relative errors of determining the lattice parameters as well as the axial ratios are confined to about 0.7 ± 0.4% and 0.07 ± 0.03%, respectively, for ideal simulated EBSD patterns. The accuracy of the crystal orientation determination reaches about 0.06 ± 0.03°. With careful manual band detection, the accuracy of determining lattice parameters from experimental patterns can be as good as from simulated patterns, although the results from simulated patterns are often better than expermental patterns, which are lower quality and contain uncertain systematic errors. The reasonably high accuracy is obtained primarily because the detection of the diffracting-plane traces and zone axes is relatively accurate. The results here demonstrate that the developed procedure based on the EBSD technique presents a reliable tool for crystallographic characterization of the Bravais lattices of unknown phases.
In failure analysis, micro-fractographic analysis of fracture surfaces is usually performed based on practical knowledge which is gained from available studies, own comparative tests, from the literature, as well as online databases. Based on comparisons with already existing images, fracture mechanisms are determined qualitatively. These images are mostly two-dimensional and obtained by light optical and scanning electron imaging techniques. So far, quantitative assessments have been limited to macrocopically determined percentages of fracture types or to the manual measurement of fatigue striations, for example. Recently, more and more approaches relying on computer algorithms have been taken, with algorithms capable of finding and classifying differently structured fracture characteristics. For the Industrial Collective Research (Industrielle Gemeinschaftsforschung, IGF) project “iFrakto” presented in this paper, electron-optical images are obtained, from which topographic information is calculated. This topographic information is analyzed together with the conventional 2D images. Analytical algorithms and deep learning are used to analyze and evaluate fracture characteristics and are linked to information from a fractography database. The most important aim is to provide software aiding in the application of fractography for failure analysis. This paper will present some first results of the project.