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Paper des Monats
- ja (72)
Duplex stainless steels (DSS) are frequently used, especially in applications requiring high strength combined with high corrosion resistance in aggressive media. Examples include power plant components and maritime structures. During welding of these steels, local variations in chemical composition can occur. This results in ferritization of the material and negatively affects the mechanical properties of the components. In this work, tungsten inert gas (TIG) welding experiments were performed with DSS. Chemical composition analysis was realized in situ by using Laser Induced Breakdown Spectroscopy (LIBS). The aim of the work is to quantitatively measure the chemical composition in the weld seam of various DSS and to identify possible influences of welding parameters on the microstructure of the material. The chemical concentrations of the main alloying elements Cr, Ni, Mn on the surface of the sample during the welding process and the cooling process were measured. Mn and Ni are austenite stabilizers and their content increases during welding by using certain high alloyed filler material. Spectra were recorded every 1.3 s at a spacing of approximately 2 mm. During the cooling process the location of the measurement was not changed. The LIBS method is proofed to be suitable for the quantitative representation of the chemical compositions during the welding process.
The greatest challenge when using deep convolutional neural networks (DCNNs) for automatic segmentation of microstructural X-ray computed tomography (XCT) data is the acquisition of sufficient and relevant data to train the working network. Traditionally, these have been attained by manually annotating a few slices for 2D DCNNs. However, complex multiphase microstructures would presumably be better segmented with 3D networks. However, manual segmentation labeling for 3D problems is prohibitive. In this work, we introduce a method for generating synthetic XCT data for a challenging six-phase Al–Si alloy composite reinforced with ceramic fibers and particles. Moreover, we propose certain data augmentations (brightness, contrast, noise, and blur), a special in-house designed deep convolutional neural network (Triple UNet), and a multi-view forwarding strategy to promote generalized learning from synthetic data and therefore achieve successful segmentations. We obtain an overall Dice score of 0.77. Lastly, we prove the detrimental effects of artifacts in the XCT data on achieving accurate segmentations when synthetic data are employed for training the DCNNs. The methods presented in this work are applicable to other materials and imaging techniques as well. Successful segmentation coupled with neural networks trained with synthetic data will accelerate scientific output.
The concept behind this Research Topic (RT) was to collect works, in which Atomic Force Microscopy (AFM) techniques are employed to study tribological phenomena and to push the resolution of measurements towards single asperity contact. Thanks to the direct determination of sample height with sub-nanometer resolution and the possibility of measuring local friction, AFM can be employed after a tribotest to detect topography and friction changes at the nanometer scale. Recently, efforts are being expended to use AFM cantilevers as tribometers, i.e., as probes altering the volume of suitable samples, thereby measuring tip and/or sample wear and friction at the nano/microscale. Thus, single asperity contact, friction, and wear can be investigated. Since friction and wear at the macroscale are the result of asperities interactions, such experiments are of great importance for better understanding of tribological processes.
In 2005, a team of Polish archaeologists discovered three Coptic codices, two on papyrus and one on parchment, in the rubbish dump of a hermitage at Sheik Abd el-Gurna, Egypt. While the parchment codex and the remains of the bindings of the papyrus ones have been conserved, the text blocks of the papyrus codices were opened and disassembled and after being disinfected remained untouched for nearly 15 years, until the current project started. Currently, all three books are stored in the collection of the Coptic Museum in Cairo. Our team undertook the conservation of and archaeometric research on the papyrus codices. The first of the books contains the Canons of Pseudo-Basil, while the second is an Encomium of St. Pisenthios. Both codices have been palaeographically dated to the seventh-eighth centuries CE. In this paper, we describe our project and present the results of the work carried out on the Canons of Pseudo-Basil. Our work involved photography in transmitted light, measurements of the thickness of the papyrus leaves, and digital microscopy under three-light illumination. These measurements allowed us to determine the types of the inks and characterize the papyrus writing substrate. For conservation, we used a novel method developed in the Egyptian Museum in Berlin that comprises suspending papyrus folios on a translucent, extremely thin Japanese paper inside glass frames without the use of adhesives.In 2005, a team of Polish archaeologists discovered three Coptic codices, two on papyrus and one on parchment, in the rubbish dump of a hermitage at Sheik Abd el-Gurna, Egypt. While the parchment codex and the remains of the bindings of the papyrus ones have been conserved, the text blocks of the papyrus codices were opened and disassembled and after being disinfected remained untouched for nearly 15 years, until the current project started. Currently, all three books are stored in the collection of the Coptic Museum in Cairo. Our team undertook the conservation of and archaeometric research on the papyrus codices. The first of the books contains the Canons of Pseudo-Basil, while the second is an Encomium of St. Pisenthios. Both codices have been palaeographically dated to the seventh-eighth centuries CE. In this paper, we describe our project and present the results of the work carried out on the Canons of Pseudo-Basil. Our work involved photography in transmitted light, measurements of the thickness of the papyrus leaves, and digital microscopy under three-light illumination. These measurements allowed us to determine the types of the inks and characterize the papyrus writing substrate. For conservation, we used a novel method developed in the Egyptian Museum in Berlin that comprises suspending papyrus folios on a translucent, extremely thin Japanese paper inside glass frames without the use of adhesives.
Quantitative 1H nuclear magnetic resonance (qNMR) of aromatic amino acids for protein quantification
(2023)
Hydrolysis of protein samples into amino acids facilitates the use of NMR spectroscopy for protein and peptide quantification. Different conditions have been tested for quantifying aromatic amino acids and proteins. The pH-dependent signal shifts in the aromatic region of amino acid samples were examined. A pH of 12 was found to minimize signal overlap of the four aromatic amino acids. Several aromatic compounds, such as terephthalic acid, sulfoisophthalic acid, and benzene tricarboxylic acid, were applied as internal standards. The quantification of amino acids from an amino acid standard was performed. Using the first two suggested internal standards, recovery was ~97% for histidine, phenylalanine, and tyrosine at a concentration of approximately 1 mM in solution. Acidic hydrolysis of a certified reference material (CRM) of bovine serum albumin (BSA) and subsequent quantification of Phe and Tyr yielded recoveries of 98% ± 2% and 88% ± 4%, respectively, at a protein concentration of 16 g/L or 250 µM.
Volatile organic compounds (VOCs) are of interest in many different fields. Among them are food and fragrance analysis, environmental and atmospheric research, industrial applications, security or medical and life science. In the past, the characterization of these compounds was mostly performed via sample collection and off-site analysis with gas chromatography coupled to mass spectrometry (GC-MS) as the gold standard. While powerful, this method also has several drawbacks such as being slow, expensive, and demanding on the user. For decades, intense research has been dedicated to find methods for fast VOC analysis on-site with time and spatial resolution. We present the working principles of the most important, utilized, and researched technologies for this purpose and highlight important publications from the last five years. In this overview, non-selective gas sensors, electronic noses, spectroscopic methods, miniaturized gas chromatography, ion mobility spectrometry and direct injection mass spectrometry are covered. The advantages and limitations of the different methods are compared. Finally, we give our outlook into the future progression of this field of research.
Chromium(III) complexes can show phosphorescence from the spin-flip excited doublet states 2E/2T1 in the near-infrared with high photoluminescence quantum yields and extremely long lifetimes in the absence of dioxygen. The prototype molecular ruby, [Cr(ddpd)2]3+ (ddpd = N,N’-dimethyl-N,N’-dipyridine-2-ylpyridine-2,6-diamine), has a photoluminescence quantum yield and a luminescence lifetime of 13.7% and 1.1 ms in deaerated acetonitrile, respectively. However, its luminescence is strongly quenched by 3O2 via an efficient Dexter-type energy transfer process. To enable luminescence applications of molecular rubies in solution under aerobic conditions, we explored the potential of sterically demanding ddpd ligands to shield the chromium(III) center from O2 using steady state and time-resolved photoluminescence spectroscopy. The structures of the novel complexes with sterically demanding ligands were investigated by single crystal X-ray diffraction and quantum chemically by density functional theory calculations. The O2 sensitivity of the photoluminescence was derived from absolutely measured photoluminescence quantum yields and excited state lifetimes under inert and aerobic conditions and by Stern–Volmer analyses of these data. Optimal sterically shielded chromium(III) complexes revealed photoluminescence quantum yields of up to 5.1% and excited state lifetimes of 518 μs in air-saturated acetonitrile, underlining the large potential of this ligand design approach to broaden the applicability of highly emissive chromium(III) complexes.
Microscopic knowledge of the structural, energetic, and electronic properties of scandium fluoride is still incomplete despite the relevance of this material as an intermediate for the manufacturing of Al−Sc alloys. In a work based on first-principles calculations and X-ray spectroscopy, we assess the stability and electronic structure of six computationally predicted ScF3 polymorphs, two of which correspond to experimentally resolved single-crystal phases. In the theoretical analysis based on density functional theory (DFT), we identify similarities among the polymorphs based on their formation energies, chargedensity distribution, and electronic properties (band gaps and density of states). We find striking analogies between the results obtained for the ow- and high-temperature phases of the material, indirectly confirming that the transition occurring between them mainly consists of a rigid rotation of the lattice. With this knowledge, we examine the X-ray absorption spectra from the Sc and F K-edge contrasting firstprinciples results obtained from the solution of the Bethe−Salpeter equation on top of all-electron DFT with high-energy-resolution fluorescence detection measurements. Analysis of the computational results sheds light on the electronic origin of the absorption maxima and provides information on the prominent excitonic effects that characterize all spectra. A comparison with measurements confirms that the sample is mainly composed of the high- and low-temperature polymorphs of ScF3. However, some fine details in the experimental results suggest that the probed powder sample may contain defects and/or residual traces of metastable polymorphs.
The strain field can reflect the initiation time of solidification cracks during the welding process. The traditional strain measurement is to first obtain the displacement field through digital image correlation (DIC) or optical flow and then calculate the strain field. The main disadvantage is that the calculation takes a long time, limiting its suitability to real-time applications. Recently, convolutional neural networks (CNNs) have made impressive achievements in computer vision.
To build a good prediction model, the network structure and dataset are two key factors. In this paper, we first create the training and test sets containing welding cracks using the controlled tensile weldability (CTW) test and obtain the real strain fields through the Lucas–Kanade algorithm. Then, two new networks using ResNet and DenseNet as encoders are developed for strain prediction, called StrainNetR and StrainNetD. The results show that the average endpoint error (AEE) of the two networks on our test set is about 0.04, close to the real strain value. The computation time could be reduced to the millisecond level, which would greatly improve efficiency.
In this work, we present a novel approach to photothermal super resolution based thermographic resolution of internal defects using two-dimensional pixel pattern-based active photothermal laser heating in conjunction with subsequent numerical reconstruction to achieve a high-resolution reconstruction of internal defect structures. With the proposed adoption of pixelated patterns generated using laser coupled high-power DLP projector technology the complexity for achieving true two-dimensional super resolution can be dramatically reduced taking a crucial step forward towards widespread practical viability. Furthermore, based on the latest developments in high-power DLP projectors, we present their first application for structured pulsed thermographic inspection of macroscopic metal samples. In addition, a forward solution to the underlying inverse problem is proposed along with an appropriate heuristic to find the regularization parameters necessary for the numerical inversion in a laboratory setting. This allows the generation of synthetic measurement data, opening the door for the application of machine learning based methods for future improvements towards full automation of the method. Finally, the proposed method is experimentally validated and shown to outperform several established conventional thermographic testing techniques while conservatively improving the required measurement times by a factor of 8 compared to currently available photothermal super resolution techniques.