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Paper des Monats
- ja (27)
Creep-resistant steels such as the 13CrMoV9-10, used in the construction of thick-walled pressure vessels, are most commonly submerged arc welded (SAW). These steels can develop stress relief cracks (SRC) if the mandatory post weld heat treatment (PWHT) is performed improperly. Current PWHT parameters, such as heating rate and holding time at a specific holding temperature, are based on both empirical experience and conventional free shrinking welding experiments to characterize the SRC-susceptibility of the weld. These cannot adequately depict the higher residual stresses caused by the structurally induced stiffness of the surrounding construction. This study discusses the development of a repeatable, precise, and time-efficient methodology to study the effects of different stress levels and heating rates on the SRC susceptibility of the coarse grain heat-affected zone (CGHAZ). For that purpose, samples were thermically treated to simulate a coarse grain heat-affected zone (CGHAZ) and subsequently exposed to representative levels of stress during the heating phase of a PWHT. The recorded stress and heating rate–dependent strains were mathematically analyzed via curve tracing/calculus to identify interdependent effects. This procedure facilitates the measurement of material characteristics such as carbide growth on grain boundaries at the µm-scale via an integrated value over the entire sample volume. The first and second derivatives show a slight, precipitate-dependent, increase in hardness of the sample, depending on the heating rate and applied stress. This new methodology generates an improved assessment of the SRC susceptibility of SAW microstructures of creep-resistant CrMoV steels.
The complex nature of liquid water saturation of polymer electrolyte fuel cell (PEFC) catalyst layers (CLs) greatly affects the device performance. To investigate this problem, we present a method to quantify the presence of liquid water in a PEFC CL using small-angle X-ray scattering (SAXS). This method leverages the differences in electron densities between the solid catalyst matrix and the liquid water filled pores of the CL under both dry and wet conditions. This approach is validated using ex situ wetting experiments, which aid the study of the transient saturation of a CL in a flow cell configuration in situ. The azimuthally integrated scattering data are fitted using 3D morphology models of the CL under dry conditions. Different wetting scenarios are realized in silico, and the corresponding SAXS data are numerically simulated by a direct 3D Fourier transformation. The simulated SAXS profiles of the different wetting scenarios are used to interpret the measured SAXS data which allows the derivation of the most probable wetting mechanism within a flow cell electrode.
While the synthesis of Metal-Organic Framework (MOF) particles can be as easy as adding two solutions together, reproducibly obtaining the same particles, time and time again, is a lot harder. As laboratory-independent reproducibility is a cornerstone of the scientific method, we must put effort into finding and controlling all necessary parameters to achieve this.
An open-source Python/EPICS-controlled robotic platform (see picture) was adapted to systematically explore this for a 20 ml MOF synthesis of the Zeolitic Imidazole Framework-8 (ZIF-8) chemistry in methanol. Parameters that were explored included: 1) addition sequence, 2) addition speeds, 3) reaction times, 4) source chemicals, 5) stirring speeds, 6) stirring bar choice, 7) starting concentrations, and 8) workup methodologies. It was found that, by controlling these parameters, highly reproducible syntheses are obtained. Secondly, the variation of these parameters alone led to a dramatic difference in volume-weighted particle size means, which exceeds an order of magnitude as investigated by our in-house X-ray scattering instrument [1].
The syntheses are thoroughly documented in an automated fashion, and the synthesis libraries as well as analyses libraries will become available in batches soon. With this library, it will be possible to extract previously unknown correlations, and other laboratories can produce specific particles by following the exact procedures of the particles of their choice.
Robot-assisted laser thermography for surface breaking crack detection on complex shaped components
(2023)
Laser thermography using a focused (spot or line) beam has proved to be effective for detection of surface breaking cracks on planar samples. In this work, we use the same principle, but applied to complex shaped components, like a rail cross-section, a gear, and a gas turbine blade. We use a six-axis robot to move the sample in-front of our thermographic setup. Several scanning-path and thermographic parameters are explored: scanning speed, density of points in each scanning slice, laser power, camera framerate. Additionally, we explore semi-automatic evaluation algorithms for crack detection, as well as 2D-to-3D registration of the found indications.
The use of benchtop-NMR instruments is constantly increasing during the recent years. Advantages of being affordable, portable and easy-to-operate without the need for trained staff make them especially interesting for industrial applications in quality control. However, applications of NMR spectroscopy as an online PAT tool are still very rare but offer a huge potential for process optimization and control. A key task to exploit this potential is hardware field integration of the lab-instruments in a rough environment of a chemical plant. Additionally, developments in automation and data evaluation are mandatory to ensure a robust unattended operation with low maintenance requirements. Here, we show an approach of a fully automated analyzer enclosure considering explosion safety, field communication, as well as environmental conditions in the field.
Temperature sensitivity is still a limitation of benchtop-NMR instruments in flow applications. Recent developments of manufacturers allow for limited operation at static temperature levels, however, a dynamic system for continuous operation is still not available. Using a prototype system offering a larger bore, active temperature shielding studies with thermostated air were performed evaluating the performance.
Automated data evaluation of NMR spectra using a modular indirect hard modeling (IHM) approach showed good results and flexibility. A second data analysis approach based on artificial neural networks (ANN) was evaluated.Therefore, amount of data was augmented to be sufficient for training. The results show comparable performance, while improving the calculation time tremendously, offering new ways to simultaneously evaluating large numbers of different models.
Immobilized metal affinity chromatography (IMAC) is a widely used technique for purifying polyhistidine-tagged recombinant proteins. However, it often has practical limitations that require complex optimizations and additional steps for purification. In this study, we introduce functionalized corundum particles as a novel, efficient, and economical method for purifying recombinant proteins in a column-free format. The corundum surface is modified with amino silane APTES, followed by EDTA dianhydride, and then loaded with nickel ions. We used the Kaiser test to monitor the modification process and ICP-MS to quantify the metal-binding capacity. To evaluate the system, we used His-tagged protein A/G (PAG) mixed with bovine serum albumin (BSA). The corundum particles exhibited a binding capacity of approximately 3 mg of protein per gram of corundum or 2.4 mg per 1 mL of corundum suspension. We also examined cytoplasm obtained from different E. coli strains as an example of a complex matrix. Varying the imidazole concentration in the loading and washing buffers showed that higher concentrations during loading improved purity. Even with sample volumes as large as one liter, we successfully isolated recombinant proteins down to a concentration of 1 µg/mL. We found higher purity levels with corundum when comparing the corundum material to standard Ni–NTA agarose beads. We successfully purified His6-MBP-mSA2, a fusion protein comprising monomeric streptavidin and maltose-binding protein, from E. coli cytoplasm, demonstrating the method's applicability. We also purified SARS-CoV-2-S-RBD-His8 expressed in human Expi293F cells, confirming its suitability for mammalian cell culture supernatants. The material cost of the nickel-loaded corundum material (without regeneration) is estimated to be less than 30 cents per gram of functionalized support or 10 cents per milligram of isolated protein. Another advantage of this system is the exceptional physical and chemical stability of corundum particles. Overall, we have demonstrated that this novel material offers an efficient, robust, and cost-effective purification platform for His-tagged proteins, even in challenging, complex matrices and large sample volumes with low product concentrations. This method has potential applications in both small laboratories and large-scale industrial settings.
The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE.
This paper reports a systematic study into the effect of nitrogen on iron-catalyzed graphitization of biomass. Chitin, chitosan, N-acetylglucosamine, gelatin and glycine were selected to represent nitrogen-rich saccharides and amino-acid/polypeptide biomass precursors. The materials were pyrolyzed with an iron catalyst to produce carbons with a wide range of chemical and structural features such as mesoporosity and nitrogen-doping. Many authors have reported the synthesis of nitrogen-doped carbons by pyrolysis and these have diverse applications. However, this is the first systematic study of how nitrogen affects pyrolysis of biomass and importantly the catalytic graphitization step. Our data demonstrates that nitrogen inhibits graphitization but that some nitrogen survives the catalytic graphitization process to become incorporated into various chemical environments in the carbon product.
The dataset contains raw data gathered with the ultrasonic pulse-echo method on concrete specimens. The surfaces of the measuring objects were automatically scanned point by point. Pulse-echo measurements were performed at each of these measuring points. The test specimens represent two typical testing tasks in construction industry: the detection of objects and the determination of dimensions to describe the geometry of components. By automating the measurement process, the different test scenarios are examined with a high repeatability, precision and measuring point density. Longitu- dinal and transversal waves were used and the geometrical aperture of the testing system was varied. The low-frequency probes operate in a range of up to approximately 150 kHz. In addition to the specification of the geometrical dimensions of the individual probes, the directivity pattern and the sound field characteristics are provided. The raw data are stored in a universally readable format. The length of each time signal (A-scan) is two milliseconds and the sampling rate is two mega-samples per second. The provided data can be used for comparative studies in signal analysis, imag- ing and interpretation as well as for evaluation pur- poses in different, practically relevant testing scenarios.
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing
workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers.
Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample
labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.