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We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.
The corner echo is a well-known effect in ultrasonic testing, which allows detection of surface breaking cracks with predominantly perpendicular orientation to the surface as, for example, corrosion cracks in metal pipes or shafts. This echo is formed by two planes, the surface of the crack and the surface which the crack breaks. It can also be classified as a half-skip method, since a reflection of the pulse occurs on the backwall before the reflection at the defect takes place. In combination with the diffraction from the crack tip, the corner echo also allows crack sizing. As shown in this paper, the corner reflection can be used in civil engineering for nondestructive inspection of concrete. Commercially available low frequency ultrasonic arrays with dry point contact sources generate SH transversal waves with sufficient divergence of the sound field in order to detect corner reflections. Ultrasonic line-scans and area-scans were acquired with a linear array on flat concrete specimens, and the data were reconstructed by the Synthetic aperture focusing technique. If the angles and the area of reconstruction are chosen accordingly, the corner echo reflection can be distinguished from other ultrasonic information. The corner echo can thus be used as a method for deciding whether a crack is a partial-depth crack or a full-depth crack and thus for obtaining a statement about crack depth. This paper presents corresponding experimental results obtained on concrete specimens with artificial test defects and cracks induced under controlled conditions.
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.
An automated and straightforward detection and data treatment strategy for the determination of the protein relative concentration in individual human cells by single cell–inductively coupled plasma–time-of-flight mass spectrometry (sc-ICP-ToF-MS) is proposed. Metal nanocluster (NC)-labeled specific antibodies for the target proteins were employed, and ruthenium red (RR) staining, which binds to the cells surface, was used to determine the number of cell events as well as to evaluate the relative volume of the cells. As a proof of concept, the expression of hepcidin, metallothionein-2, and ferroportin employing specific antibodies labeled with IrNCs, PtNCs, and AuNCs, respectively, was investigated by sc-ICP-ToF-MS in human ARPE-19 cells. Taking into account that ARPE-19 cells are spherical in suspension and RR binds to the surface of the cells, the Ru intensity was related to the cell volume (i.e., the cell volume is directly proportional to (Ru intensity)3/2), making it possible to determine not only the mass of the target proteins in each individual cell but also the relative concentration. The proposed approach is of particular interest in comparing cell cultures subjected to different supplementations. ARPE-19 cell cultures under two stress conditions were compared: a hyperglycemic model and an oxidative stress model. The comparison of the control with treated cells shows not only the mass of analyzed species but also the relative changes in the cell volume and concentration of target proteins, clearly allowing the identification of subpopulations under the respective treatment.
A sedimented freshwater suspended organic matter fortified with particles of polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET) was employed in an interlaboratory comparison of thermoanalytical methods for microplastics identification and quantification. Three laboratories performed pyrolysis gas chromatography-mass spectrometry (Py-GC-MS), three others provided results using thermal extraction desorption followed by gas chromatography coupled to mass spectrometry (TED-GC-MS). One participant performed thermogravimetry-infrared spectroscopy (TGA-FTIR) and two participants used thermogravimetry coupled to mass spectrometry (TGA-MS). Further participants used differential scanning microscopy (DSC), a procedure based on micro combustion calorimetry (MCC) and a procedure based on elemental analysis.
Each participant employed a different combination of sample treatment, calibration and instrumental Settings for polymer identification and quantification. Though there is obviously room for improvements regarding the between-laboratory reproducibility and the harmonization of procedures it was seen that the participants Performing Py-GC-MS, TED-GC-MS, and TGA-FTIR were able to correctly identify all polymers and to report reasonable quantification results in the investigated concentration range (PE: 20.0 μg/mg, PP: 5.70 μg/mg; PS: 2.20 μg/mg, PET: 18.0 μg/mg). Although for the other methods limitations exists regarding the detection of specific polymers, they showed potential as alternative approaches for polymer quantification in solid environmental matrices.
In this study, we compare analytical methods for PFAS determination–target analysis, non-target screening (NTS), direct total oxidizable precursor assay (dTOPA) and extractable organically bound fluorine (EOF). Therefore, suspended particulate matter (SPM) samples from German rivers at different locations in time series from2005 to 2020 were analyzed to investigate temporal and spatially resolved trends. In this study 3 PFAS mass balances approaches were utilized: (i) PFAA target vs. PFAS dTOPA, (ii) PFAS target vs. EOF and (iii) PFAS target vs. PFAS dTOPA vs. organofluorines NTS vs. EOF. Mass balance approach (i) revealed high proportions of precursor substances in SPM samples. For the time resolved analysis an increase from 94% (2005) to 97% in 2019 was observable. Also for the spatial resolved analysis precursor proportions were high with >84% at all sampling sites. Mass balance approach (ii) showed that the unidentified EOF (uEOF) fraction increased over time from82% (2005) to 99% (2019). Furthermore, along the river courses the uEOF increased. In the combined mass balance approach (iii) using 4 different analytical approaches EOF fractions were further unraveled. The EOF pattern was fully explainable at the sampling sites at Saar and Elbe rivers. For the time resolved analysis, an increased proportion of the EOF was now explainable. However, still 27% of the EOF for the time resolved analysis and 25% of the EOF for the spatial resolved analysis remained unknown. Therefore, in a complementary approach, both the EOF and dTOPA reveal unknown gaps in the PFAS mass balance and are valuable contributions to PFAS risk assessment. Further research is needed to identify organofluorines summarized in the EOF parameter.
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.
Optimizing the Green Synthesis of ZIF-8 by Reactive Extrusion Using In Situ Raman Spectroscopy
(2023)
We report the scale-up of a batch solid synthesis of zeolitic imidazolate framework-8 (ZIF-8) for reactive extrusion. The crystalline product forms in the extruder directly under the mixture of solid 2-methylimidazole and basic zinc carbonate in the presence of a catalytic amount of liquid. The process parameters such as temperature, liquid type, feeding rate, and linker excess were optimized using the setup specifically designed for in situ Raman spectroscopy. Highly crystalline ZIF-8 with a Brunauer–Emmett–Teller (BET) surface area of 1816 m2 g–1 was quantitatively prepared at mild temperature using a catalytic amount of ethanol and a small excess of the linker. Finally, we developed a simple and comprehensive approach to evaluating the environmental friendliness and scalability of metal–organic framework (MOF) syntheses in view of their large-scale production.
In this study, we employed nitrogen microwave inductively coupled atmospheric-pressure plasma (MICAP) combined with quadrupole mass spectrometry (MS) and a liquid sample introduction system to analyze heavy metals in soils. The vanadium, cobalt, nickel, zinc, copper, chromium, arsenic, lead, and cadmium contents in seven reference and three environmental soil samples determined using MICAP-MS were within the uncertainty of the reference values, indicating that MICAP-MS is promising for soil analysis similar to the conventional inductively coupled plasma mass spectrometry (ICP-MS) technique. In addition, the limits of detection (LODs) and sensitivity of both techniques using N2 and Ar plasma were of the same order of magnitude. Furthermore, the performance of MICAP-MS under different N2 purity was investigated, and we found that the plasma formation and ionization efficiency were not influenced by the impurities in the gas. A prominent advantage of MICAP-MS is the low operating cost associated with gas consumption. In this work, MICAP-MS used nitrogen, which is cheaper than argon, and consumed 25% less gas than ICP-MS. Using low-purity N2 can further reduce the gas cost, making MICAP-MS more cost effective than ICP-MS. These results suggest that MICAP-MS is a promising alternative to ICP-MS for the analysis of heavy metals in the soil.
The combination of acoustically levitated droplets, mid-IR laser evaporation, and subsequent post-ionization by secondary electrospray ionization was applied for monitoring the enzymatic digestion of various proteins. Acoustically levitated droplets are an ideal, wall-free model reactor, readily allowing compartmentalized microfluidic trypsin digestions. Time-resolved interrogation of the droplets yielded real-time information on the progress of the reaction and thus provided insights into reaction kinetics. After 30 min of digestion in the acoustic levitator, the obtained protein sequence coverages were identical to the reference overnight digestions. Importantly, our results clearly demonstrate that the applied experimental setup can be used for the real-time investigation of chemical reactions. Furthermore, the described methodology only uses a fraction of the typically applied amounts of solvent, analyte, and trypsin. Thus, the results exemplify the use of acoustic levitation as a green analytical chemistry alternative to the currently used batch reactions.