Analytische Chemie
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Paper des Monats
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Per- and polyfluoroalkyl substances (PFAS) are a large group of organofluorine surfactants used in the formulations of thousands of consumer goods, including aqueous film-forming foams (AFFF) used to suppress aviation fires in training scenarios, non-stick cookware, fast-food wrappers, water-repellent fabrics, medical equipment, and plastic and leather products. As a result of the perpetual use of PFAS containing products, effluents and sewage sludge from wastewater treatment plants (WWTPs) have been observed to be an important pathway for PFAS into the environment. In Germany, phosphorus and other nutrients from sewage sludge and wastewater should be recycled in WWTPs of cities with a large population. However, it is not clear if PFAS contamination from wastewater and sewage sludge end up in novel wastewater-based fertilizers. Normally, PFAS are analyzed using PFAS protocols typically with time-consuming extraction steps and LC-MS-MS quantification. However, for screening of PFAS contaminations in sewage sludge or wastewater-based fertilizers also passive sampler based on the Diffusive Gradients in Thin-films (DGT) technique can be used for the PFAS extraction. Afterwards, combustion ion chromatography (CIC) can be applied to analyse the “total” amount of PFAS on the passive sampler. Here, we show results from the DGT method in comparison to those of the extractable organic fluorine (EOF) method for a variety of wastewater-based fertilizers. Additionally, we analysed the adsorption of PFAS on the weak anion exchanger (WAX) based DGT passive sampler binding layer by infrared and fluorine K-edge X-ray adsorption near-edge structure (XANES) spectroscopy.
Equilibrium model of titanium laser induced plasma in air with reverse deposition of titanium oxides
(2022)
A chemical-hydrodynamic model of laser induced plasma is developed to study a process of deposition of titanium oxides from titanium laser induced plasma to the titanium target surface. The model is relevant to texturing and coating of titanium bone implants that is done by scanning the ablation laser across implant surfaces. Such the procedure improves the biocompatibility and durability of the implants. The model considers plasma chemical reactions, formation of condensed species inside the plasma plume, and deposition and accumulation of these species on the ablation surface. A chemical part of the model is based on minimization of Gibbs free energy of the chemical system; it is used to calculate the chemical composition of the plasma. A hydrodynamic part uses the 2D fluid-dynamic equations that model a 3D axisymmetric plasma plume and assumes the mass and energy exchange between the plasma and the surface. The initial parameters for the model are inferred from experiment.
The model shows that condensed titanium oxides, mostly TiO2, form in a peripheral plasma zone and gradually adhere to the surface during the plasma plume evolution. The model predicts the major component and thickness of the deposit and can be applied for the optimization of experiments aimed at surface modification.
Brillouin Optical Frequency Domain Analysis (BOFDA) is a powerful and well-established method for static distributed sensing of temperature and strain. Recently, we demonstrated a BOFDA system based on convolutional neural network which shortens the measurement time considerably. In this paper, we apply leave-one-out cross validation to evaluate the generalization performance and provide an unbiased and reliable machine learning model for a time-efficient BOFDA system.
In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine learning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective temperature and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection.
The deposition of titanium oxides during titanium laser ablation in air has been experimentally and numerically investigated. A titanium sample was irradiated by nanosecond pulses from an Yb-fber laser with a beam scanned across the sample surface for its texturing. As a result, the hierarchical structure was observed consisting of a microrelief formed by the laser ablation and a nanoporous coating formed by the reverse deposition from the laser induced plasma plume. The chemical and phase composition of the nanoporous coating, as well as the morphology and structure of the surface, were studied using scanning electron microscopy, atomic force microscopy, and X-ray microanalysis. It was found that the deposit consists mostly of porous TiO2 with 26% porosity and inclusions of TiO, Ti2O3, and Ti2O3N. Optical emission spectroscopy was used to control the plasma composition and estimate the effective temperature of plasma plume. The chemical-hydrodynamic model of laser induced plasma was developed to get a deeper insight into the deposition process. The model predicts that condensed titanium oxides, formed in peripheral plasma
zones, gradually accumulate on the surface during the plasma plume evolution. A satisfactory agreement between the experimental and calculated chemical composition of the plasma plume as well as between the experimental and calculated composition and thickness of the deposited film was demonstrated. This allows a cautious conclusion that the formation of condensed oxides in the plasma and their consequent deposition onto the ablation surface are among the key mechanisms of formation of porous surface films.
Following the reviews of atomic-weight determinations and other cognate data in 2015, 2017, 2019 and 2021, the IUPAC (International Union of Pure and Applied Chemistry) Commission on Isotopic Abundances and Atomic Weights (CIAAW) reports changes of standard atomic weights. The symbol Ar(E) was selected for standard atomic weight of an element to distinguish it from the atomic weight of an element E in a specific substance P, designated Ar(E, P). The CIAAW has changed the values of the standard atomic weights of five elements based on recent determinations of terrestrial isotopic abundances:
Ar (argon): from 39.948 ± 0.001 to [39.792, 39.963]
Hf (hafnium): from 178.49 ± 0.02 to 178.486 ± 0.006
Ir (iridium): from 192.217 ± 0.003 to 192.217 ± 0.002
Pb (lead): from 207.2 ± 0.1 to [206.14, 207.94]
Yb (ytterbium): from 173.054 ± 0.005 to 173.045 ± 0.010
The standard atomic weight of argon and lead have changed to an interval to reflect that the natural variation in isotopic composition exceeds the measurement uncertainty of Ar(Ar) and Ar(Pb) in a specific substance. The standard atomic weights and/or the uncertainties of fourteen elements have been changed based on the Atomic Mass Evaluations 2016 and 2020 accomplished under the auspices of the International Union of Pure and Applied Physics (IUPAP). Ar of Ho, Tb, Tm and Y were changed in 2017 and again updated in 2021:
Al (aluminium), 2017: from 26.981 5385 ± 0.000 0007 to 26.981 5384 ± 0.000 0003
Au (gold), 2017: from 196.966 569 ± 0.000 005 to 196.966 570 ± 0.000 004
Co (cobalt), 2017: from 58.933 194 ± 0.000 004 to 58.933 194 ± 0.000 003
F (fluorine), 2021: from 18.998 403 163 ± 0.000 000 006 to 18.998 403 162 ± 0.000 000 005
(Ho (holmium), 2017: from 164.930 33 ± 0.000 02 to 164.930 328 ± 0.000 007)
Ho (holmium), 2021: from 164.930 328 ± 0.000 007 to 164.930 329 ± 0.000 005
Mn (manganese), 2017: from 54.938 044 ± 0.000 003 to 54.938 043 ± 0.000 002
Nb (niobium), 2017: from 92.906 37 ± 0.000 02 to 92.906 37 ± 0.000 01
Pa (protactinium), 2017: from 231.035 88 ± 0.000 02 to 231.035 88 ± 0.000 01
Pr (praseodymium), 2017: from 140.907 66 ± 0.000 02 to 140.907 66 ± 0.000 01
Rh (rhodium), 2017: from 102.905 50 ± 0.000 02 to 102.905 49 ± 0.000 02
Sc (scandium), 2021: from 44.955 908 ± 0.000 005 to 44.955 907 ± 0.000 004
(Tb (terbium), 2017: from 158.925 35 ± 0.000 02 to 158.925 354 ± 0.000 008)
Tb (terbium), 2021: from 158.925 354 ± 0.000 008 to 158.925 354 ± 0.000 007
(Tm (thulium), 2017: from 168.934 22 ± 0.000 02 to 168.934 218 ± 0.000 006)
Tm (thulium), 2021: from 168.934 218 ± 0.000 006 to 168.934 219 ± 0.000 005
(Y (yttrium), 2017: from 88.905 84 ± 0.000 02 to 88.905 84 ± 0.000 01)
Y (yttrium), 2021: from 88.905 84 ± 0.000 01 to 88.905 838 ± 0.000 002
Anear-infrared (NIR) light-triggered release method for nitric oxide (NO) was developed utilizing core/shell NaYF4: Tm/Yb/Ca@NaGdF4:Nd/Yb up-conversion nanoparticles (UCNPs) bearing a mesoporous silica (mSiO2) shell loaded with theNOdonor S-nitroso-N-acetyl-DL-penicillamine (SNAP). To avoid overheating in biological samples, Nd3+ was chosen as a sensitizer, Yb3+ ions as the bridging sensitizer, andTm3+ ions as UV-emissive activator while co-doping with Ca2+ was done to enhance the luminescence of the activatorTm3+.NOrelease from SNAP was triggered by an NIR-UV up-conversion process, initiated by 808nmlight absorbed by the Nd3+ ions.NOrelease was confirmed by the Griess method. Under 808nmirradiation, the viability of the liver cancer cell line HepG2 significantly decreased with increasing UCNPs@mSiO2-SNAP concentration. For a UCNPs@mSiO2-SNAP concentration of 200 μgml−1, the cell survival probability was 47%. These results demonstrate that UCNPs@mSiO2-SNAP can induce the release of apoptosis-inducingNOby NIR irradiation.
For more than 110 years, BAM has been producing reference materials for a wide range of application fields. With the development of new analytical methods and new applications as well as continuously emerging more stringent requirements of laboratory accreditation with regard to quality control and metrological traceability, the demand and requirements for reference materials are increasing. This trend article gives an overview of general developments in the field of reference materials as well as developments in selected fields of application in which BAM is active. This includes inorganic and metal analysis, gas analysis, food and consumer products, and geological samples. In addition to these more traditional fields of application, developments in the areas of optical spectroscopy, particulary fluorescence methods, and nanomaterials are considered.
Thermographic photothermal super resolution reconstruction enables the resolution of internal defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures. In our work, we report an extension of the capabilities of the method for efficient detection and resolution of defect cross sections with fully two-dimensional structured laser-based heating. The reconstruction is carried out using one of two different algorithms that are proposed within this work. Both algorithms utilize the combination of several coherent measurements using convex optimization and exploit the sparse nature of defects/inhomogeneities as is typical for most nondestructive testing scenarios. Finally, the performance of each algorithm is rated on reconstruction quality and algorithmic complexity. The presented experimental approach is based on repeated spatially structured heating by a high power laser. As a result, a two-dimensional sparse defect/inhomogeneity map can be obtained. In addition, the obtained results are compared with those of conventional thermographic inspection methods that make use of homogeneous illumination. Due to the sparse nature of the reconstructed defect/inhomogeneity map, this comparison is performed qualitatively.
An experimental cold-gas study of the response of a choked convergent–divergent nozzle to swirl perturbations is presented. The perturbations were obtained by means of upstream unsteady tangential injections into initially steady flows with different values of steady background swirl. The swirl perturbations induced changes in the axial mass-flow rate, due to either their ingestion or evacuation by the nozzle. This in turn caused a downstream acoustic response. For low-intensity background swirl the responses were found to be similar to those obtained without steady background swirl. Perturbations of a high-intensity background swirl led to different effects. For long injection times, the negative mass-flow rate modulation occurred in two stages. The first stage was similar to that of the background-swirl free case. The second stage occurred after a short time delay, and induced a much stronger negative acoustic response. This unexpected behavior suggests that a significant part of the tangentially injected fluid flows upstream inducing an accumulation of swirl, which is – after tangential injection is ceased – suddenly cleared out through the nozzle. A scaling rule for the amplitudes of these acoustic responses is reported. Furthermore, quasi-steady models, based on steady-state measurements are proposed. These models predict the downstream acoustic response amplitude within a factor two. Additionally, preliminary empirical evidence of the effect of swirl on the downstream acoustic response due to the interaction of entropy patches with a choked nozzle is reported. This was obtained by comparison of sound produced by abrupt radial or tangential sonic injection, upstream from the choked nozzle, of air from a reservoir at room temperature to that from a reservoir with a higher stagnation temperature. Because the mass flow through the nozzle does not increase instantaneously, the injected higher-enthalpy air accumulates upstream of the injection-port position in the main flow. This eventually induces a large downstream acoustic pulse when tangential injection is interrupted. The magnitude of the resulting sound pulse can reach that of a quasi-steady response of the nozzle to a large air patch with a uniform stagnation temperature equal to that of the upstream-injected heated air. This hypothesis is consistent with the fact that the initial indirect-sound pulse is identical to one obtained with unheated air injection. The authors posit that – given all of the insight gleaned from them in this case – acoustic measurements of indirect sound appear to be a potentially useful diagnostic tool.
Core–shell nanoparticles have attracted much attention in recent years due to their unique properties and their increasing importance in many technological and consumer products. However, the chemistry of nanoparticles is still rarely investigated in comparison to their size and morphology. In this review, the possibilities, limits, and challenges of X-ray photoelectron spectroscopy (XPS) for obtaining more insights into the composition, thickness, and homogeneity of nanoparticle coatings are discussed with four examples: CdSe/CdS quantum dots with a thick coating and a small core; NaYF4-based upconverting nanoparticles with a large Yb-doped core and a thin Er-doped coating; and two types of polymer nanoparticles with a poly(tetrafluoroethylene) core with either a poly(methyl methacrylate) or polystyrene coating. Different approaches for calculating the thickness of the coating are presented, like a simple numerical modelling or a more complex simulation of the photoelectron peaks. Additionally, modelling of the XPS background for the investigation of coating is discussed. Furthermore, the new possibilities to measure with varying excitation energies or with hard-energy X-ray sources (hard-energy X-ray photoelectron spectroscopy) are described. A discussion about the sources of uncertainty for the determination of the thickness of the coating completes this review.
Iron nitride (Fe3N) and iron carbide (Fe3C) nanoparticles can be prepared via sol−gel synthesis. While sol−gel methods are simple, it can be difficult to control the crystalline composition, i.e., to achieve a Rietveld-pure product. In a previous in situ synchrotron study of the sol−gel synthesis of Fe3N/Fe3C, we showed that the reaction proceeds as follows:
Fe3O4 → FeOx → Fe3N → Fe3C. There was considerable overlap between the different phases, but we were unable to ascertain whether this was due to the experimental setup (side-on heating of a quartz capillary which could lead to thermal gradients) or whether individual particle reactions proceed at different rates. In this paper, we use in situ wide- and small-angle X-ray scattering (wide-angle X-ray scattering (WAXS) and small-angle X-ray scattering (SAXS)) to demonstrate that the overlapping phases are indeed due to variable reaction rates. While the initial oxide nanoparticles have a small range of diameters, the size range expands considerably and very rapidly during the oxide−nitride transition. This has implications for the isolation of Rietveld-pure Fe3N, and in an extensive laboratory study, we were indeed unable to isolate phasepure Fe3N. However, we made the surprising discovery that Rietveld-pure Fe3C nanoparticles can be produced at 500 °C with a sufficient furnace dwell time. This is considerably lower than the previous reports of the sol−gel synthesis of Fe3C nanoparticles.
Towards Unbiased Evaluation of Ionization Performance in LC-HRMS Metabolomics Method Development
(2022)
As metabolomics increasingly finds its way from basic science into applied and regulatory environments, analytical demands on nontargeted mass spectrometric detection methods continue to rise. In addition to improved chemical comprehensiveness, current developments aim at enhanced robustness and repeatability to allow long-term, inter-study, and meta-analyses. Comprehensive metabolomics relies on electrospray ionization (ESI) as the most versatile ionization technique, and recent liquid chromatography-high resolution mass spectrometry (LC-HRMS) instrumentation continues to overcome technical limitations that have hindered the adoption of ESI for applications in the past. Still, developing and standardizing nontargeted ESI methods and instrumental setups remains costly in terms of time and required chemicals, as large panels of metabolite standards are needed to reflect biochemical diversity. In this paper, we investigated in how far a nontargeted pilot experiment, consisting only of a few measurements of a test sample dilution series and comprehensive statistical analysis, can replace conventional targeted evaluation procedures. To examine this potential, two instrumental ESI ion source setups were compared, reflecting a common scenario in practical method development. Two types of feature evaluations were performed, (a) summary statistics solely involving feature intensity values, and (b) analyses additionally including chemical interpretation.
Results were compared in detail to a targeted evaluation of a large metabolite standard panel. We reflect on the advantages and shortcomings of both strategies in the context of current harmonization initiatives in the metabolomics field.
A brief introduction is given into our data collection and organization procedure, and why we have settled on the HDF5-based NeXus format for describing experimental data.
The links between NeXus and the SciCat data catalog is also provided, showing how the NeXus metadata is automatically added as searchable metadata in the catalog.
Digitalisierung in der Konformitätsbewertung - Einblicke aus unserer internationalen QI-FoKuS-Studie
(2022)
Die Präsentation enthält Informationen zur internationalen Studie der BAM zum Thema Digitalisierung in der Konformitätsbewertung, insbesondere erste Ergebnisse aus Deutschland. Sie zeigt u.a. die digitale Reife der teilnehmenden Prüflaboratorien und Zertifizierungsstellen sowie technologische Trends.
Core-shell (CS) particles have been increasingly used for a wide range of applications due to their unique properties by merging individual characteristics of the core and the shell materials. The functionality of the designed particles is strongly influenced by their surface roughness. Quantitative evaluation of the roughness of CS microparticles is, however, a challenging task for Scanning Electron Microscopy (SEM).
The SEM images contain two-dimensional (2D) information providing contour roughness data only from the projection of the particle in the horizontal plane. This study presents a practical procedure to achieve more information by tilting the sample holder, hence allowing images of different areas of a single particle to be recorded at different orientations under the same view angle. From the analysis of these images, quasi three-dimensional (3D) information is obtained.
Three types of home-made particles were investigated: i) bare polystyrene (PS) particles, ii) PS particles decorated with a first magnetic iron oxide (Fe3O4) nanoparticle shell forming CS microbeads, iii) PS/Fe3O4 particles closed with a second silica (SiO2) shell forming core-shell-shell (CSS) microbeads. A series images of a single particle were taken with stepwise tilted sample holder up to 10° by an SEM with high-resolution and surface sensitive SE-InLens® mode.
A reliable analysis tool has been developed by a script in Python to analyze SEM images automatically and to evaluate profile roughness quantitatively, for individual core-shell microparticles. Image analysis consists of segmentation of the images, identifying contour and the centre of the particle, and extracting the root mean squared roughness value (RMS-RQ) of the contour profile from the particle projection within a few seconds.
The variation in roughness from batch-to-batch was determined with the purpose to set the method as a routine quality check procedure. Flow cytometry measurements provided complementary data. Measurement uncertainties associated to various particle orientations were also estimated.
Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce ‘ad hoc learning of fragmentation’ (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94%.
Hundreds of papyrus rolls, carbonized during the 79CE eruption of Mount Vesuvius, were discovered in 1754 at Herculaneum. Sophisticated mechanical methods for unrolling the best-preserved scrolls have been applied, with varying success. However, such processes have been abandoned, to prevent risk from irremediable damage or loss and to preserve the integrity of the extremely fragile rolls. Following the development of X-ray based non-invasive techniques, attempts to virtually unroll the scrolls were made. The most common ink in Antiquity was carbon-based, and the main element of carbonized papyrus is carbon, making these investigations difficult. However, some attempts with synchrotron X-ray phase-contrast tomography (XPCT) were successful. Recently, the identification of antique inks containing metals raised hope that if some of the inks contain metal the rolls can be virtually unrolled using conventional CT- technique. We investigated the inks of a selection of partially unrolled fragments stored at the Biblioteca Nazionale di Napoli with X-ray fluorescence in order to select the best candidates for tomography. Despite the many difficulties (analysis of several layers sticking together, letters barely visible, difficulty to separate contribution from the ink and from the papyrus, inhomogeneity of the support, fragility of the fragments…), encouraging results were found, with a number of inks from Greek fragments found to contain additions to the soot (Fe, Pb, Cu P).
The simultaneous detection of different analytes has gained increasing importance in recent years, especially in the fields of medical diagnostics and environmental analysis. Multiplex assays allow for a range of biomarkers or pollutants to be rapidly and simultaneously measured. Particularly suitable formats for multiplexing are bead-based assays. The beads employed need to fulfil size and density requirements, important for instance for flow cytometry, and shall exhibit a high modularity to be facilely adapted to various kinds of analytes and detection systems. Core/shell particles are ideally suited in this sense because of their modularity in design and adaptability for various (bio)analytical assays. Here, polystyrene particles coated with different kinds of shells are presented, possessing features that are useful for a multitude of assay formats.
The particles in focus were coated with mesoporous and non-porous silica shells, with the possibility to introduce magnetic features to facilitate easier handling dependent on the assay format (e.g., in microfluidics). With high throughput analyses in mind, cytometric model assays were developed. Different factors such as pH or mediator salt used during shell preparation were evaluated with shell inspection by electron microscopy (SEM/TEM/EDX) being key to architectural control of the monodisperse particles. The optimized core/shell particles can be functionalized with capture biomolecules for toxins, viruses, or drugs to demonstrate particle performance. Showing how tailoring of the shell’s surface area controls sensitivity and dynamic range of the assay, an antibody-based assay for the detection of mycotoxins and a multiplex assay for the determination of DNA from different human papilloma virus (HPV) lines were developed.
Background Due to physical coupling between mechanical stress and magnetization in ferromagnetic materials, it is assumed in the literature that the distribution of the magnetic stray field corresponds to the internal (residual) stress of the specimen.
The correlation is, however, not trivial, since the magnetic stray field is also influenced by the microstructure and the geometry of component. The understanding of the correlation between residual stress and magnetic stray field could help to evaluate the integrity of welded components.
Objective This study aims at understanding the possible correlation of subsurface and bulk residual stress with magnetic stray field in a low carbon steel weld. Methods The residual stress was determined by synchrotron X-ray diffraction (SXRD, subsurface region) and by neutron diffraction (ND, bulk region). SXRD possesses a higher spatial resolution than ND. Magnetic stray fields were mapped by utilizing high-spatial-resolution giant magneto resistance (GMR) sensors.
Results The subsurface residual stress overall correlates better with the magnetic stray field distribution than the bulk stress.
This correlation is especially visible in the regions outside the heat affected zone, where the influence of the microstructural features is less pronounced but steep residual stress gradients are present.
Conclusions It was demonstrated that the localized stray field sources without any obvious microstructural variations are associated with steep stress gradients. The good correlation between subsurface residual stress and magnetic signal indicates that the source of the magnetic stray fields is to be found in the range of the penetration depth of the SXRD measurements.
Changes in the global bulk and local structures, of three different barium compounds (BaZrO3, BaF2, and BaFCl),were induced by mechanical milling and followed using X-ray powder diffraction (PXRD), subsequent microstructure analysis, and 137Ba solid state NMR spectroscopy. Harder materials like BaZrO3 experience significantly higher structural changes upon milling than softer materials like BaF2. Moreover, soft materials with layered structures, like BaFCl, show a pronounced structural change during the milling process. By combining PXRD and solid state NMR, detailed information on the changes to the global and local structures were obtained, which are of interest for mechanochemical synthesis, mechanically treated catalysts or ionic conductors.
MALDI-TOF-MS-Based Identification of Monoclonal Murine Anti-SARS-CoV-2 Antibodies within One Hour
(2022)
During the SARS-CoV-2 pandemic, many virus-binding monoclonal antibodies have been developed for clinical and diagnostic purposes. This underlines the importance of antibodies as universal bioanalytical reagents. However, little attention is given to the reproducibility crisis that scientific studies are still facing to date. In a recent study, not even half of all research antibodies mentioned in publications could be identified at all. This should spark more efforts in the search for practical solutions for the traceability of antibodies. For this purpose, we used 35 monoclonal antibodies against SARS-CoV-2 to demonstrate how sequence-independent antibody identification can be achieved by simple means applied to the protein. First, we examined the intact and light chain masses of the antibodies relative to the reference material NIST-mAb 8671. Already half of the antibodies could be identified based solely on these two parameters. In addition, we developed two complementary peptide mass fingerprinting methods with MALDI-TOF-MS that can be performed in 60 min and had a combined sequence coverage of over 80%. One method is based on the partial acidic hydrolysis of the protein by 5 mM of sulfuric acid at 99 degrees C. Furthermore, we established a fast way for a tryptic digest without an alkylation step. We were able to show that the distinction of clones is possible simply by a brief visual comparison of the mass spectra. In this work, two clones originating from the same immunization gave the same fingerprints. Later, a hybridoma sequencing confirmed the sequence identity of these sister clones. In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0. This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context.
The dataset presented contains ultrasonic data recorded in pulse echo mode. The investigated specimen is made of the isotropic homogeneous material polyamide and has a drill hole of constant diameter running parallel to the surface, which was scanned in a point grid using an automatic scanner system. At each measuring position, a pitch-catch measurement was performed using a sampling rate of 2 MHz. The probes used are arrays consisting of a spatially separated receiving and in-phase transmitting unit. The transmitting and receiving sides each consist of 12 point-shaped single probes. These dry-point contact (DPC) probes operate according to the piezoelectric principle at nominal frequencies of 55 kHz (shear waves) and 100 kHz (longitudinal waves), respectively, and do not require a coupling medium. The measurements are performed with longitudinal (100 kHz) and transverse (55 kHz) waves with different geometric orientations of the probe on the measurement surface. The data presented in the article provide a valid source for evaluating reconstruction algorithms for imaging in the low-frequency ultrasound range.
The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer‐based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented
algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.
Natural zeolite clinoptilolite CLIN with a framework ratio of Si/Al ≥ 4 containing mainly potassium and calcium ions in its internal channel system was used as a starting material. The acidic HCLIN catalysts were prepared under soft conditions avoiding the use of environmental less benign mineral acids. The starting material was ion exchanged using a 0.2 M aqueous ammonium nitrate solution at a temperature 80 ◦C for 2 h. The obtained NH4CLIN was converted into the acid HCLIN catalyst by calcination at 300–600 ◦C. The obtained samples were characterized by XRD, FTIR, SEM/TEM, AAS, and EDX element mapping. The state of aluminium and silicon was studied by 27Al- and 29SiMAS NMR spectroscopy. The textural properties of the catalysts were investigated by nitrogen adsorption and desorption measurements. The Brønsted acidity of the HCLIN catalysts was studied by temperature-programmed decomposition of the exchanged ammonium ions releasing ammonia as well as 1H MAS NMR, {1H–27Al} Trapdor, and {1H–27Al}
Redor experiments. The strongly agglomerated samples were crystalline and thermally stable up to >500 ◦C. Although a part of the clinoptilolite framework is maintained up to 600 ◦C, a loss of crystallinity is already observed starting from 450 ◦C. The specific surface areas of the starting CLIN and ammonium exchanged NH4CLIN are low with ca. 26 m2/g. The pores are nearly blocked by the exchangeable cations located in the zeolite pores. The thermal decomposition of the ammonium ions by calcination at 400 ◦C causes an opening of the pore entrances and a markable increase in the specific micropore area and micropore volume to ca. 163 m2/g and 0.07 cm3/g, respectively. It decreases with further rising calcination temperature indicating some structural loss. The catalysts show a broad distribution of Brønsted acid sites (BS) ranging from weak to strong sites as indicated the
thermal decomposition of exchanged ammonium ions (TPDA). The ammonium ion decomposition leaving BS, i.e., H+ located at Al–O–Si framework bridges, starts at ≥250 ◦C. A part of the Brønsted sites is lost after calcination specifically at 500 ◦C. It is related to the formation of penta-coordinated aluminium at the expense of tetrahedral framework aluminium. The Brønsted sites are partially recreated after repeated ammonium ion exchange. The catalytic performance of the acidic HCLIN
catalysts was tested in the etherification of glycerol as a green renewable resource with different C1 -C4 alcohols. The catalysts are highly active in the etherification of glycerol, especially with alcohols containing the branched, tertiary alkyl groups. Highest activity is observed with the soft activated catalyst HCLIN300 (300 ◦C, temperature holding time: 1 min). A total of 78% conversion of glycerol to mono and di ether were achieved with tert-butanol at 140 ◦C after 4 h of reaction.
The mono- and di-ether selectivity were 75% and 25%, respectively. The catalyst can be reused.
Objectives: Using a murine model of multiple sclerosis, we previously showed that repeated administration of gadopentetate dimeglumine led to retention of gadolinium (Gd) within cerebellar structures and that this process was enhanced with inflammation. This study aimed to compare the kinetics and retention profiles of Gd in inflamed and healthy brains after application of the macrocyclic Gd-based contrast agent (GBCA) gadobutrol or the linear GBCA gadopentetate. Moreover, potential Gd-induced neurotoxicity was investigated in living hippocampal slices ex vivo.
Materials and Methods: Mice at peak of experimental autoimmune encephalomyelitis (EAE; n = 29) and healthy control mice (HC; n = 24) were exposed to a cumulative dose of 20 mmol/kg bodyweight of either gadopentetate dimeglumine or gadobutrol (8 injections of 2.5 mmol/kg over 10 days). Magnetic resonance imaging (7 T) was performed at baseline as well as at day 1, 10, and 40 post final injection (pfi) of GBCAs. Mice were sacrificed after magnetic resonance imaging and brain and blood Gd content was assessed by laser ablation-inductively coupled plasma (ICP)-mass spectrometry (MS) and ICP-MS, respectively. In addition, using chronic organotypic hippocampal slice cultures, Gd-induced neurotoxicity was addressed in living brain tissue ex vivo, both under control or inflammatory (tumor necrosis factor α [TNF-α] at 50 ng/μL) conditions.
Results: Neuroinflammation promoted a significant decrease in T1 relaxation times after multiple injections of both GBCAs as shown by quantitative T1 mapping of EAE brains compared with HC. This corresponded to higher Gd retention within the EAE brains at 1, 10, and 40 days pfi as determined by laser ablation-ICP-MS. In inflamed cerebellum, in particular in the deep cerebellar nuclei (CN), elevated Gd retention was observed until day 40 after last gadopentetate application (CN: EAE vs HC, 55.06 ± 0.16 μM vs 30.44 ± 4.43 μM). In contrast, gadobutrol application led to a rather diffuse Gd content in the inflamed brains, which strongly diminished until day 40 (CN: EAE vs HC, 0.38 ± 0.08 μM vs 0.17 ± 0.03 μM). The analysis of cytotoxic effects of both GBCAs using living brain tissue revealed an elevated cell death rate after incubation with gadopentetate but not gadobutrol at 50 mM. The cytotoxic effect due to gadopentetate increased in the presence of the inflammatory mediator TNF-α (with vs without TNF-α, 3.15% ± 1.18% vs 2.17% ± 1.14%; P = 0.0345).
Conclusions: In the EAE model, neuroinflammation promoted increased Gd retention in the brain for both GBCAs. Whereas in the inflamed brains, efficient clearance of macrocyclic gadobutrol during the investigated time period was observed, the Gd retention after application of linear gadopentetate persisted over the entire observational period. Gadopentetate but not gadubutrol appeared to be neurotoxic in an ex vivo paradigm of neuronal inflammation.
Atmospheric pressure plasmas interact in various physical ways with their surroundings. They release heat and generate charge carriers, which leads to two effects. The first effect is the generation of acoustic waves due to heat release, known as the thermoacoustic effect, and the second effect is the perturbation of the resting fluid provoked by the release of charge carriers, called “ionic wind”. The direct connection between the charge carrier production of the discharge arrangement and the surroundings also allows the detection of acoustic waves by tracking the electrical current of the arrangement.
This contribution introduces a multi-fluid model approach to describe the acoustic interaction of atmospheric plasmas. In addition, we present experimental results on commercially available and in-house fabricated discharge arrangements for either emitting or receiving acoustic waves.
Catalysts derived from pyrolysis of metal organic frameworks (MOFs) are promising candidates to replace expensive and scarce platinum-based electrocatalysts commonly used in polymer electrolyte membrane fuel cells. MOFs contain ordered connections between metal centers and organic ligands. They can be pyrolyzed into metal- and nitrogen-doped carbons, which show electrocatalytic activity toward the oxygen reduction reaction (ORR). Furthermore, metal-free heteroatom-doped carbons, such as N-F-Cs, are known for being active as well. Thus, a carbon material with Co-N-F doping could possibly be even more promising as ORR electrocatalyst. Herein, we report the mechanochemical synthesis of two polymorphs of a zeolitic imidazole framework, Co-doped zinc 2-trifluoromethyl-1H-imidazolate (Zn0.9Co0.1(CF3-Im)2). Time-resolved in situ X-ray diffraction studies of the mechanochemical formation revealed a direct conversion of starting materials to the products. Both polymorphs of Zn0.9Co0.1(CF3-Im)2 were pyrolyzed, yielding Co-N-F containing carbons, which are active toward electrochemical ORR.
Formation Mechanism of a Nano-Ring of Bismuth Cations and Mono-Lacunary Keggin-Type Phosphomolybdate
(2022)
A new hetero-bimetallic polyoxometalate (POM) nano-ring was synthesized in a one-pot procedure. The structure consists of tetrameric units containing four bismuth-substituted monolacunary Keggin anions including distorted [BiO8] cubes. The nano-ring is formed via self-assembly from metal precursors in aqueous acidic medium. The compound (NH4)16[(BiPMo11O39)4] ⋅ 22 H2O; (P4Bi4Mo44) was characterized by single-crystal X-ray diffraction, extended X-ray absorption fine structure spectroscopy (EXAFS), Raman spectroscopy, matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-TOF), and thermogravimetry/differential scanning calorimetry mass spectrometry (TG-DSC-MS). The formation of the nano-ring in solution was studied by time-resolved in situ small- and wide-angle X-ray scattering (SAXS/WAXS) and in situ EXAFS measurements at the Mo−K and the Bi−L3 edge indicating a two-step process consisting of condensation of Mo-anions and formation of Bi−Mo-units followed by a rapid self-assembly to yield the final tetrameric ring structure.
This article presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super-resolution (SR) imaging. The photothermal SR approach is a well-known technique to overcome the spatial resolution limitation in photothermal imaging by extracting high-frequency spatial components based on the deconvolution with the thermal point spread function (PSF). However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, handcrafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, this article proposes Photothermal-SR-Net, which performs deconvolution by deep unfolding considering the underlying physics. Since defects appear sparsely in materials, our approach includes trained block-sparsity thresholding in each convolutional layer. This enables to super-resolve 2-D thermal images for nondestructive testing (NDT) with a substantially improved convergence rate compared to classic approaches. The performance of the proposed approach is evaluated on various deep unfolding and thresholding approaches. Furthermore, we explored how to increase the reconstruction quality and the computational performance. Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.
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.
Lake Magadi, East African Rift Valley, is a hyperalkaline and saline soda lake highly enriched in Na+, K+, CO32–, Cl–, HCO3–, and SiO2 and depleted in Ca2+ and Mg2+, where thick evaporite deposits and siliceous sediments have been forming for 100 000 years. The hydrogeochemistry and the evaporite deposits of soda lakes are subjects of growing interest in paleoclimatology, astrobiology, and planetary sciences. In Lake Magadi, different hydrates of sodium carbonate/bicarbonate and other saline minerals precipitate. The precipitation sequence of these minerals is a key for understanding the hydrochemical evolution, the paleoenvironmental conditions of ancient evaporite deposits, and industrial crystallization. However, accurate determination of the precipitation sequence of these minerals was challenging due to the dependency of the different hydrates on temperature, water activity, pH and pCO2, which could induce phase transformation and secondary mineral precipitation during sample handling. Here, we report a comprehensive methodology applied for monitoring the evaporitic mineral precipitation and hydrochemical evolution of Lake Magadi. Evaporation and mineral precipitations were monitored by using in situ video microscopy and synchrotron X-ray diffraction of acoustically levitated droplets. The mineral patterns were characterized by ex situ Raman spectroscopy, X-ray diffraction, and scanning electron microscopy. Experiments were coupled with thermodynamic models to understand the evaporation and precipitation-driven hydrochemical evolution of brines. Our results closely reproduced the mineral assemblages, patterns, and textural relations observed in the natural setting. Alkaline earth carbonates and fluorite were predicted to precipitate first followed by siliceous sediments. Among the salts, dendritic and acicular trona precipitate first via fractional crystallization─reminiscent of grasslike trona layers of Lake Magadi. Halite/villiaumite, thermonatrite, and sylvite precipitate sequentially after trona from residual brines depleted in HCO3–. The precipitation of these minerals between trona crystals resembles the precipitation process observed in the interstitial brines of the trona layers. Thermonatrite precipitation began after trona equilibrated with the residual brines due to the absence of excess CO2 input. We have shown that evaporation and mineral precipitation are the major drivers for the formation of hyperalkaline, saline, and SiO2-rich brines. The discrepancy between predicted and actual sulfate and phosphate ion concentrations implies the biological cycling of these ions. The combination of different in situ and ex situ methods and modeling is key to understanding the mineral phases, precipitation sequences, and textural relations of modern and ancient evaporite deposits. The synergy of these methods could be applicable in industrial crystallization and natural brines to reconstruct the hydrogeochemical and hydroclimatic conditions of soda lakes, evaporite settings, and potentially soda oceans of early Earth and extraterrestrial planets.
Due to the diffusive nature of heat propagation in solids, the detection and resolution of internal defects with active thermography based non-destructive testing is commonly limited to a defect-depth-to-defect-size ratio greater than or equal to one. In the more recent past, we have already demonstrated that this limitation can be overcome by using a spatially modulated illumination source and photothermal super resolution-based reconstruction. Furthermore, by relying on compressed sensing and computational imaging methods we were able to significantly reduce the experimental complexity to make the method viable for investigating larger regions of interest. In this work we share our progress on improving the defect/inhomogeneity characterization using fully 2D spatially structured illumination patterns instead of scanning with a single laser spot. The experimental approach is based on the repeated blind pseudo-random illumination using modern projector technology and a high-power laser. In the subsequent post-processing, several measurements are then combined by taking advantage of the joint sparsity of the defects within the sample applying 2D-photothermal super resolution reconstruction. Here, enhanced nonlinear convex optimization techniques are utilized for solving the underlying ill-determined inverse problem for typical simple defect geometries. As a result, a higher resolution defect/inhomogeneity map can be obtained at a fraction of the measurement time previously needed.
Sialic acid (SA) is a monosaccharide usually linked to the terminus of glycan chains on the cell surface. It plays a crucial role in many biological processes, and hypersialylation is a common feature in cancer. Lectins are widely used to analyze the cell surface expression of SA.
However, these protein molecules are usually expensive and easily denatured, which calls for the development of alternative glycan-specific receptors and cell imaging technologies. In this study, SA-imprinted fluorescent core-shell molecularly imprinted polymer particles (SA-MIPs) were employed to recognize SA on the cell surface of cancer cell lines. The SA-MIPs improved suspensibility and scattering properties compared with previously used core-shell SA-MIPs. Although SA-imprinting was performed using SA without preference for the alpha-2,3- and alpha-2,6-SA forms, we screened the cancer cell lines analyzed using the lectins Maackia Amurensis Lectin I (MAL I, alpha-2,3-SA) and Sambucus Nigra Lectin (SNA, alpha-2,6-SA). Our results show that the selected cancer cell lines in this study presented a varied binding behavior with the SA-MIPs. The binding pattern of the lectins was also demonstrated. Moreover, two different pentavalent SA conjugates were used to inhibit the binding of the SA-MIPs to breast, skin, and lung cancer cell lines, demonstrating the specificity of the SA-MIPs in both flow cytometry and confocal fluorescence microscopy. We concluded that the synthesized SA-MIPs might be a powerful future tool in the diagnostic analysis of various cancer cells.
Additively manufactured (and in particular laser powder bed fused) materials represent a manyfold challenge for the materials scientist and engineer because of their distinctive microstructure. If laser powder bed fusion is used to produce components, the complexity level increases because meso-structures (e.g., overhanging features, surface and internal defects) gain importance. Furthermore, if the main advantage of additive manufacturing, i.e., the freedom of design, is to be fully exploited, and geometrically complex structures, such as lattices, are manufactured, then such structures become meta-materials. This means that the geometry and the materials properties become equally important.
This matryoshka-like (more literary than the dry “multi-scale”) complexity makes the characterization of the residual stress fields by means of diffraction methods so difficult with the current means, that new paradigms are necessary to tackle the challenge.
Indeed, classic open problems acquire an extra layer of difficulty, such that new solutions need to be found and the sometimes-dormant debate needs to be re-opened. Examples include the determination of: a- the unstrained reference: this reference can become location-dependent and needs to be carefully determined; b- the so-called diffraction elastic constants, which becomes immensely challenging since even the single-crystal elastic constants are not known for additively manufactured materials.
On top of this, other problems arise. The determination of the principal axes of stress becomes non-trivial because the hatching strategy sometimes dominates over the sample geometry. Even further, in complex structures, such as lattices, the textbook statement that the strain measurement in six independent directions uniquely identifies the strain tensor becomes simply invalid. The peculiar surface features of additively manufactured materials transform trivial tasks into formidable challenges: the precise alignment of a specimen in a beam or the determination of surface stresses with laboratory X-rays need to be thoroughly re-discussed and lay far from being routine tasks.
In this paper, we will show a few examples of the cases mentioned above. We will demonstrate that sometimes the classic approach works very well, but other times surprising conclusions can be drawn from in-depth studies of the residual stress in additively manufactured materials. In short, we predicate that classic methods cannot be used on additively manufactured materials and structures without a critical evaluation of their validity and application range.
Near-infrared (NIR) spectroscopy is a promising candidate for low-cost, nondestructive, and high-throughput mass quantification of micro¬plastics in environmental samples. Widespread application of the technique is currently hampered mainly by the low sensitivity of NIR spectroscopy compared to thermo-analytical approaches commonly used for this type of analysis. This study shows how the application of NIR spectroscopy for mass quantification of microplastics can be extended to smaller analyte levels by combining it with a simple and rapid microplastic enrichment protocol. For this purpose, the widely used flotation of microplastics in a NaCl solution, accelerated by centrifugation, was chosen which allowed to remove up to 99 % of the matrix at recovery rates of 83–104 %. The spectroscopic measurements took place directly on the stainless-steel filters used to collect the extracted particles to reduce sample handling to a minimum. Partial least squares regression (PLSR) models were used to identify and quantify the extracted microplastics in the mass range of 1–10 mg. The simple and fast extraction procedure was systematically optimized to meet the requirements for the quantification of microplastics from common PE-, PP-, and PS-based packaging materials with a particle size < 1 mm found in compost or soils with high natural organic matter content (> 10 % determined by loss on ignition). Microplastics could be detected in model samples at a mass fraction of 1 mg g-1. The detectable microplastic mass fraction is about an order of magnitude lower compared to previous studies using NIR spectroscopy without additional enrichment. To emphasize the cost-effectiveness of the method, it was implemented using some of the cheapest and most compact NIR spectrometers available.
In the last few years, the use of machine learning has emerged in the field of distributed fiber optic sensors as a promising approach to enhance their performance and provide new capabilities. In this study, we use machine learning for simultaneous measurements of temperature and humidity in polyimide (PI)-coated optical fibers based on Brillouin Brillouin optical frequency domain analysis (BOFDA). Different non-linear machine learning algorithms are employed, namely polynomial regression, decision trees and artificial neural networks (ANNs), and their discrimination performance is benchmarked against that of the conventional linear regression. The performance is evaluated using leave-one-out cross-validation to ensure that the models are reliable and able to generalize well on new data. We show that nonlinear machine learning algorithms outperform the conventional linear regression and thus could pave the way towards simultaneous cost-effective temperature and humidity distributed sensing, which has the potential to find attractive new applications in the field of civil and geotechnical engineering, from structural health monitoring of dikes and bridges to subsea cables and long pipelines corrosion detection.
Distributed humidity fiber-optic sensor based on BOFDA using a simple machine learning approach
(2022)
We report, to our knowledge for the first time, on distributed relative humidity sensing in silica polyimide-coated optical fibers using Brillouin optical frequency domain analysis (BOFDA). Linear regression, which is a simple and well-interpretable algorithm in machine learning and statistics, is utilized. The algorithm is trained using as features the Brillouin frequency shifts and linewidths of the fiber’s multipeak Brillouin spectrum. To assess and improve the effectiveness of the regression algorithm, we make use of machine learning concepts to estimate the model’s uncertainties and select the features that contribute most to the model’s performance. In addition to relative humidity, the model is also able to simultaneously provide distributed temperature information addressing the well-known cross-sensitivity effects.
Quality assessment of components produced by metal based additive manufacturing (AM) technologies such as laser powder bed fusion is rising in importance due to the increased use of AM in industrial production. Here, the presence of internal porosity was identified as a limiting factor for the final component quality. The utilization of thermography as an in-situ monitoring technique allows the determination of the part’s thermal history which was found to be connected to the porosity formation [1]. Combining the local thermal information derived from thermography with the porosity information obtained by x-ray micro computed tomography, machine learning algorithms can be utilized to predict the porosity distribution in the part. In this study, a first approach for the prediction of keyhole porosity in a cylindric specimen from AISI 316L stainless steel is presented. It is based on data augmentation using the “SmoteR” algorithm [2] to cure the dataset imbalance and a 1-dimensional convolutional neural network.
[1] C.S. Lough et al., Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared thermal feature porosity probability maps. Journal of Materials Processing Technology, 302, p. 117473 (2022)
https://dx.doi.org/10.1016/j.imatprotec.2021.117473
[2] L. Torgo et al., SMOTE for Regression. Progress in Artificial Intelligence, Chapter 33, p. 378-289 (2013)
https://dx.doi.org/10.1007/978-3-642-40669-0_33
Atmospheric pressure plasmas interact in various physical ways with their surroundings. They release heat and generate charge carriers, which leads to two effects. The first effect is the generation of acoustic waves due to heat release, known as the thermoacoustic effect, and the second effect is the perturbation of the resting fluid provoked by the release of charge carriers, called “ionic wind”. The direct connection between the charge carrier production of the discharge arrangement and the surroundings also allows the detection of acoustic waves by tracking the electrical current of the arrangement.
This contribution introduces a multi-fluid model approach to describe the acoustic interaction of atmospheric plasmas. In addition, we present experimental results on commercially available and in-house fabricated discharge arrangements for either emitting or receiving acoustic waves.
The molecular ruby analogue [Cr(ddpd)2]3+ (ddpd=N,N’-dimethyl-N,N’-dipyridine-2-ylpyridine-2,6-diamine) exhibits near infrared (NIR) emission with a high photoluminescence (PL) quantum yield ΦPL of 11 % and a lifetime of 898 μs in deaerated water at room temperature. While ligand-based control of the photophysical properties has received much attention, influences of the counter anions and microenvironment are still underexplored. In this study, the luminescence properties of the molecular ruby were systematically examined for the counter anions Cl−, Br−, [BF4]−, [PF6]−, [BPh4]−, and [BArF24]− in acetonitrile (MeCN) solution, in crystals, and embedded into polystyrene nanoparticles (PSNP). Stern-Volmer analyses of the oxygen quenching studies in the intensity and lifetime domain showed the highest oxygen sensitivity of the complexes with the counter anions of [BF4]− and [BArF24]−, which also revealed the longest luminescence lifetimes. Embedding [Cr(ddpd)2][PF6]3 in PSNPs and shielding with poly(vinyl alcohol) yields a strongly NIR-emissive oxygen-insensitive material with a record ΦPL of 15.2 % under ambient conditions.
Sensitized triplet-triplet annihilation upconversion (sTTA-UC) mainly relies on precious metal complexes thanks to their high intersystem crossing (ISC) efficiencies, excited state energies, and lifetimes, while complexes of abundant first-row transition metals were only rarely utilized and with often moderate UC quantum yields. [Cr(bpmp)2]3+ (bpmp = 2,6-bis(2-pyridylmethyl)pyridine) containing earth-abundant chromium possesses an absorption band suitable for green light excitation, a doublet excited state energy matching the triplet energy of 9,10-diphenyl anthracene (DPA), a close to millisecond excited state lifetime, and high photostability. Combined ISC and doublet-triplet energy transfer from excited [Cr(bpmp)2]3+ to DPA gives 3DPA with close-to-unity quantum yield. TTA of 3DPA furnishes greento-blue UC with a quantum yield of 12.0 % (close to the theoretical maximum). Sterically less-hindered anthracenes undergo a [4+4] cycloaddition with [Cr(bpmp)2]3+ and green light.
The prerequisites for a successful energy transition and the economic use of hydrogen as a clean green energy carrier and for H2 readiness are a rapid market ramp-up and the establishment of the required value chains. Reliable quality and safety standards for innovative technologies are the prerequisite for ensuring supply security, for environmental compatibility and sustainable climate protection, for building trust in these technologies and thus enable product and process innovations.
With the Competence Centre "H2Safety@BAM", BAM is creating the safety-related prere-quisites for the successful implementation of hydrogen technologies at national as well as European level. BAM uses decades of experience in dealing with hydrogen technologies to develop the necessary quality and safety standards.
The presentation will draw a bow from the typical basic tasks of BAM in the field of competence "Sensors, analytics and certified reference materials", such as maintenance and dissemination of the national gas composition standards for calorific value determination as Designated Institute for Metrology in Chemistry within the framework of the Metre Convention, to the further development of measurement and sensor technology for these tasks. For the certification of reference materials, a mostly slow and time-consuming but solid reference analysis is common. With hydrogen and its special properties, completely new requirements are added. In addition, fast and simple online analysis is required for process control, for example to register quality changes, e.g., during load changes or refuelling processes.
Dust deposition is an important source of phosphorus (P) to many ecosystems. However, there is little evidence of dust-derived P-containing minerals in soils. Here we studied P forms along a well-described climatic gradient on Hawaii, which is also a dust deposition gradient. Soil mineralogy and soil P forms from six sites along the climatic gradient were analyzed with bulk (X-ray diffraction and P K-edge X-ray absorption near edge structure)
and microscale (X-ray fluorescence, P K-edge X-ray absorption near edge structure, and Raman) analysis methods. In the wettest soils, apatite grains ranging from 5 to 30 μm in size were co-located at the micro-scale with quartz, a known continental dust indicator suggesting recent atmospheric deposition. In addition to colocation with quartz, further evidence of dust-derived P included backward trajectory modeling indicating that dust particles could be brought to Hawaii from the major global dust-loading areas in central Asia and northern Africa. Although it is not certain whether the individual observed apatite grains were derived from long-distance transport of dust, or from local dust sources such as volcanic ash or windblown fertilizer, these observations offer direct evidence that P-containing minerals have reached surface layers of highly-weathered grassland soils
through atmospheric deposition.
Fast and accurate determination of the protein content of a sample is an important and non-trivial task of many biochemical, biomedical, food chemical, pharmaceutical, and environmental research activities. Different methods of total protein determination are used for a wide range of proteins with highly variable properties in complex matrices. These methods usually work reasonably well for proteins under controlled conditions, but the results for non-standard and complex samples are often questionable. Here, we compare new and well-established methods, including traditional amino acid analysis (AAA), aromatic amino acid analysis (AAAA) based on the amino acids phenylalanine and tyrosine, reversed-phase liquid chromatography of intact proteins with UV absorbance measurements at 220 and 280 nm (LC-220, LC-280), and colorimetric assays like Coomassie Blue G-250 dye-binding assay (Bradford) and bicinchoninic acid (BCA) assay. We investigated different samples, including proteins with challenging properties, chemical modifications, mixtures, and complex matrices like air particulate matter and pollen extracts. All methods yielded accurate and precise results for the protein and matrix used for calibration. AAA, AAAA with fluorescence detection, and the LC-220 method yielded robust results even under more challenging conditions (variable analytes and matrices). These methods turned out to be well-suited for reliable determination of the protein content in a wide range of samples, such as air particulate matter and pollen.
The diffusive gradients in thin-films (DGT) technique shows in many publications a superior correlation to the amount of plant-available phosphorus (P) in soil. However, this technique cannot give information on the plant-available P species in soil. Therefore, we combined DGT with solution 31P nuclear magnetic resonance (NMR) spectroscopy. This was achieved by using a modified DGT device in which the diffusive layer had a larger pore size, the binding layer incorporated an adsorption material with a higher capacity, and the device had a larger exposure area. The spectroscopic investigation was undertaken after elution of the deployed DGT binding layer in a NaOH solution. Adsorption tests using solutions of known organic P compounds showed that a sufficient amount of these compounds could be adsorbed on the binding layer in order for them to be analyzed by solution 31P NMR spectroscopy. Furthermore, various intermediates of the hydrolysis of trimetaphosphate in soil could be also analyzed over time.