Wissenschaftliche Artikel der BAM
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Paper des Monats
- ja (71)
A route to a ZrF4 catalyst active in room temperature Friedel–Crafts and dehydrofluorination reactions was developed via a fluorolytic sol–gel route, which was followed by a postfluorination step using a stream of CHClF2. The behaviour of different Zr(IV) precursors in a sol–gel reaction with anhydrous isopropanol/HF solution was investigated. The subsequent post-fluorination step was optimised in its temperature ramp and confirmed the necessity of a fluorination of the generated xerogels to obtain catalytic activity. The process is discussed in the context of the analysis of the materials using Brunauer–Emmett–Teller analysis (BET), powder X-ray diffraction (XRD), infrared spectroscopy (IR), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The local structure of the amorphous catalyst was elucidated by extended X-ray absorption fine structure spectroscopy (EXAFS).
Quaternary ammonium compounds (QACs) are widely used as active agents in disinfectants, antiseptics, and preservatives. Despite being in use since the 1940s, there remain multiple open questions regarding their detailed mode-of-action and the mechanisms, including phenotypic heterogeneity, that can make bacteria less susceptible to QACs. To facilitate studies on resistance mechanisms towards QACs, we synthesized a fluorescent quaternary ammonium compound, namely N-dodecyl-N,N-dimethyl-[2-[(4-nitro-2,1,3-benzoxadiazol-7-yl)amino]ethyl]azanium-iodide (NBD-DDA). NBD-DDA is readily detected by flow cytometry and fluorescence microscopy with standard GFP/FITC-settings, making it suitable for molecular and single-cell studies. As a proof-of-concept, NBD-DDA was then used to investigate resistance mechanisms which can be heterogeneous among individual bacterial cells. Our results reveal that the antimicrobial activity of NBD-DDA against Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa is comparable to that of benzalkonium chloride (BAC), a widely used QAC, and benzyl-dimethyl-dodecylammonium chloride (BAC12), a mono-constituent BAC with alkyl-chain length of 12 and high structural similarity to NBD-DDA. Characteristic time-kill kinetics and increased tolerance of a BAC tolerant E. coli strain against NBD-DDA suggest that the mode of action of NBD-DDA is similar to that of BAC. As revealed by confocal laser scanning microscopy (CLSM), NBD-DDA is preferentially localized to the cell envelope of E. coli, which is a primary target of BAC and other QACs. Leveraging these findings and NBD-DDA‘s fluorescent properties, we show that reduced cellular accumulation is responsible for the evolved BAC tolerance in the BAC tolerant E. coli strain and that NBD-DDA is subject to efflux mediated by TolC. Overall, NBD-DDA’s antimicrobial activity, its fluorescent properties, and its ease of detection render it a powerful tool to study resistance mechanisms of QACs in bacteria and highlight its potential to gain detailed insights into its mode-of-action.
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.
A first pilot study on the sorption of environmental pollutants on various microplastic materials
(2017)
With the drastic increase in plastic production, the input of plastic particles into the environment has become a recognised problem.
Xenobiotics are able to sorb to polymer materials, and this process is further enhanced where they Encounter microplastics (plastic fragments <5 mm). In this work we studied the sorption of metformin, a type-2 diabetes drug, and difenoconazole, a fungicide, onto the virgin polymer materials polyamide (PA), polypropylene (PP), and polystyrene (PS). Additionally, PP was cryo-milled and PA was treated with acid to investigate the influence of an increase in surface area and chemical modification. The material properties were also studied by dynamic scanning calorimetry (DSC), gel permeation chromatography (GPC) and Fourier transform infrared spectroscopy (FTIR). Sorption experiments were performed on the basis of a full factorial design examining the effect of agitation, pH value, and salinity. Experimental results showed that difenoconazole sorbs readily to all microplastics, whereas the more polar analyte metformin did not show any affinity to the materials used. For difenoconazole the governing factor in all cases is agitation, while both pH and salinity exhibited only a slight influence. The modification of polymers leads to enhanced sorption, indicating that an increase in surface area (cryo-milled PP) or inner volume (acid-treated PA) strongly favours adsorption. Moreover, long-term experiments demonstrated that the time until equilibrium is reached depends strongly on the particle size.
One essential step on the way towards accurate quantitative simulation of radiographic testing is an accurate description of the utilized energy spectrum of X-ray photons. For use in general purpose simulation tools, the spectra of X-ray tubes have to be described by a model covering at least the intended range of applications. This range includes transmission tubes as well as direct beam tubes with varying angles of incidence and emission, for a number of typical target materials. In radiographic testing acceleration voltages frequently reach up to 450 kV for direct beam targets and up to 225 kV for transmission targets, with even higher voltages available or being developed. Currently used models are unable to cover the whole range of configurations.
Here a model is presented that employs a unified approach for simulating the photon energy spectra for transmission and direct beam targets composed of arbi-trary homogeneous materials. In order to achieve this, a detailed model of electron transport within the target is employed. The validity of the developed model is shown through comparisons with Monte Carlo simulations as well as measurements for a number of different configurations.
A facile method to probe the vascular permeability of nanoparticles in nanomedicine applications
(2017)
The effectiveness of nanoparticles (NP) in nanomedicine depends on their ability to extravasate from vasculature towards the target tissue. This is determined by their permeability across the endothelial barrier. Unfortunately, a quantitative study of the diffusion permeability coefficients (Pd) of NPs is difficult with in vivo models. Here, we utilize a relevant model of vascular-tissue interface with tunable endothelial permeability in vitro based on microfluidics. Human umbilical vein endothelial cells (HUVECs) grown in microfluidic devices were treated with Angiopoietin 1 and cyclic adenosine monophosphate (cAMP) to vary the Pd of the HUVECs monolayer towards fluorescent polystyrene NPs (pNPs) of different sizes, which was determined from image analysis of their fluorescence intensity when diffusing across the monolayer. Using 70 kDa dextran as a probe, untreated HUVECs yielded a Pd that approximated tumor vasculature while HUVECs treated with 25 μg/mL cAMP had Pd that approximated healthy vasculature in vivo. As the size of pNPs increased, its Pd decreased in tumor vasculature, but remained largely unchanged in healthy vasculature, demonstrating a trend similar to tumor selectivity for smaller NPs. This microfluidic model of vascular-tissue interface can be used in any laboratory to perform quantitative assessment of the tumor selectivity of nanomedicine-based systems.
The site of Schöningen (Germany), dated to ca. 300,000 years ago, yielded the earliest large-scale record of humanly-made wooden tools. These include wooden spears and shorter double-pointed sticks, discovered in association with herbivores that were hunted and butchered along a lakeshore. Wooden tools have not been systematically analysed to the same standard as other Palaeolithic technologies, such as lithic or bone tools. Our multianalytical study includes micro-CT scanning, 3-dimensional microscopy, and Fourier transform infrared spectroscopy, supporting a systematic technological and taphonomic analysis, thus setting a new standard for wooden tool analysis. In illustrating the biography of one of Schöningen’s double-pointed sticks, we demonstrate new human behaviours for this time period, including sophisticated woodworking techniques. The hominins selected a spruce branch which they then debarked and shaped into an aerodynamic and ergonomic tool. They likely seasoned the wood to avoid cracking and warping. After a long period of use, it was probably lost while hunting, and was then rapidly buried in mud. Taphonomic alterations include damage from trampling, fungal attack, root damage and compression. Through our detailed analysis we show that Middle Pleistocene humans had a rich awareness of raw material properties, and possessed sophisticated woodworking skills. Alongside new detailed morphometrics of the object, an ethnographic review supports a primary function as a throwing stick for hunting, indicating potential hunting strategies and social contexts including for communal hunts involving children. The Schöningen throwing sticks may have been used to strategically disadvantage larger ungulates, potentially from distances of up to 30 metres. They also demonstrate that the hominins were technologically capable of capturing smaller fast prey and avian fauna, a behaviour evidenced at contemporaneous Middle Pleistocene archaeological sites.
In Near Edge X-Ray Absorption Fine Structure (NEXAFS) spectroscopy X-Ray photons are used to excite tightly bound core electrons to low-lying unoccupied orbitals of the system. This technique offers insight into the electronic structure of the system as well as useful structural information. In this work, we apply NEXAFS to two kinds of imidazolium based ionic liquids ([CnC₁im]⁺[NTf₂]⁻ and [C₄C₁im]⁺[I]⁻). A combination of measurements and quantum chemical calculations of C K and N K NEXAFS resonances is presented. The simulations, based on the transition potential density functional theory method (TP-DFT), reproduce all characteristic features observed by the experiment. Furthermore, a detailed assignment of resonance features to excitation centers (carbon or nitrogen atoms) leads to a consistent interpretation of the spectra.
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.
Physical adsorption at cryogenic temperature (cryoadsorption) is a reversible mechanism that can reduce the pressure of conventional compressed gas storage systems. Metal–organic framework (MOF) materials are remarkable candidates due to the combination of high specific surface area and density which, in some cases, provide a high volumetric storage capacity. However, such extensive use of MOFs for this application requires the selection of affordable structures, easy to produce and made from feasible metallic and organic components. Herein, we introduce a MOF database detailing the crystallographic and porous properties of 3600 existing MOFs made from industrially relevant metals and their organic composition. The comparison of the available minimum costs of linkers allowed the creation of a database to select affordable structures with high potential for volumetric hydrogen storage by cryoadsorption, considering their composition based on individual or mixed building blocks. A user interface, available online, facilitates the selection of MOFs based on the properties or names of structures and linkers.