540 Chemie und zugeordnete Wissenschaften
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In this paper we present first-order reversal curve (FORC) diagrams of ensembles of three-dimensional Co3Fe nanostructures as 2 × 2 arrays of nano-cubes and nano-trees. The structures are fabricated and investigated by an advanced platform of focused electron beam induced deposition combined with high-resolution detection of magnetic stray fields using a home-built micro-Hall magnetometer based on an AlGaAs/GaAs heterostructure. The experimental FORC diagrams are compared to macrospin simulations for both geometries at different angles of the externally applied magnetic field. The measured FORC diagrams are in good agreement with the simulated ones and reflect non-uniform magnetization reversal dominated by multi-vortex states within, and strong magnetic coupling between, the building blocks of our nanostructures. Thus, a FORC analysis of small arrays of 3D magnetic nanostructures provides more detailed insights into the mechanisms of magnetization reversal beyond standard major hysteresis loop measurements.
A novel thiazol‐based ratiometric dye for the detection of local pH values is synthesized, and its properties are characterized by a combination of optical spectroscopy, solid‐state NMR and DNP (dynamic nuclear polarization)‐enhanced solid‐state NMR. This novel dye covers a completely different sensitivity range with its acidic pKa value of 3.5 compared to other established dyes for ratiometric pH detection, such as SNARF. The dye is grafted to the surfaces of mesoporous silica materials, which enables, for the first time, direct in situ measurements of the local pH values in silica mesopores by a simple UV‐vis spectroscopy method. The obtained results, which are in good agreement with previous indirect techniques, indicate a background electrolyte‐dependent pKa shift of at least one pH unit under nanoconfined conditions compared to the pKa of the dye in bulk solution.
CO2 has been electrochemically reduced to the intermediate formate, which was subsequently used as sole substrate for the production of the polymer polyhydroxybutyrate (PHB) by the microorganism Cupriavidus necator. Faradaic efficiencies (FE) up to 54 % have been reached with Sn‐based gas‐diffusion electrodes in physiological electrolyte. The formate containing electrolyte can be used directly as drop‐in solution in the following biological polymer production by resting cells. 56 mg PHB L−1 and a ratio of 34 % PHB per cell dry weight were achieved. The calculated overall FE for the process was as high as 4 %. The direct use of the electrolyte as drop‐in media in the bioconversion enables simplified processes with a minimum of intermediate purification effort. Thus, an optimal coupling between electrochemical and biotechnological processes can be realized.
Cell-free expression represents an attractive method to produce large quantities of selectively labeled protein for NMR applications. Here, cell-free expression was used to label specific regions of the growth hormone secretagogue receptor (GHSR) with NMR-active isotopes. The GHSR is a member of the class A family of G protein-coupled receptors. A cell-free expression system was established to produce the GHSR in the precipitated form. The solubilized receptor was refolded in vitro and reconstituted into DMPC lipid membranes. Methionines, arginines, and histidines were chosen for 13C-labeling as they are representative for the transmembrane domains, the loops and flanking regions of the transmembrane α-helices, and the C-terminus of the receptor, respectively. The dynamics of the isotopically labeled residues was characterized by solid-state NMR measuring motionally averaged 1H-13C dipolar couplings, which were converted into molecular order parameters. Separated local field DIPSHIFT experiments under magic-angle spinning conditions using either varying cross polarization contact times or direct excitation provided order parameters for these residues showing that the C-terminus was the segment with the highest motional amplitude. The loop regions and helix ends as well as the transmembrane regions of the GHSR represent relatively rigid segments in the overall very flexible receptor molecule. Although no site resolution could be achieved in the experiments, the previously reported highly dynamic character of the receptor concluded from uniformly 13C labeled receptor samples could be further specified by this segmental labeling approach, leading to a more diversified understanding. of the receptor dynamics under equilibrium conditions
Hepatocyte nuclear factor 4α (HNF4α) is a ligand-sensing transcription factor and presents as a potential drug target in metabolic diseases and cancer. In humans, mutations in the HNF4α gene cause maturity-onset diabetes of the young (MODY), and the elevated activity of this protein has been associated with gastrointestinal cancers. Despite the high therapeutic potential, available ligands and structure–activity relationship knowledge for this nuclear receptor are scarce. Here, we disclose a chemically diverse collection of orthogonally validated fragment-like activators as well as inverse agonists, which modulate HNF4α activity in a low micromolar range. These compounds demonstrate the druggability of HNF4α and thus provide a starting point for medicinal chemistry as well as an early tool for chemogenomics.
The layer‐by‐layer (LbL) method is a well‐established method for the growth of surface‐attached metal–organic frameworks (SURMOFs). Various experimental parameters, such as surface functionalization or temperature, have been identified as essential in the past. In this study, inspired by these recent insights regarding the LbL SURMOF growth mechanism, the impact of reactant solutions concentration on LbL growth of the Cu2(F4bdc)2(dabco) SURMOF (F4bdc2−=tetrafluorobenzene‐1,4‐dicarboxylate and dabco=1,4‐diazabicyclo‐[2.2.2]octane) in situ by using quartz‐crystal microbalance and ex situ with a combination of spectroscopic, diffraction and microscopy techniques was investigated. It was found that number, size, and morphology of MOF crystallites are strongly influenced by the reagent concentration. By adjusting the interplay of nucleation and growth, we were able to produce densely packed, yet thin films, which are highly desired for a variety of SURMOF applications.
Despite the great interest in glycoproteins, structural information reporting on conformation and dynamics of the sugar moieties are limited. We present a new biochemical method to express proteins with glycans that are selectively labeled with NMR‐active nuclei. We report on the incorporation of 13C‐labeled mannose in the C‐mannosylated UNC‐5 thrombospondin repeat. The conformational landscape of the C‐mannose sugar puckers attached to tryptophan residues of UNC‐5 is characterized by interconversion between the canonical 1C4 state and the B03 / 1S3 state. This flexibility may be essential for protein folding and stabilization. We foresee that this versatile tool to produce proteins with selectively labeled C‐mannose can be applied and adjusted to other systems and modifications and potentially paves a way to advance glycoprotein research by unravelling the dynamical and conformational properties of glycan structures and their interactions.
Safety requirements and the need of low‐migration UV inks have received increasing attention in the packaging industry. Crucial for the development and design of low‐migration UV inkjet inks for migration‐sensitive applications is the polymerization degree. In this study, curing‐behavior of a black, high purity packaging ink (HPP‐ink) was monitored using ATR‐FTIR spectroscopy. UV irradiation of HPP‐ink led to changes in specific absorption bands of the FTIR spectra due to crosslinking reaction of double bonds. Changes in absorptions bands at 1,408 and 1,321 cm−1 permitted the determination of CC conversion of acrylic and vinyl double bond, independently of one another. In addition, a method was developed which allows the investigation of surface‐cure and deep‐cure behavior, separately.
In biological systems (cell culture media, cells, body fluids), drugs/toxicants are usually not freely dissolved but partially bound to biomolecules; only a fraction of the chemical is free/unbound (fu). To predict pharmacological effects and toxicity, it is important that the fu of the drug is known. As the differences between free and nominal concentrations are determined by test system parameters (e.g., the protein and lipid content, and the type of surface material), comparison of nominal concentrations between two different new approach methods (NAM) may lead to faulty conclusions. The same problem exists when in vitro concentrations are compared to those in human subjects. Therefore, the respective fu of a chemical in a test system needs to be determined for in vitro-to-in vivo extrapolations (IVIVE). Besides direct measurements, prediction models can help to obtain fu. Here we describe a simplified approach to approximate fu and provide background information on the underlying assumptions. Comparative predictions and measurements of fu of various drugs are shown to exemplify the approach. Basic input data, like protein and lipid concentrations, are also provided. Beyond such test systems data, the only required chemical-specific inputs are the lipophilicity of the candidate drug and its ionization state, as determined by the dissociation constants of its acidic or basic groups. This overview is intended to be used by any lab scientist without specific toxicokinetics training to obtain an estimate of fu in a given cell culture medium.
This study proposes a novel multi-network architecture consisting of a multi-scale convolution neural network (MSCNN) with fully connected graph convolution network (GCN), named MSCNN-GCN, for the detection of musculoskeletal abnormalities via musculoskeletal radiographs. To obtain both detailed and contextual information for a better description of the characteristics of the radiographs, the designed MSCNN contains three subnetwork sequences (three different scales). It maintains high resolution in each sub-network, while fusing features with different resolutions. A GCN structure was employed to demonstrate global structure information of the images. Furthermore, both the outputs of MSCNN and GCN were fused through the concat of the two feature vectors from them, thus making the novel framework more discriminative. The effectiveness of this model was verified by comparing the performance of radiologists and three popular CNN models (DenseNet169, CapsNet, and MSCNN) with three evaluation metrics (Accuracy, F1 score, and Kappa score) using the MURA dataset (a large dataset of bone X-rays). Experimental results showed that the proposed framework not only reached the highest accuracy, but also demonstrated top scores on both F1 metric and kappa metric. This indicates that the proposed model achieves high accuracy and strong robustness in musculoskeletal radiographs, which presents strong potential for a feasible scheme with intelligent medical cases.