We present a double multilayer monochromator (DMM) design which has been realized for the BAMline (BESSY‐II light source, Germany) as well as in an updated version for the TopoTomo beamline (ANKA light source. Germany) [1–4]. The latter contains two pairs of multilayer stripes in order to avoid absorption edges of the coating material. For both DMMs, the second multilayer offers a meridional bending option for beam compression to increase the available photon flux density. Each multilayer mirror is equipped with a vertical stage for height adjustments allowing for compensation of varying incoming beam heights and giving a certain flexibility choosing the offset. The second multilayer can be moved in the beam direction in order to cover the full energy range available. Furthermore, a white beam option is available.
We report three-dimensional (3D) direct imaging of complex surface–liquid interfaces by hard X-ray phase contrast tomography as a non-destructive approach for the morphological characterization of surfaces at the micro- and nanoscale in contact with water. Specifically, we apply this method to study the solid–air–water interface in hydrophobic macroporous polymethacrylate surfaces, and the solid–oil–water interface in slippery liquid-infused porous surfaces (SLIPS). Varying the isotropic spatial resolution allows the 3D quantitative characterization of individual polymer globules, globular clusters (porosity) as well as the infused lubricant layer on SLIPS. Surface defects were resolved at the globular level. We show the first application of X-ray nanotomography to hydrated surface characterizations and we anticipate that X-ray nanoscale imaging will open new ways for various surface/interface studies.
The size of 4D tomography datasets acquired at synchrotron or neutron imaging facilities can reach several terabytes, which presents a significant challenge for their evaluation. This paper presents a framework that allows a compressed dataset to be kept in memory and makes it possible to evaluate and manipulate the dataset without requiring enough memory to decompress the entire dataset. The framework enables the compensation of imaging artifacts, including the compression artifacts of the 4D dataset, through the integration of neural networks. The reduction of imaging artifacts can be performed at the imaging facility or at the user's home institution. This framework reduces the computational burden on the computing infrastructure of large synchrotron and neutron facilities by allowing end users to process datasets on their institution's computers. This is made possible by compressing TBs of data to less than 128 GB, allowing powerful PCs to process TBs of 4D tomography data.
Industrial biocatalysis plays an important role in the development of a sustainable economy, as enzymes can be used to synthesize an enormous range of complex molecules under environmentally friendly conditions. To further develop the field, intensive research is being conducted on process technologies for continuous flow biocatalysis in order to immobilize large quantities of enzyme biocatalysts in microstructured flow reactors under conditions that are as gentle as possible in order to realize efficient material conversions. Here, monodisperse foams consisting almost entirely of enzymes covalently linked via SpyCatcher/SpyTag conjugation are reported. The biocatalytic foams are readily available from recombinant enzymes via microfluidic air-in-water droplet formation, can be directly integrated into microreactors, and can be used for biocatalytic conversions after drying. Reactors prepared by this method show surprisingly high stability and biocatalytic activity. The physicochemical characterization of the new materials is described and exemplary applications in biocatalysis are shown using two-enzyme cascades for the stereoselective synthesis of chiral alcohols and the rare sugar tagatose.
Mineralization of hydrogel biomaterials is desirable to improve their suitability as materials for bone regeneration. In this study, gellan gum (GG) hydrogels were formed by simple mixing of GG solution with bioactive glass microparticles of 45S5 composition, leading to hydrogel formation by ion release from the amorphous bioactive glass microparticles. This resulted in novel injectable, self-gelling composites of GG hydrogels containing 20% bioactive glass. Gelation occurred within 20 min. Composites containing the standard 45S5 bioactive glass preparation were markedly less stiff. X-ray microcomputed tomography proved to be a highly sensitive technique capable of detecting microparticles of diameter approximately 8 μm, that is, individual microparticles, and accurately visualizing the size distribution of bioactive glass microparticles and their aggregates, and their distribution in GG hydrogels. The widely used melt-derived 45S5 preparation served as a standard and was compared with a calcium-rich, sol–gel derived preparation (A2), as well as A2 enriched with zinc (A2Zn5) and strontium (A2Sr5). A2, A2Zn, and A2Sr bioactive glass particles were more homogeneously dispersed in GG hydrogels than 45S5. Composites containing all four bioactive glass preparations exhibited antibacterial activity against methicillin-resistant Staphylococcus aureus. Composites containing A2Zn5 and A2Sr5 bioactive glasses supported the adhesion and growth of osteoblast-like cells and were considerably more cytocompatible than 45S5. All composites underwent mineralization with calcium-deficient hydroxyapatite upon incubation in simulated body fluid. The extent of mineralization appeared to be greatest for composites containing A2Zn5 and 45S5. The results underline the importance of the choice of bioactive glass when preparing injectable, self-gelling composites.
V-Al-C-N hard coatings with high carbon content were deposited by reactive radio-frequency magnetron sputtering using an experimental combinatorial approach, deposition from a segmented sputter target. The composition-dependent coexisting phases within the coating were analysed using the complementary methods of X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), X-ray absorption near-edge spectroscopy (XANES) and extended X-ray absorption fine-structure spectroscopy (EXAFS). For the analysis of the X-ray absorption near-edge spectra, a new approach for evaluation of the pre-edge peak was developed, taking into account the self-absorption effects in thin films. Within the studied composition range, a mixed face-centred cubic (V,Al)(C,N) phase coexisting with a C-C-containing phase was observed. No indication of hexagonal (V,Al)(N,C) was found. The example of V-Al-C-N demonstrates how important a combination of complementary methods is for the detection of coexisting phases in complex multi-element coatings.
We investigated the lattice dynamics of the prototypic ferroelectric barium titanate close to its ferroelectric--paraelectric phase transition aiming at a better understanding of the atomistic nature of the transition.The usage of time-resolved X-ray techniques allows to disentangle lattice motion and unit cell changes, which, in part, relate to the ferroelectric polarization.In the quasi-static case both the electrical and the laser excitation show a mean-field, simple thermal behaviour, while for time scales shorter than nanoseconds the impulsive nature of the excitation becomes visible.
Morphological atlases are an important tool in organismal studies, and modern high-throughput Computed Tomography (CT) facilities can produce hundreds of full-body high-resolution volumetric images of organisms. However, creating an atlas from these volumes requires accurate organ segmentation. In the last decade, machine learning approaches have achieved incredible results in image segmentation tasks, but they require large amounts of annotated data for training. In this paper, we propose a self-training framework for multi-organ segmentation in tomographic images of Medaka fish. We utilize the pseudo-labeled data from a pretrained Teacher model and adopt a Quality Classifier to refine the pseudo-labeled data. Then, we introduce a pixel-wise knowledge distillation method to prevent overfitting to the pseudo-labeled data and improve the segmentation performance. The experimental results demonstrate that our method improves mean Intersection over Union (IoU) by 5.9% on the full dataset and enables keeping the quality while using three times less markup.
A theoretical study of grazing incidence diffraction by laterally patterned epitaxial nanostructures is presented and successfully applied to experimental findings of pseudomorphic multilayer gratings. Numerical results from a distorted wave Born approximation are given. The grating structure generates a fine structure around the reciprocal lattice points consisting of grating truncation rods, which are similar to the crystal truncation rod of a planar crystal surface. We show the influence of the grating shape, the compositional profile and the lattice strain on the intensity pattern along the grating truncation rods. The effect of lattice strain will be discussed by comparing the fully strained state (tetragonal lattice distortion), uniform strain relaxation and graduated strain relaxation in the surface grating. Our theoretical treatment allows us to interpret grazing incidence diffraction measurements obtained from strained layer gratings and superlattice gratings.
