The in­do­line portion of the title mol­ecule, C16H13NO2, is planar. In the crystal, a layer structure is generated by C—H⋯O hydrogen bonds and C—H⋯π(ring), π-stacking and C=O⋯π(ring) inter­actions. The Hirshfeld surface analysis of the crystal structure indicates that the most important contributions for the crystal packing are from H⋯H (43.0%), H⋯C/C⋯H (25.0%) and H⋯O/O⋯H (22.8%) inter­actions. Hydrogen bonding and van der Waals inter­actions are the dominant inter­actions in the crystal packing. The volume of the crystal voids and the percentage of free space were calculated to be 120.52 Å3 and 9.64%, respectively, showing that there is no large cavity in the crystal packing. Evaluation of the electrostatic, dispersion and total energy frameworks indicate that the stabilization is dominated by the dispersion energy contributions in the title compound. Moreover, the DFT-optimized structure at the B3LYP/6-311G(d,p) level is compared with the experimentally determined mol­ecular structure in the solid state.

In the title mol­ecule, C21H23N3O3, the imidazolidine ring slightly deviates from planarity and the morpholine ring exhibits the chair conformation. In the crystal, N—H⋯O and C—H⋯O hydrogen bonds form helical chains of mol­ecules extending parallel to the c axis that are connected by C—H⋯π(ring) inter­actions. A Hirshfeld surface analysis reveals that the most important contributions for the crystal packing are from H⋯H (55.2%), H⋯C/C⋯H (22.6%) and H⋯O/O⋯H (20.5%) inter­actions. The volume of the crystal voids and the percentage of free space were calculated to be 236.78 Å3 and 12.71%, respectively. Evaluation of the electrostatic, dispersion and total energy frameworks indicates that the stabilization is dominated by the nearly equal electrostatic and dispersion energy contributions. The DFT-optimized mol­ecular structure at the B3LYP/6-311 G(d,p) level is compared with the experimentally determined mol­ecular structure in the solid state. Moreover, the HOMO–LUMO behaviour was elucidated to determine the energy gap.

The benzimidazole entity of the title mol­ecule, C17H21N5O, is almost planar (r.m.s. deviation = 0.0262 Å). In the crystal, bifurcated C—H⋯O hydrogen bonds link individual mol­ecules into layers extending parallel to the ac plane. Two weak C—H⋯π(ring) inter­actions may also be effective in the stabilization of the crystal structure. Hirshfeld surface analysis of the crystal structure reveals that the most important contributions for the crystal packing are from H⋯H (57.9%), H⋯C/C⋯H (18.1%) and H⋯O/O⋯H (14.9%) inter­actions. Hydrogen bonding and van der Waals inter­actions are the most dominant forces in the crystal packing. Evaluation of the electrostatic, dispersion and total energy frameworks indicate that the stabilization of the title compound is dominated via dispersion energy contributions. The mol­ecular structure optimized by density functional theory (DFT) at the B3LYP/6–311 G(d,p) level is compared with the experimentally determined mol­ecular structure in the solid state.

The pyrazolo­pyrimidine moiety in the title mol­ecule, C13H12N4S, is planar with the methyl­sulfanyl substituent lying essentially in the same plane. The benzyl group is rotated well out of this plane by 73.64 (6)°, giving the mol­ecule an approximate L shape. In the crystal, C—H⋯π(ring) inter­actions and C—H⋯S hydrogen bonds form tubes extending along the a axis. Furthermore, there are π–π inter­actions between parallel phenyl rings with centroid-to-centroid distances of 3.8418 (12) Å. A Hirshfeld surface analysis of the crystal structure indicates that the most important contributions to the crystal packing are from H⋯H (47.0%), H⋯N/N⋯H (17.6%) and H⋯C/C⋯H (17.0%) inter­actions. The volume of the crystal voids and the percentage of free space were calculated to be 76.45 Å3 and 6.39%, showing that there is no large cavity in the crystal packing. Evaluation of the electrostatic, dispersion and total energy frameworks indicate that the cohesion of the crystal structure is dominated by the dispersion energy contributions.

In the structure of the title salt, {[Ba(μ3-C3H2N3O2)2(μ-H2O)(H2O)]·H2O}n, the barium ion and all three oxygen atoms of the water mol­ecules reside on a mirror plane. The hydrogen atoms of the bridging water and the solvate water mol­ecules are arranged across a mirror plane whereas all atoms of the monodentate aqua ligand are situated on this mirror plane. The distorted ninefold coord­ination of the Ba ions is completed with four nitroso-, two carbonyl- and three aqua-O atoms at the distances of 2.763 (3)–2.961 (4) Å and it is best described as tricapped trigonal prism. The three-dimensional framework structure is formed by face-sharing of the trigonal prisms, via μ-nitroso- and μ-aqua-O atoms, and also by the bridging coordination of the anions via carbonyl-O atoms occupying two out of the three cap positions. The solvate water mol­ecules populate the crystal channels and facilitate a set of four directional hydrogen bonds. The principal Ba–carbamoyl­cyano­nitro­somethanido linkage reveals a rare example of the inherently polar binodal six- and three-coordinated bipartite topology (three-letter notation sit). It suggests that small resonance-stabilized cyano­nitroso anions can be utilized as bridging ligands for the supra­molecular synthesis of MOF solids. Such an outcome may be anti­cipated for a broader range of hard Lewis acidic alkaline earth metal ions, which perfectly match the coordination preferences of highly nucleophilic nitroso-O atoms. Thermal analysis reveals two-stage dehydration of the title compound (383 and 473 K) followed by decomposition with release of CO2, HCN and H2O at 558 K.

The benzimidazole moiety in the title mol­ecule, C19H25N5O, is almost planar and oriented nearly perpendicular to the triazole ring. In the crystal, C—H⋯O hydrogen bonds link the mol­ecules into a network structure. There are no π–π inter­actions present but two weak C—H⋯π(ring) inter­actions are observed. A Hirshfeld surface analysis of the crystal structure indicates that the most important contributions for the crystal packing are from H⋯H (62.0%), H⋯C/C⋯H (16.1%), H⋯N/N⋯H (13.7%) and H⋯O/O⋯H (7.5%) inter­actions. Evaluation of the electrostatic, dispersion and total energy frameworks indicate that the stabilization is dominated via the dispersion energy contributions in the title compound.

Reaction of copper(I)chloride with 2,3-di­methyl­pyrazine in ethanol leads to the formation of the title compound, poly[[μ-chlorido-μ-(2,3-di­methyl­pyrazine)-copper(I)] ethanol hemisolvate], {[CuCl(C6H8N2)]·0.5C2H5OH}n or CuCl(2,3-di­methyl­pyrazine) ethanol hemisolvate. Its asymmetric unit consists of two crystallographically independent copper cations, two chloride anions and two 2,3-di­methyl­pyrazine ligands as well as one ethanol solvate mol­ecule in general positions. The ethanol mol­ecule is disordered and was refined using a split model. The methyl H atoms of the 2,3-di­methyl­pyrazine ligands are also disordered and were refined in two orientations rotated by 60° relative to each other. In the crystal structure, each copper cation is tetra­hedrally coordinated by two N atoms of two bridging 2,3-di­methyl­pyrazine ligands and two μ-1,1-bridg­ing chloride anions. Each of the two copper cations are linked by pairs of bridging chloride anions into dinuclear units that are further linked into layers via bridging 2,3-di­methyl­pyrazine coligands. These layers are stacked in such a way that channels are formed in which the disordered solvent mol­ecules are located. The topology of this network is completely different from that observed in the two polymorphic modifications of CuCl(2,3-di­methyl­pyrazine) reported in the literature [Jess & Näther (2006). Inorg. Chem. 45, 7446–7454]. Powder X-ray diffraction measurements reveal that the title compound is unstable and transforms immediately into an unknown crystalline phase.

The title mol­ecule, C25H23BrN2O2, adopts a cup shaped conformation with the distinctly ruffled imidazolidine ring as the base. In the crystal, weak C—H⋯O hydrogen bonds and C—H⋯π(ring) inter­actions form helical chains of mol­ecules extending along the b-axis direction that are linked by additional weak C—H⋯π(ring) inter­actions across inversion centres. The Hirshfeld surface analysis of the crystal structure indicates that the most important contributions for the crystal packing are from H⋯H (51.0%), C⋯H/H⋯C (21.3%), Br⋯H/H⋯Br (12.8%) and O⋯H/H⋯O (12.4%) inter­actions. The volume of the crystal voids and the percentage of free space were calculated to be 251.24 Å3 and 11.71%, respectively, showing that there is no large cavity in the crystal packing. Evaluation of the electrostatic, dispersion and total energy frameworks indicate that the stabilization is dominated by the dispersion energy.

In cellulo crystallization is a rare event in nature. Recent advances that have made use of heterologous overexpression can promote the intracellular formation of protein crystals, but new tools are required to detect and characterize these targets in the complex cell environment. The present work makes use of Mask R-CNN, a convolutional neural network (CNN)-based instance segmentation method, for the identification of either single or multi-shaped crystals growing in living insect cells, using conventional bright field images. The algorithm can be rapidly adapted to recognize different targets, with the aim of extracting relevant information to support a semi-automated screening pipeline, in order to aid the development of the intracellular protein crystallization approach.

Fluctuation X-ray scattering (FXS) offers a complementary approach for nano- and bioparticle imaging with an X-ray free-electron laser (XFEL), by extracting structural information from correlations in scattered XFEL pulses. Here a workflow is presented for single-particle structure determination using FXS. The workflow includes procedures for extracting the rotational invariants from FXS patterns, performing structure reconstructions via iterative phasing of the invariants, and aligning and averaging multiple reconstructions. The reconstruction pipeline is implemented in the open-source software xFrame and its functionality is demonstrated on several simulated structures.

X-ray reflectometry (XRR) is a powerful tool for probing the structural characteristics of nanoscale films and layered structures, which is an important field of nanotechnology and is often used in semiconductor and optics manufacturing. This study introduces a novel approach for conducting quantitative high-resolution millisecond monochromatic XRR measurements. This is an order of magnitude faster than in previously published work. Quick XRR (qXRR) enables real time and in situ monitoring of nanoscale processes such as thin film formation during spin coating. A record qXRR acquisition time of 1.4 ms is demonstrated for a static gold thin film on a silicon sample. As a second example of this novel approach, dynamic in situ measurements are performed during PMMA spin coating onto silicon wafers and fast fitting of XRR curves using machine learning is demonstrated. This investigation primarily focuses on the evolution of film structure and surface morphology, resolving for the first time with qXRR the initial film thinning via mass transport and also shedding light on later thinning via solvent evaporation. This innovative millisecond qXRR technique is of significance for in situ studies of thin film deposition. It addresses the challenge of following intrinsically fast processes, such as thin film growth of high deposition rate or spin coating. Beyond thin film growth processes, millisecond XRR has implications for resolving fast structural changes such as photostriction or diffusion processes.

Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters.

Recent developments in synchrotron radiation facilities have increased the amount of data generated during acquisitions considerably, requiring fast and efficient data processing techniques. Here, the application of dense neural networks (DNNs) to data treatment of X-ray diffraction computed tomography (XRD-CT) experiments is presented. Processing involves mapping the phases in a tomographic slice by predicting the phase fraction in each individual pixel. DNNs were trained on sets of calculated XRD patterns generated using a Python algorithm developed in-house. An initial Rietveld refinement of the tomographic slice sum pattern provides additional information (peak widths and integrated intensities for each phase) to improve the generation of simulated patterns and make them closer to real data. A grid search was used to optimize the network architecture and demonstrated that a single fully connected dense layer was sufficient to accurately determine phase proportions. This DNN was used on the XRD-CT acquisition of a mock-up and a historical sample of highly heterogeneous multi-layered decoration of a late medieval statue, called `applied brocade'. The phase maps predicted by the DNN were in good agreement with other methods, such as non-negative matrix factorization and serial Rietveld refinements performed with TOPAS, and outperformed them in terms of speed and efficiency. The method was evaluated by regenerating experimental patterns from predictions and using the R-weighted profile as the agreement factor. This assessment allowed us to confirm the accuracy of the results.

Metal–organic frameworks (MOFs) have garnered significant attention in recent years owing to their exceptional properties. Understanding the intricate relationship between the structure of a material and its properties is crucial for guiding the synthesis and application of these materials. (Scanning) Transmission electron microscopy (S)TEM imaging stands out as a powerful tool for structural characterization at the nanoscale, capable of detailing both periodic and aperiodic local structures. However, the high electron-beam sensitivity of MOFs presents substantial challenges in their structural characterization using (S)TEM. This paper summarizes the latest advancements in low-dose high-resolution (S)TEM imaging technology and its application in MOF material characterization. It covers aspects such as framework structure, defects, and surface and interface analysis, along with the distribution of guest molecules within MOFs. This review also discusses emerging technologies like electron ptychography and outlines several prospective research directions in this field.

Understanding the structure–property relationship in electrocatalysts under working conditions is crucial for the rational design of novel and improved catalytic materials. This paper presents the Aarhus University reactor for electrochemical studies using X-rays (AUREX) operando electrocatalytic flow cell, designed as an easy-to-use versatile setup with a minimal background contribution and a uniform flow field to limit concentration polarization and handle gas formation. The cell has been employed to measure operando total scattering, diffraction and absorption spectroscopy as well as simultaneous combinations thereof on a commercial silver electrocatalyst for proof of concept. This combination of operando techniques allows for monitoring of the short-, medium- and long-range structure under working conditions, including an applied potential, liquid electrolyte and local reaction environment. The structural transformations of the Ag electrocatalyst are monitored with non-negative matrix factorization, linear combination analysis, the Pearson correlation coefficient matrix, and refinements in both real and reciprocal space. Upon application of an oxidative potential in an Ar-saturated aqueous 0.1 M KHCO3/K2CO3 electrolyte, the face-centered cubic (f.c.c.) Ag gradually transforms first to a trigonal Ag2CO3 phase, followed by the formation of a monoclinic Ag2CO3 phase. A reducing potential immediately reverts the structure to the Ag (f.c.c.) phase. Following the electrochemical-reaction-induced phase transitions is of fundamental interest and necessary for understanding and improving the stability of electrocatalysts, and the operando cell proves a versatile setup for probing this. In addition, it is demonstrated that, when studying electrochemical reactions, a high energy or short exposure time is needed to circumvent beam-induced effects.

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