The reaction of Co(NCS)2 with 4-methyl­pyridine N-oxide (C6H7NO) leads to the formation of two compounds, namely, tetra­kis­(4-methyl­pyridine N-oxide-κO)bis­(thio­cyanato-κN)cobalt(II), [Co(NCS)2(C6H7NO)4] (1), and tris­(4-methyl­pyridine N-oxide-κO)bis­(thio­cyanato-κN)cobalt(II), [Co(NCS)2(C6H7NO)3] (2). The asymmetric unit of 1 consists of one CoII cation located on a centre of inversion, as well as one thio­cyanate anion and two 4-methyl­pyridine N-oxide coligands in general positions. The CoII cations are octa­hedrally coordinated by two terminal N-bonding thio­cyanate anions in trans positions and four 4-methyl­pyridine N-oxide ligands. In the extended structure, these complexes are linked by C—H⋯O and C—H⋯S inter­actions. In compound 2, two crystallographically independent complexes are present, which occupy general positions. In each of these complexes, the CoII cations are coordinated in a trigonal–bipyramidal manner by two terminal N-bonding thio­cyanate anions in axial positions and by three 4-methyl­pyridine N-oxide ligands in equatorial positions. In the crystal, these complex mol­ecules are linked by C—H⋯S inter­actions. For compound 2, a nonmerohedral twin refinement was performed. Powder X-ray diffraction (PXRD) reveals that 2 was nearly obtained as a pure phase, which is not possible for compound 1. Differential thermoanalysis and thermogravimetry data (DTA–TG) show that compound 2 start to decompose at about 518 K.

In the structure of the title co-crystal, C3H3N3O2·C5H8N2, the components are linked by a set of directional O—H⋯N, N—H⋯O, N—H⋯N and C—H⋯O hydrogen bonds to yield a two-dimensional mono-periodic arrangement. The structure propagates in the third dimension by extensive π–π stacking inter­actions of nearly parallel mol­ecules of the two components, following an alternating sequence. The primary structure-defining inter­action is very strong oxime-OH donor to pyrazole-N acceptor hydrogen bond [O⋯N = 2.587 (2) Å], while the significance of weaker hydrogen bonds and π–π stacking inter­actions is comparable. The distinct structural roles of different kinds of inter­actions agree with the results of a Hirshfeld surface analysis and calculated inter­action energies. The title compound provides insights into co-crystals of active agrochemical mol­ecules and features the rational integration in one structure of a fungicide, C3H3N3O2, and a second active component, C5H8N2, known for alleviation the toxic effects of fungicides on plants. The material appears to be well suited for practical uses, being non-volatile, air-stable, water-soluble, but neither hygroscopic nor efflorescent.

Tri­chlorido­(4-methyl­piperidine)­gold(III), [AuCl3(C6H13N)], 1, crystallizes in Pbca with Z = 8. Tri­bromido­(4-methyl­piperidine)­gold(III), [AuBr3(C6H13N)], 2, crystallizes as two polymorphs, 2a in Pnma with Z = 4 (imposed mirror symmetry) and 2b, which is isotypic to 1. The Au—N bonds trans to Cl are somewhat shorter than those trans to Br, and the Au—Cl bonds trans to N are longer than those cis to N, whereas the Au—Br bonds trans to N are slightly shorter than the cis bonds. The methyl and AuX3 groups (X = halogen) occupy equatorial positions at the six-membered ring. The packing of all three structures involves chains of mol­ecules with offset stacking of the AuX3 moieties associated with short Au⋯X contacts; for 1 and 2b these are reinforced by N—H⋯X hydrogen bonds, whereas for 2a there are no classical hydrogen bonds and the chains are inter­connected by Br⋯Br contacts.

3-Phenyl-2-(thio­phen-3-yl)-2,3-di­hydro-4H-pyrido[3,2-e][1,3]thia­zin-4-one (C17H12N2OS2, 1) and 2-(1H-indol-3-yl)-3-phenyl-2,3-di­hydro-4H-pyrido[3,2-e][1,3]thia­zin-4-one 0.438-hydrate (C21H15N3OS·0.438H2O, 2) crystallize in space groups P21/n and C2/c, respectively. The asymmetric unit in each case is comprised of two parent mol­ecules, albeit of mixed chirality in the case of 1 and of similar chirality in 2 with the enanti­omers occupying the neighboring asymmetric units. Structure 2 also has water mol­ecules (partial occupancies) that form continuous channels along the b-axis direction. The thia­zine rings in both structures exhibit an envelope conformation. Inter­molecular inter­actions in 1 are defined only by C—H⋯O and C—H⋯N hydrogen bonds between crystallographically independent mol­ecules. In 2, hydrogen bonds of the type N—H⋯O between independent mol­ecules and C—H⋯N(π) type, and π–π stacking inter­actions between the pyridine rings of symmetry-related mol­ecules are observed.

Tetra­kis(μ-acetato-κ2O:O')bis­{[1,3-bis­(2,6-diiso­propyl­phen­yl)imidazol-2-yl­idene-κC2]chromium(II)} tetra­hydro­furan disolvate, [Cr2(C2H3O2)4(C27H36N4)2]·2C4H8O or [Cr2(OAc)4(IDipp)2]·2C4H8O (1), and tetra­kis­(μ-acetato-κ2O:O')bis­{[1,3-bis­(2,4,6-tri­methyl­phen­yl)imidazol-2-yl­idene-κC2]chromium(II)}, {Cr2(C2H3O2)4(C21H24N2)2] or [Cr2(OAc)4(IMes)2] (2), were synthesized from anhydrous chromium(II) acetate [Cr2(OAc)4] and the corresponding NHC (NHC = N-heterocyclic carbene) in toluene as solvent. Both complexes crystallize in the triclinic system, space group Poverline{1}. The mol­ecular structures consist of Cr2(OAc)4 paddle-wheels that carry two terminal NHC ligands. This leads to a square-pyramidal coordination of the chromium atoms.

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.

Two different multi-component crystals consisting of papaverine [1-(3,4-di­meth­oxy­benz­yl)-6,7-di­meth­oxy­iso­quinoline, C20H21NO4] and fumaric acid [C4H4O4] were obtained. Single-crystal X-ray structure analysis revealed that one, C20H21NO4·1.5C4H4O4 (I), is a salt co-crystal composed of salt-forming and non-salt-forming mol­ecules, and the other, C20H21NO4·0.5C4H4O4 (II), is a salt–co-crystal inter­mediate (i.e., in an inter­mediate state between a salt and a co-crystal). In this study, one state (crystal structure at 100 K) within the salt–co-crystal continuum is defined as the ‘inter­mediate’.

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.

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.

X-ray photon correlation spectroscopy (XPCS) is a powerful tool for the investigation of dynamics covering a broad range of timescales and length scales. The two-time correlation function (TTC) is commonly used to track non-equilibrium dynamical evolution in XPCS measurements, with subsequent extraction of one-time correlations. While the theoretical foundation for the quantitative analysis of TTCs is primarily established for equilibrium systems, where key parameters such as the diffusion coefficient remain constant, non-equilibrium systems pose a unique challenge. In such systems, different projections (`cuts') of the TTC may lead to divergent results if the underlying fundamental parameters themselves are subject to temporal variations. This article explores widely used approaches for TTC calculations and common methods for extracting relevant information from correlation functions, particularly in the light of comparing dynamics in equilibrium and non-equilibrium systems.

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