The title compound, [Cd(C14H16N3S)Cl] or [CdLCl] (1), where LH = 2-[bis­(pyridin-2-ylmeth­yl)amino]­ethane-1-thiol, was prepared and structurally characterized. The Cd2+ complex crystallizes in P21/c with a distorted trigonal–bipyramidal metal coordination geometry. Supra­molecular inter­actions in 1 include parallel offset face-to-face inter­actions between inversion-related pyridyl rings and potential hydrogen bonds with chlorine or sulfur as the acceptor. Additional cooperative pyrid­yl–pyridyl inter­actions with roughly 45° tilt angles and centroid–centroid distances of less than 5.5 Å likely also contribute to the overall solid-state stability. Hirshfeld surface analysis indicates that H⋯H (51.2%), Cl⋯H/H⋯Cl (13.9%), C⋯H/H⋯C (12.3%) and S⋯H/H⋯S (11.8%) inter­actions are dominant in the solid state.

The mol­ecular salt sulfamethoxazolium {or 4-[(5-methyl-1,2-oxazol-3-yl)sulf­amo­yl]anilinium methyl sulfate monohydrate}, C10H12N3O3S+·CH3O4S−·H2O, was prepared by the reaction of sulfamethoxazole and H2SO4 in methanol and crystallized from methanol–ether–water. Protonation takes place at the nitro­gen atom of the primary amino group. In the crystal, N—H⋯O hydrogen bonds (water and methyl­sulfate anion) and inter­molecular N—H⋯N inter­actions involving the sulfonamide and isoxazole nitro­gen atoms, link the components into a tri-dimensional network, additional cohesion being provided by face-to-face π–π inter­actions between the phenyl rings of adjacent mol­ecules. A Hirshfeld surface analysis was used to verify the contributions of the different inter­molecular inter­actions, showing that the three most important contributions for the crystal packing are from H⋯O (54.1%), H⋯H (29.2%) and H⋯N (5.0%) inter­actions.

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.

The crystal structure of the title compound, [Ni(C8H20N4)(NO3)]NO3, at room temperature, has monoclinic (P21/n) symmetry. The structure displays inter­molecular hydrogen bonding. The nickel displays a distorted bipyramidal geometry with the symmetric bidentate bonded nitrate occupying an equatorial site. The 1,4,7,10-tetra­aza­cyclo­dodecane (cyclen) backbone has the [4,8] configuration, with three nitro­gen-bound H atoms directed above the plane of the nitro­gen atoms towards the offset nickel atom with the fourth nitro­gen-bound hydrogen directed below from the plane of the nitro­gen atoms. The nitrate anion O atoms are seen to hydrogen bond to the H atoms bound to the N atoms of the ligand.

The structures of the title compounds 2-hy­droxy-N'-methyl­acetohydrazide, 1, and 2-hy­droxy-N-methyl­acetohydrazide, 2, both C3H8N2O2, as regioisomers differ in the position of the methyl group relative to the N atoms in 2-hy­droxy-acetohydrazide. In the structure of 1, the 2-hy­droxy-acetohydrazide core [OH—C—C(=O)—NH—NH] is almost planar and the methyl group is rotated relative to this plane. As opposed to 1, in the structure of 2 all non-hydrogen atoms lie in the same plane. The hydroxyl and carbonyl groups in structures 1 and 2 are in trans and cis positions, respectively. The methyl amino group and carbonyl group are in the cis position relative to the C—N bond in structure 1, while the amino group and carbonyl group are in the trans position relative to the C—N bond in stucture 2. In the crystal, mol­ecules of 1 are linked by N—H⋯O and O—H⋯N inter­molecular hydrogen bonds, forming layers parallel to the ab crystallographic plane. A Hirshfeld surface analysis showed that the H⋯H contacts dominate the crystal packing with a contribution of 55.3%. The contribution of the H⋯O/O⋯H inter­action is somewhat smaller, amounting to 30.8%. In the crystal, as a result of the inter­molecular O—H⋯O hydrogen bonds, mol­ecules of 2 form dimers, which are linked by N—H⋯O hydrogen bonds and a three-dimensional supra­molecular network The major contributors to the Hirshfeld surface are H⋯H (58.5%) and H⋯O/O⋯H contacts (31.7%).

Two new phenyl­sulfonyl­indole derivatives, namely, N-{[3-bromo-1-(phenyl­sulfon­yl)-1H-indol-2-yl]meth­yl}-N-(4-bromo-3-meth­oxy­phen­yl)benzene­sulfonamide, C28H22Br2N2O5S2, (I), and N,N-bis­{[3-bromo-1-(phenyl­sulfon­yl)-1H-indol-2-yl]meth­yl}benzene­sulfonamide, C36H27Br2N3O6S3, (II), reveal the impact of intra­molecular π–π inter­actions of the indole moieties as a factor not only governing the conformation of N,N-bis­(1H-indol-2-yl)meth­yl)amines, but also significantly influencing the crystal patterns. For I, the crystal packing is dominated by C—H⋯π and π–π bonding, with a particular significance of mutual indole–indole inter­actions. In the case of II, the mol­ecules adopt short intra­molecular π–π inter­actions between two nearly parallel indole ring systems [with the centroids of their pyrrole rings separated by 3.267 (2) Å] accompanied by a set of forced Br⋯O contacts. This provides suppression of similar inter­actions between the mol­ecules, while the importance of weak C—H⋯O hydrogen bonding to the packing naturally increases. Short contacts of the latter type [C⋯O = 3.389 (6) Å] assemble pairs of mol­ecules into centrosymmetric dimers with a cyclic R22(13) ring motif. These findings are consistent with the results of a Hirshfeld surface analysis and together they suggest a tool for modulating the supra­molecular behavior of phenyl­sulfonyl­ated indoles.

In the title compound, C25H17NO3, the torsion angle associated with the phenyl benzoate group is −173.7 (2)° and that for the benz­yloxy group is −174.8 (2)° establishing an anti-type conformation. The dihedral angles between the ten-membered cyanona­phthalene ring and the aromatic ring of the phenyl benzoate and the benz­yloxy fragments are 40.70 (10) and 87.51 (11)°, respectively, whereas the dihedral angle between the aromatic phenyl benzoate and the benz­yloxy fragments is 72.30 (13)°. In the crystal, the mol­ecules are linked by weak C—H⋯O inter­actions forming S(4) chains propagating parallel to [010]. The packing is consolidated by three C—H⋯π inter­actions and two π–π stacking inter­actions between the aromatic rings of naphthalene and phenyl benzoate with centroid-to-centroid distances of 3.9698 (15) and 3.8568 (15) Å, respectively. Inter­molecular inter­actions were qu­anti­fied using Hirshfeld surface analysis. The mol­ecular structure was further optimized by density functional theory (DFT) at the B3LYP/6–311+ G(d,p) level, revealing that the energy gap between HOMO and LUMO is 3.17 eV. Mol­ecular docking studies were carried out for the title compound as a ligand and SARS-Covid-2(PDB ID:7QF0) protein as a receptor giving a binding affinity of −9.5 kcal mol−1.

In the title mol­ecule, C20H18ClN3O2, the 2-chloro­phenyl group is disordered to a small extent [occupancies 0.875 (2)/0.125 (2)]. The phenyl­acetamide moiety is nearly planar due to a weak, intra­molecular C—H⋯O hydrogen bond. In the crystal, N—H⋯O hydrogen bonds and π-stacking inter­actions between pyridazine and phenyl rings form helical chains of mol­ecules in the b-axis direction, which are linked by C—H⋯O hydrogen bonds and C—H⋯π(ring) inter­actions. A Hirshfeld surface analysis was performed, which showed that H⋯H, C⋯H/H⋯C and O⋯H/H⋯O inter­actions to dominate the inter­molecular contacts in the crystal.

The title organic–inorganic hybrid salt, C2H7IN+·I−, is isotypic with its bromine analog, C2H7BrN+·Br− [Semenikhin et al. (2024). Acta Cryst. E80, 738–741]. Its asymmetric unit consists of one 2-iodo­ethyl­ammonium cation and one iodide anion. The NH3+ group of the organic cation forms weak hydrogen bonds with four neighboring iodide anions, leading to the formation of supra­molecular layers propagating parallel to the bc plane. Hirshfeld surface analysis reveals that the most important contribution to the crystal packing is from N—H⋯I inter­actions (63.8%). The crystal under investigation was twinned by a 180° rotation around [001].

A new uranium metal–organic complex salt, [U(C10H9O2)2O2(C2H6O)], with benzoyl acetone, namely, bis­(benzoyl­acetonato)(ethanol)dioxidouranium(VI), was synthesized. The compound has monoclinic P21/n symmetry. The geometry of the seven-coordinate U atom is penta­gonal bipyramidal, with the uranyl oxygen atoms in apical positions. In the complex, the ligands bind to the metal through oxygen atoms. Additional weak O—H⋯O contacts between the cations and anions consolidate the three-dimensional arrangement of the structure. On the Hirshfeld surface, the largest contributions come from the short contacts such as van der Waals forces, including H⋯H, O⋯H and C⋯H. Inter­actions including C⋯C and O⋯C contacts were also observed; however, their contribution to the overall cohesion of the crystal structure is minor. A packing analysis was performed to check the strength of the crystal packing.

In crystallographic texture analysis, ensuring that sample directions are preserved from experiment to the resulting orientation distribution is crucial to obtain physical meaning from diffraction data. This work details a procedure to ensure instrument and sample coordinates are consistent when analyzing diffraction data with a Rietveld refinement using the texture analysis software MAUD. A quartz crystal is measured on the HIPPO diffractometer at Los Alamos National Laboratory for this purpose. The methods described here can be applied to any diffraction instrument measuring orientation distributions in polycrystalline materials.

Van Vleck modes describe all possible displacements of octahedrally coordinated ligands about a core atom. They are a useful analytical tool for analysing the distortion of octahedra, particularly for first-order Jahn–Teller distortions, but determination of the Van Vleck modes of an octahedron is complicated by the presence of angular distortion of the octahedron. This problem is most commonly resolved by calculating the bond distortion modes (Q2, Q3) along the bond axes of the octahedron, disregarding the angular distortion and losing information on the octahedral shear modes (Q4, Q5 and Q6) in the process. In this paper, the validity of assuming bond lengths to be orthogonal in order to calculate the Van Vleck modes is discussed, and a method is described for calculating Van Vleck modes without disregarding the angular distortion. A Python package for doing this, VanVleckCalculator, is introduced and some examples of its use are given. Finally, it is shown that octahedral shear and angular distortion are often, but not always, correlated, and a parameter η is proposed as the shear fraction. It is demonstrated that η can be used to predict whether the values will be correlated when varying a tuning parameter such as temperature or pressure.

X-ray absorption spectroscopy (XAS) is a promising technique for determining structural information from sensitive biological samples, but high-accuracy X-ray absorption fine structure (XAFS) requires corrections of systematic errors in experimental data. Low-temperature XAS and room-temperature X-ray absorption spectro-electrochemical (XAS-EC) measurements of N-truncated amyloid-β samples were collected and corrected for systematic effects such as dead time, detector efficiencies, monochromator glitches, self-absorption, radiation damage and noise at higher wavenumber (k). A new protocol was developed using extended X-ray absorption fine structure (EXAFS) data analysis for monitoring radiation damage in real time and post-analysis. The reliability of the structural determinations and consistency were validated using the XAS measurement experimental uncertainty. The correction of detector pixel efficiencies improved the fitting χ2 by 12%. An improvement of about 2.5% of the structural fitting was obtained after dead-time corrections. Normalization allowed the elimination of 90% of the monochromator glitches. The remaining glitches were manually removed. The dispersion of spectra due to self-absorption was corrected. Standard errors of experimental measurements were propagated from pointwise variance of the spectra after systematic corrections. Calculated uncertainties were used in structural refinements for obtaining precise and reliable values of structural parameters including atomic bond lengths and thermal parameters. This has permitted hypothesis testing.

BioXTAS RAW is a free open-source program for reduction, analysis and modelling of biological small-angle scattering data. Here, the new developments in RAW version 2 are described. These include improved data reduction using pyFAI; updated automated Guinier fitting and Dmax finding algorithms; automated series (e.g. size-exclusion chromatography coupled small-angle X-ray scattering or SEC-SAXS) buffer- and sample-region finding algorithms; linear and integral baseline correction for series; deconvolution of series data using regularized alternating least squares (REGALS); creation of electron-density reconstructions using electron density via solution scattering (DENSS); a comparison window showing residuals, ratios and statistical comparisons between profiles; and generation of PDF reports with summary plots and tables for all analysis. Furthermore, there is now a RAW API, which can be used without the graphical user interface (GUI), providing full access to all of the functionality found in the GUI. In addition to these new capabilities, RAW has undergone significant technical updates, such as adding Python 3 compatibility, and has entirely new documentation available both online and in the program.

An implementation of Slater-type spherical scattering factors for X-ray and electron diffraction for elements in the range Z = 1–103 is presented within the software Olex2. Both high- and low-angle Fourier behaviour of atomic electron density and electrostatic potential can thus be addressed, in contrast to the limited flexibility of the four Gaussian plus constant descriptions which are currently the most widely used method for calculating atomic scattering factors during refinement. The implementation presented here accommodates the increasing complexity of the electronic structure of heavier elements by using complete atomic wavefunctions without any interpolation between precalculated tables or intermediate fitting functions. Atomic wavefunctions for singly charged ions are implemented and made accessible, and these show drastic changes in electron diffraction scattering factors compared with the neutral atom. A comparison between the two different spherical models of neutral atoms is presented as an example for four different kinds of X-ray and two electron diffraction structures, and comparisons of refinement results using the existing diffraction data are discussed. A systematic but slight improvement in R values and residual densities can be observed when using the new scattering factors, and this is discussed relative to effects on the atomic displacement parameters and atomic positions, which are prominent near the heavier elements in a structure.

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.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

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.

Processing of single-crystal X-ray diffraction data from area detectors can be separated into two steps. First, raw intensities are obtained by integration of the diffraction images, and then data correction and reduction are performed to determine structure-factor amplitudes and their uncertainties. The second step considers the diffraction geometry, sample illumination, decay, absorption and other effects. While absorption is only a minor effect in standard macromolecular crystallography (MX), it can become the largest source of uncertainty for experiments performed at long wavelengths. Current software packages for MX typically employ empirical models to correct for the effects of absorption, with the corrections determined through the procedure of minimizing the differences in intensities between symmetry-equivalent reflections; these models are well suited to capturing smoothly varying experimental effects. However, for very long wavelengths, empirical methods become an unreliable approach to model strong absorption effects with high fidelity. This problem is particularly acute when data multiplicity is low. This paper presents an analytical absorption correction strategy (implemented in new software AnACor) based on a volumetric model of the sample derived from X-ray tomography. Individual path lengths through the different sample materials for all reflections are determined by a ray-tracing method. Several approaches for absorption corrections (spherical harmonics correction, analytical absorption correction and a combination of the two) are compared for two samples, the membrane protein OmpK36 GD, measured at a wavelength of λ = 3.54 Å, and chlorite dismutase, measured at λ = 4.13 Å. Data set statistics, the peak heights in the anomalous difference Fourier maps and the success of experimental phasing are used to compare the results from the different absorption correction approaches. The strategies using the new analytical absorption correction are shown to be superior to the standard spherical harmonics corrections. While the improvements are modest in the 3.54 Å data, the analytical absorption correction outperforms spherical harmonics in the longer-wavelength data (λ = 4.13 Å), which is also reflected in the reduced amount of data being required for successful experimental phasing.

Three-dimensional cryo electron microscopy reconstructions are obtained by extracting information from a large number of projections of the object. These projections correspond to different `views' or `orientations', i.e. directions in which these projections show the reconstructed object. Uneven distribution of these views and the presence of dominating preferred orientations may distort the reconstructed spatial images. This work describes the program VUE (views on uniform grids for cryo electron microscopy), designed to study such distributions. Its algorithms, based on uniform virtual grids on a sphere, allow an easy calculation and accurate quantitative analysis of the frequency distribution of the views. The key computational element is the Lambert azimuthal equal-area projection of a spherical uniform grid onto a disc. This projection keeps the surface area constant and represents the frequency distribution with no visual bias. Since it has multiple tunable parameters, the program is easily adaptable to individual needs, and to the features of a particular project or of the figure to be produced. It can help identify problems related to an uneven distribution of views. Optionally, it can modify the list of projections, distributing the views more uniformly. The program can also be used as a teaching tool.

The influence of various combinations of residual stress, composition and grain interaction gradients in polycrystalline materials with cubic symmetry on energy-dispersive X-ray stress analysis is theoretically investigated. For the evaluation of the simulated sin2ψ distributions, two different strategies are compared with regard to their suitability for separating the individual gradients. It is shown that the separation of depth gradients of the strain-free lattice parameter a0(z) from residual stress gradients σ(z) is only possible if the data analysis is carried out in section planes parallel to the surface. The impact of a surface layer z* that is characterized by a direction-dependent grain interaction model in contrast to the volume of the material is quantified by comparing a ferritic and an austenitic steel, which feature different elastic anisotropy. It is shown to be of minor influence on the resulting residual stress depth profiles if the data evaluation is restricted to reflections hkl with orientation factors Γhkl close to the model-independent orientation Γ*. Finally, a method is proposed that allows the thickness of the anisotropic surface layer z* to be estimated on the basis of an optimization procedure.

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.

This article presents a Python-based program, DFT2FEFFIT, to regress theoretical extended X-ray absorption fine structure (EXAFS) spectra calculated from density functional theory structure models against experimental EXAFS spectra. To showcase its application, Ce-doped fluorapatite [Ca10(PO4)6F2] is revisited as a representative of a material difficult to analyze by conventional multi-shell least-squares fitting of EXAFS spectra. The software is open source and publicly available.

Structural modelling of operando pair distribution function (PDF) data of complex functional materials can be highly challenging. To aid the understanding of complex operando PDF data, this article demonstrates a toolbox for PDF analysis. The tools include denoising using principal component analysis together with the structureMining, similarityMapping and nmfMapping apps available through the online service `PDF in the cloud' (PDFitc, https://pdfitc.org/). The toolbox is used for both ex situ and operando PDF data for 3 nm TiO2-bronze nanocrystals, which function as the active electrode material in a Li-ion battery. The tools enable structural modelling of the ex situ and operando PDF data, revealing two pristine TiO2 phases (bronze and anatase) and two lithiated LixTiO2 phases (lithiated versions of bronze and anatase), and the phase evolution during galvanostatic cycling is characterized.

Characterization of crystallization processes in situ is of great importance to furthering knowledge of how nucleation and growth processes direct the assembly of organic and inorganic materials in solution and, critically, understanding the influence that these processes have on the final physico-chemical properties of the resulting solid form. With careful specification and design, as demonstrated here, it is now possible to bring combined X-ray diffraction and Raman spectroscopy, coupled to a range of fully integrated segmented and continuous flow platforms, to the laboratory environment for in situ data acquisition for timescales of the order of seconds. The facility used here (Flow-Xl) houses a diffractometer with a micro-focus Cu Kα rotating anode X-ray source and a 2D hybrid photon-counting detector, together with a Raman spectrometer with 532 and 785 nm lasers. An overview of the diffractometer and spectrometer setup is given, and current sample environments for flow crystallization are described. Commissioning experiments highlight the sensitivity of the two instruments for time-resolved in situ data collection of samples in flow. Finally, an example case study to monitor the batch crystallization of sodium sulfate from aqueous solution, by tracking both the solute and solution phase species as a function of time, highlights the applicability of such measurements in determining the kinetics associated with crystallization processes. This work illustrates that the Flow-Xl facility provides high-resolution time-resolved in situ structural phase information through diffraction data together with molecular-scale solution data through spectroscopy, which allows crystallization mechanisms and their associated kinetics to be analysed in a laboratory setting.

The increasing structural complexity and downscaling of modern nanodevices require continuous development of structural characterization techniques that support R&D and manufacturing processes. This work explores the capability of laboratory characterization of periodic planar nanostructures using 3D X-ray standing waves as a promising method for reconstructing atomic profiles of planar nanostructures. The non-destructive nature of this metrology technique makes it highly versatile and particularly suitable for studying various types of samples. Moreover, it eliminates the need for additional sample preparation before use and can achieve sub-nanometre reconstruction resolution using widely available laboratory setups, as demonstrated on a diffractometer equipped with a microfocus X-ray tube with a copper anode.

Energy-dispersive Laue diffraction (EDLD) is a powerful method to obtain position-resolved texture information in inhomogeneous biological samples without the need for sample rotation. This study employs EDLD texture scanning to investigate the impact of two salivary peptides, statherin (STN) and histatin-1 (HTN) 21 N-terminal peptides (STN21 and HTN21), on the crystallographic structure of dental enamel. These proteins are known to play crucial roles in dental caries progression. Three healthy incisors were randomly assigned to three groups: artificially demineralized, demineralized after HTN21 peptide pre-treatment and demineralized after STN21 peptide pre-treatment. To understand the micro-scale structure of the enamel, each specimen was scanned from the enamel surface to a depth of 250 µm using microbeam EDLD. Via the use of a white beam and a pixelated detector, where each pixel functions as a spectrometer, pole figures were obtained in a single exposure at each measurement point. The results revealed distinct orientations of hydroxyapatite crystallites and notable texture variation in the peptide-treated demineralized samples compared with the demineralized control. Specifically, the peptide-treated demineralized samples exhibited up to three orientation populations, in contrast to the demineralized control which displayed only a single orientation population. The texture index of the demineralized control (2.00 ± 0.21) was found to be lower than that of either the STN21 (2.32 ± 0.20) or the HTN21 (2.90 ± 0.46) treated samples. Hence, texture scanning with EDLD gives new insights into dental enamel crystallite orientation and links the present understanding of enamel demineralization to the underlying crystalline texture. For the first time, the feasibility of EDLD texture measurements for quantitative texture evaluation in demineralized dental enamel samples is demonstrated.

Small-angle neutron scattering is a widely used technique to study large-scale structures in bulk samples. The largest accessible length scale in conventional Bragg scattering is determined by the combination of the longest available neutron wavelength and smallest resolvable scattering angle. A method is presented that circumvents this limitation and is able to extract larger length scales from the low-q power-law scattering using a modification of the well known Porod law connecting the scattered intensity of randomly distributed objects to their specific surface area. It is shown that in the special case of a highly aligned domain structure the specific surface area extracted from the modified Porod law can be used to determine specific length scales of the domain structure. The analysis method is applied to study the micrometre-sized domain structure found in the intermediate mixed state of the superconductor niobium. The analysis approach allows the range of accessible length scales to be extended from 1 µm to up to 40 µm using a conventional small-angle neutron scattering setup.

Here, a recently commissioned five-analyzer Johann spectrometer at the Inner Shell Spectroscopy beamline (8-ID) at the National Synchrotron Light Source II (NSLS-II) is presented. Designed for hard X-ray photon-in/photon-out spectroscopy, the spectrometer achieves a resolution in the 0.5–2 eV range, depending on the element and/or emission line, providing detailed insights into the local electronic and geometric structure of materials. It serves a diverse user community, including fields such as physical, chemical, biological, environmental and materials sciences. This article details the mechanical design, alignment procedures and data-acquisition scheme of the spectrometer, with a particular focus on the continuous asynchronous data-acquisition approach that significantly enhances experimental efficiency.

High-repetition-rate free-electron lasers impose stringent requirements on the thermal deformation of beamline optics. The Shanghai HIgh-repetition-rate XFEL aNd Extreme light facility (SHINE) experiences high average thermal power and demands wavefront preservation. To deeply study the thermal field of the first reflection mirror M1 at the FEL-II beamline of SHINE, thermal analysis under a photon energy of 400 eV was executed by fluid and solid heat transfer method. According to the thermal analysis results and the reference cooling water temperature of 30 °C, the temperature of the cooling water at the flow outlet is raised by 0.15 °C, and the wall temperature of the cooling tube increases by a maximum of 0.5 °C. The maximum temperature position of the footprint centerline in the meridian direction deviates away from the central position, and this asymmetrical temperature distribution will directly affect the thermal deformation of the mirror and indirectly affect the focus spot of the beam at the sample.

Source: Dr. Joseph Pantginis 11/04/2024

H.C. Wainwright & Co. analysts Dr. Joseph Pantginis, Dr. Lander Egaña Gorroño, Dr. Joshua Korsen, Dr. Matthew Keller, and Dr. Sara Nik, in a research report published on November 4, 2024, maintained their Buy rating on Lexicon Pharmaceuticals Inc. (LXRX:NASDAQ) with a price target of US$6.00. The report follows Lexicon's presentation of preclinical data for LX9851, its ACSL5 inhibitor for obesity, at ObesityWeek 2024.

The analysts highlighted key findings from the presentations, stating, "LX9851 promotes reduction of fat mass without affecting lean body mass" and "LX9851 improves and sustains GLP-1 RA-mediated weight loss, even after semaglutide discontinuation." They added that "Mechanistic studies suggest that LX9851-mediated ACSL5 inhibition activates the ileal brake."

Regarding the drug's potential, they noted, "LX9851 is a first-in-class, oral small molecule ACSL5 inhibitor designed to enhance and maintain weight loss promoted by incretin mimetics (GLP-1 receptor agonists), and offer improved treatment alternatives for obesity and related metabolic disorders."

The report also addressed recent developments with sotagliflozin, detailing the AdCom voting results and potential scenarios for FDA action. The analysts stated, "Although we anticipate favorable feedback from the agency regarding eGFR ≥60 to <90 range, our bet is that a confirmatory trial may be required to validate sota's efficacy in this subpopulation and obtain approval."

H.C. Wainwright & Co.'s valuation methodology is based on a clinical net present value (NPV) model. The analysts explained, "Our valuation is based on our clinical net present value (NPV) model, which allows us to flex multiple assumptions affecting a drug's profile. We currently value Lexicon solely on sotagliflozin sales in the U.S. for HF (INPEFA), HCM, and LX9211 for DPNP."

They added, "We believe significant upside potential exists, based on: (1) attaining higher market penetration for HF, and HCM; and (2) adding the earlier stage assets."

In conclusion, H.C. Wainwright & Co.'s maintenance of their Buy rating and US$6 price target reflects confidence in Lexicon's pipeline potential, particularly with LX9851 and sotagliflozin. The share price at the time of the report of US$1.22 represents a potential return of approximately 392% to the analysts' target price, highlighting the significant upside potential if the company's development programs prove successful.

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Disclosures for H.C. Wainwright & Co., Lexicon Pharmaceuticals Inc., November 4, 2024

This material is confidential and intended for use by Institutional Accounts as defined in FINRA Rule 4512(c). It may also be privileged or otherwise protected by work product immunity or other legal rules. If you have received it by mistake, please let us know by e-mail reply to unsubscribe@hcwresearch.com and delete it from your system; you may not copy this message or disclose its contents to anyone. The integrity and security of this message cannot be guaranteed on the Internet. H.C. WAINWRIGHT & CO, LLC RATING SYSTEM: H.C. Wainwright employs a three tier rating system for evaluating both the potential return and risk associated with owning common equity shares of rated firms. The expected return of any given equity is measured on a RELATIVE basis of other companies in the same sector. The price objective is calculated to estimate the potential movements in price that a given equity could reach provided certain targets are met over a defined time horizon. Price objectives are subject to external factors including industry events and market volatility.

H.C. Wainwright & Co, LLC (the “Firm”) is a member of FINRA and SIPC and a registered U.S. Broker-Dealer. I, Joseph Pantginis, Ph.D., Lander Egaña Gorroño, Ph.D., Joshua Korsen, Ph.D., Matthew Keller, Ph.D. and Sara Nik, Ph.D. , certify that 1) all of the views expressed in this report accurately reflect my personal views about any and all subject securities or issuers discussed; and 2) no part of my compensation was, is, or will be directly or indirectly related to the specific recommendation or views expressed in this research report; and 3) neither myself nor any members of my household is an officer, director or advisory board member of these companies. None of the research analysts or the research analyst’s household has a financial interest in the securities of Lexicon Pharmaceuticals, Inc. (including, without limitation, any option, right, warrant, future, long or short position). As of September 30, 2024 neither the Firm nor its affiliates beneficially own 1% or more of any class of common equity securities of Lexicon Pharmaceuticals, Inc..

Neither the research analyst nor the Firm knows or has reason to know of any other material conflict of interest at the time of publication of this research report. The research analyst principally responsible for preparation of the report does not receive compensation that is based upon any specific investment banking services or transaction but is compensated based on factors including total revenue and profitability of the Firm, a substantial portion of which is derived from investment banking services. The firm or its affiliates received compensation from Lexicon Pharmaceuticals, Inc. for non-investment banking services in the previous 12 months. The Firm or its affiliates did not receive compensation from Lexicon Pharmaceuticals, Inc. for investment banking services within twelve months before, but will seek compensation from the companies mentioned in this report for investment banking services within three months following publication of the research report. The Firm does not make a market in Lexicon Pharmaceuticals, Inc. as of the date of this research report.

The securities of the company discussed in this report may be unsuitable for investors depending on their specific investment objectives and financial position. Past performance is no guarantee of future results. This report is offered for informational purposes only, and does not constitute an offer or solicitation to buy or sell any securities discussed herein in any jurisdiction where such would be prohibited. This research report is not intended to provide tax advice or to be used to provide tax advice to any person. Electronic versions of H.C. Wainwright & Co., LLC research reports are made available to all clients simultaneously. No part of this report may be reproduced in any form without the expressed permission of H.C. Wainwright & Co., LLC. Additional information available upon request. H.C. Wainwright & Co., LLC does not provide individually tailored investment advice in research reports. This research report is not intended to provide personal investment advice and it does not take into account the specific investment objectives, financial situation and the particular needs of any specific person. Investors should seek financial advice regarding the appropriateness of investing in financial instruments and implementing investment strategies discussed or recommended in this research report. H.C. Wainwright & Co., LLC’s and its affiliates’ salespeople, traders, and other professionals may provide oral or written market commentary or trading strategies that reflect opinions that are contrary to the opinions expressed in this research report. H.C. Wainwright & Co., LLC and its affiliates, officers, directors, and employees, excluding its analysts, will from time to time have long or short positions in, act as principal in, and buy or sell, the securities or derivatives (including options and warrants) thereof of covered companies referred to in this research report. The information contained herein is based on sources which we believe to be reliable but is not guaranteed by us as being accurate and does not purport to be a complete statement or summary of the available data on the company, industry or security discussed in the report. All opinions and estimates included in this report constitute the analyst’s judgment as of the date of this report and are subject to change without notice. Securities and other financial instruments discussed in this research report: may lose value; are not insured by the Federal Deposit Insurance Corporation; and are subject to investment risks, including possible loss of the principal amount invested.

( Companies Mentioned: LXRX:NASDAQ, )

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