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The crystal structure of meglumine diatrizoate has been solved and refined using synchrotron X-ray powder diffraction data and optimized using density functional theory techniques. Meglumine diatrizoate crystallizes in space group P21 (#4) with a = 10.74697(4), b = 6.49364(2), c = 18.52774(7) Å, β = 90.2263(3), V = 1292.985(5) Å3, and Z = 2. Two different crystal structures, which yielded essentially identical refinement residuals and positions of the non-H atoms, were obtained. The differences were in the H atom positions and the hydrogen bonding. One structure was 123.0 kJ/mol/cell lower in energy than the other and was adopted for the final description. The crystal structure consists of alternating double layers of cations and anions along the c-axis. The hydrogen bonds link the cations and anions into a three-dimensional framework. Each of the hydrogen atoms on the ammonium nitrogen of the cation acts as a donor in a strong N–H⋯O hydrogen bond. One of these is to a hydroxyl group of another cation, and the other is to the carboxylate group of the anion. Each of the amide nitrogen atoms of the anion forms a strong N–H⋯O intermolecular hydrogen bond, one to a carbonyl and the other to a carboxylate group. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).
Least-squares analysis on the diffraction intensity values certified for NIST SRM676a and SRM1976c α-Al2O3 (corundum) have shown that the intensities of SRM1976c can be simulated by the March-Dollase preferred orientation model along the (001)-direction. Diffraction intensities of randomly oriented corundum crystallites have been calculated from electron density data obtained by conventional and density functional theory (DFT) calculations, on the assumption of independent and similar atomic displacements for Al and O atoms. The results of DFT calculations support that the strongest peak of randomly oriented α-Al2O3 crystalline powder should be 113-reflection, though the intensities simulated by DFT calculations are not closer to NIST SRM676a intensities than those expected for a fully ionized model ${\rm Al}_2^{3 + } {\rm O}_3^{2-}$. Diffraction data of two types of relatively fine (nominally 2–3 μm and ca 0.3 μm) α-Al2O3 powder have been collected and processed by a deconvolutional treatment (DCT). Integrated peak intensities extracted from the DCT data by an individual peak profile fitting method also support that the 113-reflection is the strongest reflection of randomly oriented α-Al2O3 crystalline powder.
Quantitative phase analysis (QPA) of slags is complex due to the natural richness of phases and variability in sample composition. The number of phases frequently exceeds 10, with certain slag types (EAF, BOF, blends, stainless) having extreme peak overlap, making identification difficult. Another convolution arises from the variable crystallite sizes of phases found in slag, as well as the mixture of crystalline and amorphous components specific to each slag type. Additionally, polymorphs are common because of the complexity of the steelmaking and slag cooling processes, such as the cation-doped calcium aluminum silicate (Ca3Al2O6, C3A, Z = 24) supercell in LMF slag. References for these doped variants may not exist or in many cases are not known in advance, therefore it is incumbent on the analyzer to be aware of such discrepancies and choose the best available reference. All issues can compound to form a highly intricate QPA and have prevented previous methods of QPA from accurately measuring phase components in slag. QPA was performed via the internal standard method using 8 wt% ZnO as the internal standard and JADE Pro's Whole Pattern Fitting analysis. For each phase, five variables (lattice parameters, preferred orientation, scale factor, temperature factor, and crystallite size) must be accounted for during quantitation, with a specific emphasis on not refining crystallite sizes for iron oxides and trace phases as they are inclined to over-broaden and interact with the background to improve the goodness of fit (R/E value). Preliminary investigations show somewhat reliable results with the use of custom file sets created within PDF-4+ specifically targeted toward slag minerals to further regulate and normalize the analysis process. The objective of this research is to provide a standard protocol for collecting data, as well as to update methodologies and databases for QPA, to the slag community for implementation in a conventional laboratory setting. Currently, Whole Pattern Fitting “Modified” Rietveld block refinement coupled with the addition of a ZnO internal standard gives the most accurate QPA results, though further research is needed to improve upon the complex issues found in this study of the QPA of slags.
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the brain. In terms of basic research, it aims at improving our understanding of biological intelligence by replicating aspects of its physical substrate – spiking neurons, synaptic plasticity etc. – and harness them towards also replicating its function. With respect to technological advances, it aims to inherit the brain’s combination of computational prowess and extreme energy efficiency; this is thought to foster a plethora of applications, from large-scale neuromorphic systems for machine learning to small-scale edge devices for signal processing and control, for example in the form of wearables for healthcare or adaptive sensors/processors for autonomous agents. The demand and usefulness of neuromorphic computing in bioelectronics is likely to increase in the future as researchers continue to explore its capabilities and develop new applications.
This article analyzes raw driving data of passenger cars in the city of Semnan in Iran, with the objective of understanding the impact of traffic conditions at different times of day (morning, noon, evening, and night). For this study, two cars, the Toyota Prius and the Peugeot Pars (or the IKCO Persia), were used, and the data of speed, longitude, latitude, and altitude of the vehicles were acquired. This data was collected over a week (July 21–28, 2022) for a distance of 670 km (13 hr), with the help of the Global Positioning System application, and were presented for both cars. In addition to this, the data on fuel consumption and average speed, based on the Electronic Control Unit in the Prius, was also collected. Finally, a sensitivity analysis was done on the features of the raw data, based on the Principal Component Analysis method.
Although being an old concern, phosphate analysis is still a tremendous challenge. While many different experimental techniques are found in the literature, very few use powder X-ray diffraction (PXRD) patterns for quantitative phase analysis of different phosphate types. Our measurements performed in four commercial samples of diammonium hydrogen phosphate ((NH4)2HPO4) (DAP) show the existence of phosphate contamination mixtures, such as ammonium dihydrogen phosphate (NH4H2PO4) (ADP). The larger the amount of ADP, the larger the microstrain induced in the DAP phase, which impacts both the aggregation of the nanoparticles in solution and the final anticancer activity of the nanostructure. This study shows that PXRD is an excellent technique for quantitative phase analysis to determine the presence and amount of phosphate contamination in diammonium hydrogen phosphate samples.
Programmable active matter (PAM) combines information processing and energy transduction. The physical embodiment of information could be the direction of magnetic spins, a sequence of molecules, the concentrations of ions, or the shape of materials. Energy transduction involves the transformation of chemical, magnetic, or electrical energies into mechanical energy. A major class of PAM consists of material systems with many interacting units. These units could be molecules, colloids, microorganisms, droplets, or robots. Because the interaction among units determines the properties and functions of PAMs, the programmability of PAMs is largely due to the programmable interactions. Here, we review PAMs across scales, from supramolecular systems to macroscopic robotic swarms. We focus on the interactions at different scales and describe how these (often local) interactions give rise to global properties and functions. The research on PAMs will contribute to the pursuit of generalised crystallography and the study of complexity and emergence. Finally, we ponder on the opportunities and challenges in using PAM to build a soft-matter brain.
Pb–Zr–Ti–O (PZT) perovskites span a large solid-solution range and have found widespread use due to their piezoelectric and ferroelectric properties that also span a large range. Crystal structure analysis via Rietveld refinement facilitates materials analysis via the extraction of the structural parameters. These parameters, often obtained as a function of an additional dimension (e.g., pressure), can help to diagnose materials response within a use environment. Often referred to as “in-situ” studies, these experiments provide an abundance of data. Viewing structural changes due to applied pressure conditions can give much-needed insight into materials performance. However, challenges exist for viewing/presenting results when the details are inherently three-dimensional (3D) in nature. For PZT perovskites, the use of polyhedra (e.g., Zr/Ti–O6 octahedra) to view bonding/connectivity is beneficial; however, the visualization of the octahedra behavior with pressure dependence is less easily demonstrated due to the complexity of the added pressure dimension. We present a more intuitive visualization by projecting structural data into virtual reality (VR). We employ previously published structural data for Pb0.99(Zr0.95Ti0.05)0.98Nb0.02O3 as an exemplar for VR visualization of the PZT R3c crystal structure between ambient and 0.62 GPa pressure. This is accomplished via our in-house CAD2VR™ software platform and the new CrystalVR plugin. The use of the VR environment enables a more intuitive viewing experience, while enabling on-the-fly evaluation of crystal data, to form a detailed and comprehensive understanding of in-situ datasets. Discussion of methodology and tools for viewing are given, along with how recording results in video form can enable the viewing experience.
A new ternary intermetallic compound Al3GaCu9 was synthesized experimentally. A high-quality powder diffraction pattern of the compound was collected by an X-ray diffractometer, and its crystal structure was determined using the Rietveld refinement method. Results show that the compound has a cubic cell with the Al4Cu9 structure type (space group $P\bar{4}3m$ and Pearson symbol cP52). The lattice parameter a = 8.7132(3) Å, unit-cell volume V = 661.52 Å3, calculated density Dcalc = 7.26 g/cm3, and Z = 4. The residual factors converge to Rp = 2.96%, Rwp = 4.06%, and Rexp = 2.57%. The experimentally obtained reference intensity ratio value is 7.04.
Herein, a new method to synthesise epoxide-based sequence-controlled polymers via anionic ring-opening monomer addition, a form of anionic ring-opening polymerisation, is presented. This technique allows in combination with post-polymerisation modification (PPM) reactions for the successful preparation of modified mPEG-b-oligo(allyl glycidyl ether) featuring the incorporation of one repeating unit on average at a time. Due to the possible introduction of a vast variety of molecules to the polymeric system via PPM reactions, a multitude of advanced functional polymeric materials can be generated. This, in combination with the chain extension reactions, allows for the synthesis of well-controlled and programmable architectures with particular properties. The structure of the sequence-controlled polymer was confirmed via 1H NMR spectroscopy, size exclusion chromatography, attenuated total reflection Fourier-transform infrared spectroscopy, and differential scanning calorimetry.
Despite the growing interest in addiction research, which demonstrates the potential predictive role of adverse childhood experiences (ACEs), little is known about their impact on the psychological symptoms of craving.
Methods
After reviewing the relevant diagnostic criteria for addiction and comorbid mental disorders along with routinely collected clinical and service-use data, 208 outpatients were assessed on the study protocol. Following the recruitment phase, nominal and ordinal data were analyzed using nonparametric methods.
Results
Most of the outpatients reported ACEs (89.1%) and experienced cravings (73.4–95.7%). A positive association between ACEs and either intention and preplanning (r = .14, p < .05) or lack of control (r = .15; p < .05) of the craving behavior was found.
Conclusion
Craving behavior in addiction remains a subject of debate. Although correlation analyses showed significant associations between reported ACEs and measures of craving, they were relatively small.
Parkinson’s disease (PD) is an irreversible neurodegenerative disorder clinically manifesting in uncontrolled motor symptoms. There are two primary hallmark features of Parkinson’s disease—an irreversible loss of dopaminergic neurons of the substantia nigra pars compacta and formation of intracellular insoluble aggregates called Lewy bodies mostly composed of alpha-synuclein. Using a clinical improvements-first approach, we identified several clinical trials involving consumption of a specific diet or nutritional supplementation that improved motor and nonmotor functions. Here, we aimed to investigate if and how pyrroloquinoline quinone (PQQ) compound disrupts preformed alpha-synuclein deposits using SH-SY5Y cells, widely used Parkinson’s disease cellular model. SH-SY5Y neuroblastoma cells, incubated in presence of potassium chloride (KCl) to induce alpha-synuclein protein aggregation, were treated with PQQ for up to 48 hr. Resulting aggregates were examined and quantified using confocal microscopy. Overall, nutritional compound PQQ reduced the average number and overall size of intracellular cytoplasmic alpha-synuclein aggregates in a PD cellular model.
This paper proposes a methodology for architecting microstructures with extremal stiffness, yield, and buckling strength using topology optimisation. The optimised microstructures reveal an interesting transition from simple lattice-like structures for yield-dominated situations to hierarchical lattice structures for buckling-dominated situations. The transition from simple to hierarchical is governed by the relative yield strength of the constituent base material as well as the volume fraction. The overall performances of the optimised microstructures indicate that maximum strength is determined by the buckling strength at low-volume fractions and yield strength at higher-volume fractions, regardless of the base material’s relative yield strength. The non-normalised properties of the optimised microstructures show that higher base material Young’s modulus leads to both higher Young’s modulus and strength of the architected microstructures. Furthermore, the polynomial order of the maximum strength lines with respect to mass density obtained from the optimised microstructures reduces as base material relative yield strength decreases, reducing from 2.3 for buckling-dominated thermoplastic polyurethane to 1 for yield-dominated steel microstructures.
Mechanical metamaterials have attracted extensive attention. This paper reports a metamaterial with tunable elastic wave bandgaps based on bistable buckling structure. First, we find that deformation of two symmetric buckling shells is intrinsically asymmetric, which blocks the realisation of robust tunability. Based on an analytical model, we clarify that the mechanisms for this intrinsic asymmetricity are the bifurcations on force–deformation curves. Then we propose a superposition method of buckling shells, which can realise the symmetric deformation for robust tunable stiffness. Using this variable-stiffness oscillator, we design a metamaterial sandwich beam, and numerically and experimentally demonstrate its tunable bandgap for vibration suppression. This paper presents the unusual deformation process of buckling elements widely used for constructing metamaterials, and provides a robust way to realise metamaterials with tunable vibration bandgaps.
Artificial intelligence (AI) has advanced considerably, AlphaFold2 protein models are as good as X-ray results, language models like ChatGPT can pass MBA and medical exams, and deep learning models Midjourney, and Stable Diffusion can emulate artistic styles. Given current progress, could text-based inputs be used for the generative design of artificial proteins, pathways, or even organisms, with traits designed purely by AI? Existing strategies for biotechnology design are founded in knowledge-based approaches, such as rational enzyme engineering or whole pathway design using synthetic biology, often borrowing “parts” from other organisms. Alternatively, desired traits are achieved via random mutagenesis with iterative selection procedures. Both are costly in terms of acquiring knowledge and undertaking experimentation. Recently, advances in protein language models have allowed AI to implicitly “learn” properties that allow sequences to be folded alongside other embedded learning techniques for function prediction from primary sequences. Thus, AI offers varied routes to predicting biological outcomes from DNA sequences. However, AI has not yet been extensively used to design novel functions, despite the wealth of functionally annotated protein products at our fingertips. Thus, generative protein language models for biodesign represent a promising future. We seek to explore current technological limits and challenges, investigate new avenues and methodologies to make this possible and broach discussion around wider issues arising from AI-designed life.
As a major approach for controlling electromagnetic (EM) waves, metamaterials have experienced an abundant and rapid development in the 21st century. They have provided flexible and powerful techniques for controlling EM waves and brought many unique applications that are difficult to realise with natural materials. With increasing demands on dynamic controls of the EM waves, many innovations have been conducted in both three-dimensional metamaterials and two-dimensional metasurfaces, in which the meta-atom has been gradually evolved from passive to active. In 2014, coding and digital mechanisms were initially introduced to the metamaterials, further advancing the appearance of digitally programmable metamaterials. The programmable metamaterials have shown great potentials in not only real-time manipulations of the EM waves, but also direct information processing on the EM wave level. In this article, we present an in-depth review of the programmable EM metamaterials and metasurfaces, focusing on the programmable features including theoretical concepts, implementing methods and applications in EM controls. We first give a short retrospect of traditional metamaterials and metasurfaces, followed by the concepts and detailed discussions of digital coding and field-programmable metamaterials. Then, we introduce space-domain, time-domain and space–time-domain programmable metamaterials and metasurfaces, mainly focusing on their theories, functionalities, experimental implementations, and system-level applications. Finally, we conclude the current advances of the programmable metamaterials and metasurfaces, and give a prospect for the future developments.
X-ray powder diffraction data, including unit cell parameters and space group assignment, for the ESP15228 species of C19H34O5 formula, are here reported [a = 6.0434(6), b = 12.2543(6), c = 14.0285(8) Å, α = 86.584(3), β = 85.707(10), γ = 78.801(5)°, V = 1015.2(1) Å3, Z = 2, ρcalc = 1.152 g cm−3, and space group P-1]. All measured lines were indexed and no detectable impurities were observed.