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Zeolite crystallization predominantly occurs by nonclassical pathways involving the attachment of complex (alumino)silicate precursors to crystal surfaces, yet recurrent images of fully crystalline materials with layered surfaces are evidence of classical growth by molecule attachment. Here we use in situ atomic force microscopy to monitor three distinct mechanisms of two-dimensional (2D) growth of zeolite A where we show that layer nucleation from surface defects is the most common pathway. Direct observation of defects was made possible by the identification of conditions promoting layered growth, which correlates to the use of sodium as an inorganic structure-directing agent, whereas its replacement with an organic results in a nonclassical mode of growth that obscures 2D layers and markedly slows the rate of crystallization. In situ measurements of layered growth reveal that undissolved silica nanoparticles in the synthesis medium can incorporate into advancing steps on crystal surfaces to generate defects (i.e., amorphous silica occlusions) that largely go undetected in literature. Nanoparticle occlusion in natural and synthetic crystals is a topic of wide-ranging interest owing to its relevance in fields spanning from biomineralization to the rational design of functional nanocomposites. In this study, we provide unprecedented insight into zeolite surface growth by molecule addition through time-resolved microscopy that directly captures the occlusion of silica nanoparticles and highlights the prevalent role of defects in zeolite crystallization.

Inorganic perovskite wafers with good stability and adjustable sizes are promising in X-ray detection but the high synthetic temperature is a hindrance. Herein, dimethyl sulfoxide (DMSO) is used to prepare the CsPbBr 3 micro-bricks powder at room temperature. The CsPbBr 3 powder has a cubic shape with few crystal defects, small charge trap density, and high crystallinity. A trace amount of DMSO attaches to the surface of the CsPbBr 3 micro-bricks via Pb-O bonding, forming the CsPbBr 3 -DMSO adduct. During hot isostatic processing, the released DMSO vapor merges the CsPbBr 3 micro-bricks, producing a compact and dense CsPbBr 3 wafer with minimized grain boundaries and excellent charge transport properties. The CsPbBr 3 wafer shows a large mobility-lifetime (μτ) product of 5.16 × 10 − 4 cm 2 ·V − 1 , high sensitivity of 14,430 μC·Gy air −1 ·cm −2 , low detection limit of 564 nGy air ·s −1 , as well as robust stability in X-ray detection. The results reveal a novel strategy with immense practical potential pertaining to high-contrast X-ray detection.

To explore the effects of adulteration crystal defect on performances of hexanitrohexaazaisowurtzitane/octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (CL-20/HMX) cocrystal explosive, the CL-20/HMX cocrystal model was established based on its lattice parameters. Besides, defective CL-20/HMX cocrystal models with adulteration ratios of 1.85%, 3.70%, 5.56%, 7.41%, and 9.26% were also established, respectively. Molecular dynamics (MD) method was selected to optimize the crystal structure and predict performances of each model. The correlated energies and parameters, including binding energy, trigger bond rupture energy, cohesive energy density (CED), and detonation parameters were calculated and compared. Results show that binding energy in defective CL-20/HMX models is decreased by 12.02–307.05 kJ/mol, implying that the intermolecular interaction energy between CL-20 and HMX molecules is decreased and stability is weakened. Adulteration crystal defect makes the trigger bond rupture energy and CED decreased by 1.06–22.04 kJ/mol and 0.003–0.122 kJ/cm 3 respectively, indicating that the sensitivity of defective models is increased and safety is worsened. The crystal density of defective cocrystal models is decreased by 0.005–0.142 g/cm 3 , detonation velocity is decreased by 53–508 m/s, and detonation pressure is decreased by 0.52–5.09 GPa, meaning that defective cocrystal models have lower energy density than that of primitive model. Hence, adulteration crystal defect will bring passive influence on stability, safety and energetic performance of CL-20/HMX cocrystal explosive.

Crystal Property Prediction (CPP) and Crystal Structure Prediction (CSP) play an important role in accelerating the design and discovery of advanced materials across various scientific disciplines. Traditional computational approaches to CSP/CPP often face challenges such as high computational costs, limited scalability, and difficulties in exploring complex energy surfaces. In recent years, the combination of machine learning (ML) has emerged as a powerful approach to overcome these limitations, offering data-driven methods that enhance prediction accuracy while significantly reducing computational expenses. This review provides a comprehensive overview of the evolution of CSP and CPP methodologies, with particular emphasis on the transition from classical optimization algorithms to modern ML-based methods. Various supervised and unsupervised ML algorithms applied in this field are discussed in detail. Beyond structure and property prediction, recent advancements in ML-based modeling of crystal defects are also reviewed. Moreover, several recent case studies on CSP/CPP are presented to demonstrate the practical effectiveness of ML approaches. Finally, the review discusses current challenges and provides recommendations for future research in ML-based investigations of CSP/CPP.

The mechanical properties of PBT-based azide propellants, composed of a 3,3′-bis(azidomethyl)oxetane/tetrahydrofuran (PBT) copolymer matrix and defective ammonium perchlorate (AP) crystals, are significantly influenced by the matrix–crystal interface. This study employed molecular dynamics (MD) simulations to examine interfacial effects on mechanical performance under uniaxial tensile deformation. Models with varying cross-linking densities (70%, 80%, 90%) and AP defect widths (20 Å, 30 Å, 40 Å) were analyzed to assess the effects of temperature, strain rate, cross-linking degree, and defect size on interfacial adhesion strength and failure mechanisms. Results indicate that at low temperatures, the interface exhibited high stress peaks and brittleness characteristics, transitioning to plastic flow and enhanced ductility at higher temperatures. Cross-linking density significantly affects interfacial strength: a 90% cross-linking degree achieved the highest stress peak and optimal tensile resistance, whereas lower cross-linking resulted in weaker stress transfer and accelerated post-peak stress decay. Higher strain rates increased peak stress and shortened deformation times, while lower strain rates promoted molecular rearrangement, enhancing tensile resistance. Defect size also plays a crucial role, with smaller defects maintaining interfacial dominance, whereas larger defects shift failure toward the bulk matrix, reducing stress transfer efficiency. These findings provide atomic-scale insights into interfacial defects and key material parameters, offering theoretical guidance for optimizing the structural stability of composite propellants.

In this study, we investigate heavy ion irradiation effects on commercial 650 V p-GaN normally-off HEMTs. Ge and Cl ions are used to irradiate the GaN devices in the experiments. Ge and Cl ion beam irradiation have little impact on the output characteristics of GaN devices. After heavy ion irradiation, the leakage currents between source and drain electrodes increase significantly under off-state, decreasing the breakdown voltage (BV DS ) sharply. Additionally, Ge and Cl ion irradiation have little effect on the trap states under the gate electrode; thus, the gate leakage currents increase slightly. Many line-shaped crystal defects extending from the surface to the GaN buffer layer can be captured using a transmission electron microscope after Ge/Cl ion irradiation. The buffer layers of the irradiated devices were damaged, and the leakage path was generated in the buffer layer. Defect percolation process in buffer layer is the dominant factor of irradiated high-voltage GaN device failure.

We investigate prototypical profiles of point defects in two-dimensional liquid crystals within the framework of Landau–de Gennes theory. Using boundary conditions characteristic of defects of index k /2, we find a critical point of the Landau–de Gennes energy that is characterised by a system of ordinary differential equations. In the deep nematic regime, b 2 small, we prove that this critical point is the unique global minimiser of the Landau–de Gennes energy. For the case b 2 = 0 , we investigate in greater detail the regime of vanishing elastic constant L → 0 , where we obtain three explicit point defect profiles, including the global minimiser.

Crystalline defects, such as line-like dislocations, play an important role for the performance and reliability of many metallic devices. Their interaction and evolution still poses a multitude of open questions to materials science and materials physics. In-situ transmission electron microscopy (TEM) experiments can provide important insights into how dislocations behave and move. The analysis of individual video frames from such experiments can provide useful insights but is limited by the capabilities of automated identification, digitization, and quantitative extraction of the dislocations as curved objects. The vast amount of data also makes manual annotation very time consuming, thereby limiting the use of deep learning (DL)-based, automated image analysis and segmentation of the dislocation microstructure. In this work, a parametric model for generating synthetic training data for segmentation of dislocations is developed. Even though domain scientists might dismiss synthetic images as artificial, our findings show that they can result in superior performance. Additionally, we propose an enhanced DL method optimized for segmenting overlapping or intersecting dislocation lines. Upon testing this framework on four distinct real datasets, we find that a model trained only on synthetic training data can also yield high-quality results on real images–even more so if the model is further fine-tuned on a few real images. Our approach demonstrates the potential of synthetic data in overcoming the limitations of manual annotation of TEM image data of dislocation microstructure, paving the way for more efficient and accurate analysis of dislocation microstructures. Last but not least, segmenting such thin, curvilinear structures is a task that is ubiquitous in many fields, which makes our method a potential candidate for other applications as well.

In this study, we report the impact of structural 4H-SiC epitaxial defects on the electrical characteristics and blocking capabilities of SiC power devices. The detection and classification of the various crystal defects existing in 4H-SiC epitaxial layers and substrates was carried out with a commercial inspection tool using an optical microscope with a photoluminescence channel (PL). After the fabrication of dedicated test structures, devices that contain a single crystal defect were selected and electrically tested in reverse bias mode. Photon emission microscopy was performed to enable the localization of the leakage current spots within the devices. Thus, a direct correlation of the various crystal defects with the reduced blocking capability mechanism was made. This evaluation helps to set directions and build a strategy towards the reduction of critical defects in order to improve the performance of SiC devices for high power applications.

In this work, the Zn 1-x Na x O (x = 0, 0.01, 0.03, and 0.05) thin film gas sensors were prepared via the sol-gel spin coating method to study the impact of sodium on structural, morphological, elemental, electrical, and gas sensing applications. Crystal structure (XRD), energy dispersive X-ray analysis (EDX), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FESEM), four-probe hall measurement, and NO 2 gas sensing properties were investigated to ascertain the elemental composition, morphology, defect density states, working temperature, response/recovery time, stability, selectivity, and repeatability. The 3 wt.%Na:ZnO gas sensor displays a gas-accessible structure with more oxygen vacancies, remarkable stability, and sensitivity towards NO 2 gas at an optimum temperature (210 °C). A possible gas-sensing mechanism was also discussed and correlated with structural, elemental, morphological, and electrical properties. Graphical Abstract Pure, 1 wt.% Na-doped, 3 wt.% Na-doped, and 5 wt.% Na-doped ZnO thin film sensors were fabricated via the sol-gel spin coating technique and exhibited a hexagonal wurtzite structure. The incorporation of Na into the ZnO matrix was confirmed by EDX and XPS analysis. The 3%Na-doped ZnO thin film exhibits more oxygen vacancies and carrier concentration. The 3%Na-doped ZnO thin film shows an enhanced gas sensing response of 22.53 against 75 ppm of NO 2 gas. Good selectivity, outstanding stability, rapid response and recovery times, and excellent reproducibility are all demonstrated by the 3%Na-doped ZnO. Highlights Through sol-gel spin coating technique, pure, (1,3, and 5) wt.% Na-doped ZnO thin film sensors were fabricated and characterized. 3 wt.%Na-doped ZnO thin film with porous structure exhibit more oxygen vacancies and carrier density. 3%Na-doped ZnO thin film shows enhanced gas sensing performance against 75 ppm of NO 2 gas.