Giant Uniaxial Magnetocrystalline Anisotropy in SmCrGe3.

Magnetic anisotropy is a crucial characteristic for enhancing the spintronic device performance. The synthesis of SmCrGe3 single crystals through a high-temperature solution method has led to the determination of uniaxial magnetocrystalline anisotropy. Phase verification was achieved by using scanning transmission electron microscopy (STEM), powder, and single-crystal X-ray diffraction techniques. Electrical transport and specific heat measurements indicate a Curie temperature (TC) of approximately 160 K, while magnetization measurements were utilized to determine the anisotropy fields and constants. Curie-Weiss fitting applied to magnetization data suggests the contribution of both Sm and Cr in the paramagnetic phase. Additionally, density functional theory (DFT) calculations explored the electronic structures and magnetic properties of SmCrGe3, revealing a significant easy-axis single-ion Sm magnetocrystalline anisotropy of 16 meV/fu. Based on the magnetization measurements, easy-axis magnetocrystalline anisotropy at 20 K is 13 meV/fu.

Image tampering detection based on RDS-YOLOv5 feature enhancement transformation.

Malicious image tampering has gradually become another way to threaten social stability and personal safety. Timely detection and precise positioning can help reduce the occurrence of risks and improve the overall safety of society. Due to the limitations of highly targeted dataset training and low-level feature extraction efficiency, the generalization and actual performance of the recent tampered detection technology have not yet reached expectations. In this study, we propose a tampered image detection method based on RDS-YOLOv5 feature enhancement transformation. Firstly, a multi-channel feature enhancement fusion algorithm is proposed to enhance the tampering traces in tampered images. Then, an improved deep learning model named RDS-YOLOv5 is proposed for the recognition of tampered images, and a nonlinear loss metric of aspect ratio was introduced into the original SIOU loss function to better optimize the training process of the model. Finally, RDS-YOLOv5 is trained by combining the features of the original image and the enhancement image to improve the robustness of the detection model. A total of 6187 images containing three forms of tampering: splice, remove, and copy-move were used to comprehensively evaluate the proposed algorithm. In ablation test, compared with the original YOLOv5 model, RDS-YOLOv5 achieved a performance improvement of 6.46%, 5.13%, and 3.15% on F1-Score, mAP50 and mAP95, respectively. In comparative experiments, using SRIOU as the loss function significantly improved the model's ability to search for the real tampered regions by 2.54%. And the RDS-YOLOv5 model trained by the fusion dataset further improved the comprehensive detection performance by about 1%.

A method for assessing the risk of rockburst based on coal-rock mechanical properties and In-Situ ground stress.

With the increase in mining depth and intensity, dynamic disasters such as rockburst in mines are becoming more severe. Deep resource extraction is characterized by a high in-situ stress geological environment, closely associated with geological dynamic disasters. However, there is currently no quantitative analysis method for the correlation between the two. In this study, an elastic energy density calculation method is employed, considering the dissipative effect of the self-weight stress field on the tectonic stress field. The remaining energy, referred to as impact energy, is used to classify the risk of coal seam impact, providing a computational method for rapid assessment of impact risk before mining production. The proposed calculation method is compared with 22 mine impact engineering practices in the literature, showing accurate predictions for 21 mines. Since measuring in-situ stress and coal seam physical and mechanical properties is a preliminary work in coal seam extraction, the comprehensive analysis of these data holds significant research and practical value.

Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures.

Optical properties in solids, such as refractive index and absorption, hold vast applications ranging from solar panels to sensors, photodetectors, and transparent displays. However, first-principles computation of optical properties from crystal structures is a complex task due to the high convergence criteria and computational cost. Recent progress in machine learning shows promise in predicting material properties, yet predicting optical properties from crystal structures remains challenging due to the lack of efficient atomic embeddings. Here, Graph Neural Network for Optical spectra prediction (GNNOpt) is introduced, an equivariant graph-neural-network architecture featuring universal embedding with automatic optimization. This enables high-quality optical predictions with a dataset of only 944 materials. GNNOpt predicts all optical properties based on the Kramers-Krönig relations, including absorption coefficient, complex dielectric function, complex refractive index, and reflectance. The trained model is applied to screen photovoltaic materials based on spectroscopic limited maximum efficiency and search for quantum materials based on quantum weight. First-principles calculations validate the efficacy of the GNNOpt model, demonstrating excellent agreement in predicting the optical spectra of unseen materials. The discovery of new quantum materials with high predicted quantum weight, such as SiOs, which host exotic quasiparticles with multifold nontrivial topology, demonstrates the potential of GNNOpt in predicting optical properties across a broad range of materials and applications.

Large Spin Polarization from symmetry-breaking Antiferromagnets in Antiferromagnetic Tunnel Junctions.

Efficient detection of the magnetic state is a critical step towards useful antiferromagnet-based spintronic devices. Recently, finite tunneling magnetoresistance (TMR) has been demonstrated in tunnel junctions with antiferromagnetic electrodes, however, these studies have been mostly limited to junctions with two identical antiferromagnet (AFM) electrodes, where the matching of the spin-split Fermi surfaces played critical role. It remains unclear if AFMs can provide a finite net spin polarization, and hence be used as a spin polarizer or detector. In this work, we experimentally fabricate single-sided antiferromagnetic tunnel junctions consisting of one AFM electrode (Mn3Sn) and one ferromagnet (FM) electrode (CoFeB), where the spin polarized tunneling transport from AFM is detected by the FM layer. We observe a high TMR at cryogenic temperature (>100% at 10 K) in these asymmetric AFM tunnel junctions, suggesting a large effective spin polarization from Mn3Sn despite its nearly vanishing magnetization. The large TMR is consistent with recent theoretical studies where the broken symmetry in non-collinear AFMs is predicted to lift the spin degeneracy in the band structure. Our work provides strong evidence that spin polarized electrical transport can be achieved from AFMs.

Numerical simulation of blasting behavior of rock mass with cavity under high in-situ stress.

With the shift of coal seam mining to the deep, the in-situ stress of coal and rock mass increases gradually. High ground stress can limit the generation of rock cracks caused by blasting, and blasting usually shows different crushing states than low stress conditions. In order to study the blasting expansion rule of rock mass with cavity under high ground stress and the rock mass fracture state under different side stress coefficients. In this paper, the effective range of blasting and the stress distribution under blasting load are analyzed theoretically. The RHT (Riedel-Hiermaier-Thoma) model is used to numerically simulate the blasting process of rock mass with cavity under different ground stress, and the influence of ground stress and lateral pressure coefficient on the crack growth of rock mass is studied. The results show that when there is no ground stress, the damage cracks in rock mass are more concentrated in the horizontal direction and the fracture development tends to the direction where the holes are located, which confirms the guiding effect and stress concentration effect of the holes in rock mass, which helps to promote the crack penetration between the hole and the hole. The length difference of horizontal and vertical damage cracks in rock mass increases with the increase of horizontal and vertical stress difference. Under the same lateral stress coefficient, the larger the horizontal and vertical stress difference is, the stronger the inhibition effect on crack formation is. For blasting of rock mass with high ground stress, the crack formation length between gun holes decreases with the increase of stress level, and the crack extends preferentially in the direction of higher stress. Therefore, the placement of gun holes along the direction of greater stress and the shortening of hole spacing are conducive to the penetration of cracks between gun holes and empty holes. The research can provide reference for rock breaking behavior of deep rock mass blasting.

Virtual node graph neural network for full phonon prediction.

Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility.

Synthesis of Dienamides via Palladium-catalyzed Oxidative N-α,ß-Dehydrogenation of Amides.

Enamides and their derivatives are prominent bioactive pharmacophores found in various bioactive molecules. Herein we report a palladium-catalyzed oxidative N-α,ß-dehydrogenation of amides to produce a range of enamides with high yields and excellent tolerance toward different functional groups. Mechanistic studies indicate that the reaction involves allylic C(sp3)-H activation followed by ß-H elimination. The effectiveness of this approach is demonstrated through late-stage functionalization of bioactive molecules and the synthesis of valuable compounds through product elaboration.

Tetris-inspired detector with neural network for radiation mapping.

Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.

Numerical simulation of rock blasting under different in-situ stresses and joint conditions.

High primary rock stress can limit the generation of rock cracks caused by blasting, and blasting usually shows different rock breaking states under different primary rock stress conditions. There are a large number of naturally formed joints in rock mass, due to the limitations of laboratory tests, a numerical model of jointed rock mass was established using LS-DYNA software to investigate the evolution of blasting damage under various in-situ stresses and open joints. In this simulation, using the Lagrange-Euler (ALE) procedure and the equation of state (JWL) that defines explosive materials, the study considered different joint thicknesses (2cm, 4cm, and 6cm), joint angles (0°, 30°, 60°, and 90°), and in-situ stress conditions (lateral stress coefficients of 0.5, 1, and 2, with vertical in-situ stresses of 10MPa and 20MPa), through stress analysis and damage area comparison, the relationship between damage crack propagation and horizontal and vertical stress difference is explored. The research aimed to understand the mechanisms underlying crack initiation and propagation. The results show that: (1) The presence of joints exerts a barrier effect on the expansion and penetration of cracks. When explosion stress waves reach the joint surface, their propagation is impeded, leading to the diffusion of wing cracks at the joint ends. When the lateral stress coefficient and joint angle are the same, an increase in initial in-situ stress results in a reduction in the area of the blasting damage zone. (2) Under the same initial in-situ stress conditions, the area of the blasting damage zone initially increases and then decreases with an increasing joint angle. However, it remains larger than that without a joint, and there exists an optimal angle that maximizes the damage area. In the simulated conditions, the area of damage cracks is greatest when the joint angle is 60° dip angle. (3) The presence of initial in-situ stress has a certain impact on the initiation and expansion of blasting cracks. The degree and nature of this influence are not solely related to the lateral stress coefficient but also depend on the joint's angle and thickness. When in-situ stress is present, the initial in-situ stress field's pressure is not conducive to the initiation and propagation of blasting cracks. However, the existence of a joint has a noticeable guiding and promoting effect on crack propagation, and the pattern of crack propagation is influenced by both joint and in-situ stress conditions.

Field-free deterministic switching of all-van der Waals spin-orbit torque system above room temperature.

Two-dimensional van der Waals (vdW) magnetic materials hold promise for the development of high-density, energy-efficient spintronic devices for memory and computation. Recent breakthroughs in material discoveries and spin-orbit torque control of vdW ferromagnets have opened a path for integration of vdW magnets in commercial spintronic devices. However, a solution for field-free electric control of perpendicular magnetic anisotropy (PMA) vdW magnets at room temperatures, essential for building compact and thermally stable spintronic devices, is still missing. Here, we report a solution for the field-free, deterministic, and nonvolatile switching of a PMA vdW ferromagnet, Fe3GaTe2, above room temperature (up to 320 K). We use the unconventional out-of-plane anti-damping torque from an adjacent WTe2 layer to enable such switching with a low current density of 2.23 × 106 A cm-2. This study exemplifies the efficacy of low-symmetry vdW materials for spin-orbit torque control of vdW ferromagnets and provides an all-vdW solution for the next generation of scalable and energy-efficient spintronic devices.

Current-induced switching of a van der Waals ferromagnet at room temperature.

Recent discovery of emergent magnetism in van der Waals magnetic materials (vdWMM) has broadened the material space for developing spintronic devices for energy-efficient computation. While there has been appreciable progress in vdWMM discovery, a solution for non-volatile, deterministic switching of vdWMMs at room temperature has been missing, limiting the prospects of their adoption into commercial spintronic devices. Here, we report the first demonstration of current-controlled non-volatile, deterministic magnetization switching in a vdW magnetic material at room temperature. We have achieved spin-orbit torque (SOT) switching of the PMA vdW ferromagnet Fe3GaTe2 using a Pt spin-Hall layer up to 320 K, with a threshold switching current density as low as [Formula: see text]1.69 [Formula: see text] 106 A cm-2 at room temperature. We have also quantitatively estimated the anti-damping-like SOT efficiency of our Fe3GaTe2/Pt bilayer system to be [Formula: see text], using the second harmonic Hall voltage measurement technique. These results mark a crucial step in making vdW magnetic materials a viable choice for the development of scalable, energy-efficient spintronic devices.

Preparation of high-performance monoazo disperse dyes bearing ester groups based on benzisothiazole and their dyeing performance on polyester fabrics.

To obtain high-performance disperse dyes, a series of azo disperse dyes containing different kinds of ester groups based on benzisothiazole were synthesized by the coupling reaction of diazotization of 3-amino-5-nitro [2,1] benzisothiazole with N-substituted aniline compounds bearing different ester moieties. The structures of the synthesized dyes were evaluated using Fourier transform infrared spectroscopy (FT-IR), nuclear magnetic resonance techniques (1H-NMR), and MS analysis. UV-Vis spectrophotometry methods were applied to study absorption maxima, molar extinction coefficients, and solvatochromic behaviors of the dyes, and time-dependent density functional theory (TD-DFT) simulations were applied to reveal the nature of the absorption spectrum properties. Polyester fabrics were colored using a high-temperature dyeing method under pressure, and the dyed fabrics exhibited deep and bright intense blue hues. In addition, excellent fastness properties, including washing fastness, sublimation fastness, rubbing fastness, and light fastness, were achieved.

Integrating network pharmacology and experimental verification to explore the mucosal protective effect of Chimonanthus nitens Oliv. Leaf Granule on ulcerative colitis.

ETHNOPHARMACOLOGICAL RELEVANCE: Chimonanthus nitens Oliv. Leaf Granule (COG) is a commonly used clinical preparation of traditional Chinese medicine for the treatment of cold, but there are folk reports that it can treat diarrhea and other gastrointestinal diseases. Therefore, the mechanism of COG in the treatment of ulcerative colitis with diarrhea as the main symptom needs to be studied. AIM OF THE STUDY: Combined network pharmacology and experimental validation to explore the mechanism of COG in the treatment of ulcerative colitis. MATERIALS AND METHODS: First, the main components of COG were characterized by liquid chromatography-mass spectrometry (LC-MS); subsequently, a network pharmacology approach was used to screen the effective chemical components and action targets of COG to construct a target network of COG for the treatment of ulcerative colitis (UC). The protein-protein interaction network (PPI) and literature reports were combined to identify the potential targets of COG for the treatment of UC. Finally, the predicted results of network pharmacology were validated by animal and cellular experiments. RESULTS: 19 components of COG were characterized by LC-MS, among which 10 bioactive components could act on 377 potential targets of UC. Key therapeutic targets were collected, including SRC, HSP90AA1, PIK3RI, MAPK1 and ESR1. KEGG results are enriched in pathways related to oxidative stress. Molecular docking analysis showed good binding activity of main components and target genes. Animal experiments showed that COG significantly relieved the colitis symptoms in mice, regulated the Treg/Th17 balance, and promoted the secretion of IL-10 and IL-4, along with the inhibition of IL-1ß and TNF-α. Additionally, COG reduced the apoptosis of colon epithelial cells, and significantly improved the levels of SOD, MAO, GSH-px, and inhibited MDA, iNOS, eNOS in colon. Also, it increased the expression of tight junction proteins such as ZO-1, Claudin1, Occludin and E-cadherin. In vitro experiments, COG inhibited the oxidative stress and inflammatory injury of HCT116 cells induced by LPS. CONCLUSIONS: Combining network pharmacology and in vitro and in vivo experiments, COG was verified to have a good protective effect in UC, which may be related to enhancing antioxidation in colon tissues.

Three-dimensional flat bands in pyrochlore metal CaNi2.

Electronic flat-band materials host quantum states characterized by a quenched kinetic energy. These flat bands are often conducive to enhanced electron correlation effects and emergent quantum phases of matter1. Long studied in theoretical models2-4, these systems have received renewed interest after their experimental realization in van der Waals heterostructures5,6 and quasi-two-dimensional (2D) crystalline materials7,8. An outstanding experimental question is if such flat bands can be realized in three-dimensional (3D) networks, potentially enabling new materials platforms9,10 and phenomena11-13. Here we investigate the C15 Laves phase metal CaNi2, which contains a nickel pyrochlore lattice predicted at a model network level to host a doubly-degenerate, topological flat band arising from 3D destructive interference of electronic hopping14,15. Using angle-resolved photoemission spectroscopy, we observe a band with vanishing dispersion across the full 3D Brillouin zone that we identify with the pyrochlore flat band as well as two additional flat bands that we show arise from multi-orbital interference of Ni d-electrons. Furthermore, we demonstrate chemical tuning of the flat-band manifold to the Fermi level that coincides with enhanced electronic correlations and the appearance of superconductivity. Extending the notion of intrinsic band flatness from 2D to 3D, this provides a potential pathway to correlated behaviour predicted for higher-dimensional flat-band systems ranging from tunable topological15 to fractionalized phases16.

A multi-dimensional fusion strategy similarity measure method for patent application technology disclosure document.

Patent application technology disclosure document is one of the important bases for judging patent novelty and uniqueness. Automated evaluation can effectively solve the problems of long time and strong subjectivity of human evaluation. The text similarity evaluation algorithm based on corpus and deep learning technology has problems such as insufficient amount of cross-library learning data and insufficient core content tendency in the similarity judgment of patent application technology disclosure document, which limits their performance and practical application. In this paper, we propose a similarity evaluation method of patent application technology disclosure document based on multi-dimensional fusion strategy to realize the similarity measurement of patents. Firstly, in the text preprocessing section, word segmentation reconstruction and similarity evaluation optimization strategies based on word frequency and part-of-speech score weighted fusion are proposed. Then, a similarity calculation method of patent application technology disclosure document based on two new mapping spaces of dot matrix and image is proposed to achieve a more diversified comprehensive evaluation. The algorithm was evaluated by using four published text similarity matching datasets (containing 0-5 or 0/1 labels) and a set of patent application technology disclosure documents. Experimental results show that on the published text similarity matching datasets, the similarity evaluation method under the multi-dimensional fusion strategy proposed in this paper has a discrimination accuracy improvement of about 10% compared to traditional vector semantic model, and can match the discriminative ability of lightweight deep learning models without the need for training. At the same time, the discrimination accuracy of the proposed method on the sample dataset of patent application technology disclosure document is superior to traditional vector semantic model (20%) and various deep learning models (1%-8%), and the precision and recall rate are relatively balanced. The visual analysis results on the dataset of the patent application technology disclosure documents also prove the effectiveness and reliability of the similarity calculation method proposed in the dot matrix and image space, which provide a new idea and method for the similarity evaluation between patent application technology disclosure document.

Topological Superconductors from a Materials Perspective.

Topological superconductors (TSCs) have garnered significant research and industry attention in the past two decades. By hosting Majorana bound states which can be used as qubits that are robust against local perturbations, TSCs offer a promising platform toward (nonuniversal) topological quantum computation. However, there has been a scarcity of TSC candidates, and the experimental signatures that identify a TSC are often elusive. In this Perspective, after a short review of the TSC basics and theories, we provide an overview of the TSC materials candidates, including natural compounds and synthetic material systems. We further introduce various experimental techniques to probe TSCs, focusing on how a system is identified as a TSC candidate and why a conclusive answer is often challenging to draw. We conclude by calling for new experimental signatures and stronger computational support to accelerate the search for new TSC candidates.

Topology stabilized fluctuations in a magnetic nodal semimetal.

The interplay between magnetism and electronic band topology enriches topological phases and has promising applications. However, the role of topology in magnetic fluctuations has been elusive. Here, we report evidence for topology stabilized magnetism above the magnetic transition temperature in magnetic Weyl semimetal candidate CeAlGe. Electrical transport, thermal transport, resonant elastic X-ray scattering, and dilatometry consistently indicate the presence of locally correlated magnetism within a narrow temperature window well above the thermodynamic magnetic transition temperature. The wavevector of this short-range order is consistent with the nesting condition of topological Weyl nodes, suggesting that it arises from the interaction between magnetic fluctuations and the emergent Weyl fermions. Effective field theory shows that this topology stabilized order is wavevector dependent and can be stabilized when the interband Weyl fermion scattering is dominant. Our work highlights the role of electronic band topology in stabilizing magnetic order even in the classically disordered regime.

Spherulitic Crystallization of CsCu2I3 for High Performance Ultraviolet Photodetectors.

Ternary copper halides have become promising materials for UV photodetection due to their stability and eco-friendliness. However, the uncontrollable crystallization induces high-concentration defects in these films, inherently limiting further improvement in device performance. Herein, we reveal the antisolvent-assisted crystallization kinetics mechanism of CsCu2I3 during the film-forming process. The nucleation rate is manipulated by adjusting precursor supersaturation using different antisolvents, resulting in decreased density and preferential orientation of the nuclei within the wet film. Subsequent annealing leads to a homogeneous and low-defect CsCu2I3 film with 40-µm-scale spherulites. A resulting visible-blind ultraviolet photodetector exhibits a responsivity of 8.73 A W-1, a specific detectivity of 5.28 × 1012 jones, and a response speed of 1.12 ms. The unencapsulated photodetector shows negligible degradation of responsivity in ambient air (∼70% humidity) for one month. Moreover, the flexible device with a responsivity of 420.2 mA W-1 and a detectivity of 1.18 × 1012 jones also shows excellent bending stability.

Witnessing light-driven entanglement using time-resolved resonant inelastic X-ray scattering.

Characterizing and controlling entanglement in quantum materials is crucial for the development of next-generation quantum technologies. However, defining a quantifiable figure of merit for entanglement in macroscopic solids is theoretically and experimentally challenging. At equilibrium the presence of entanglement can be diagnosed by extracting entanglement witnesses from spectroscopic observables and a nonequilibrium extension of this method could lead to the discovery of novel dynamical phenomena. Here, we propose a systematic approach to quantify the time-dependent quantum Fisher information and entanglement depth of transient states of quantum materials with time-resolved resonant inelastic x-ray scattering. Using a quarter-filled extended Hubbard model as an example, we benchmark the efficiency of this approach and predict a light-enhanced many-body entanglement due to the proximity to a phase boundary. Our work sets the stage for experimentally witnessing and controlling entanglement in light-driven quantum materials via ultrafast spectroscopic measurements.