Search Results (45,162)

This research investigates the formation of an amorphous phase in a non-equiatomic aluminum-based alloy, Al₈₂Fe₁₂Cu₂Nb₂Co₂, synthesized via mechanical alloying. By utilizing minor additions of Nb, Co, and Cu, structural stability and “chemical complexity” effects are achieved in a matrix dominated by a single element (82% Al). Thermodynamic analysis reveals that a moderately negative mixing enthalpy (ΔHₘᵢₓ = −6.89 kJ/mol) and elevated configurational entropy (ΔSₘᵢₓ = 5.420 J/mol·K) are the primary thermodynamic drivers of amorphization, supplemented by a transitional-regime atomic size mismatch (δ = 4.82%). The evolution of the structure, morphology, and magnetic properties of mechanically alloyed amorphous Al₈₂Fe₁₂Cu₂Nb₂Co₂ as a function of milling time was systematically investigated using X-ray diffraction, scanning electron microscopy, Fourier-transform infrared spectroscopy, and a vibrating sample magnetometer. Full article

While hydroxypropyl methylcellulose (HPMC) is a promising plant-based alternative to gelatin, its industrial application is limited by poor mechanical properties and high production costs. In this study, high-performance HPMC-based hard capsules were developed using an HPMC/gellan gum/ι-carrageenan ternary system. The formulation and preparation process were optimized via single-factor experiments, response surface methodology, and low-field nuclear magnetic resonance analysis. Scanning electron microscopy was applied to characterize the microstructural evolution during disintegration. The optimized capsules exhibited rapid disintegration within 15 min across four pH media and satisfied the requirements of the Chinese Pharmacopoeia (2025). Drug dissolution profiles using cefradine and ranitidine hydrochloride showed over 85% cumulative release within 30 min, with similarity factors higher than 50 relative to commercial gelatin capsules under the tested conditions. This work provides a feasible and low-cost strategy for the industrial production of plant-based capsules and promotes the high-value utilization of polysaccharide-based capsule materials. Full article

PLLA-b-PEG-b-PLLA triblock copolymers are promising materials because of their highly tunable properties. However, a systematic understanding of composition–property relationships remains limited. In this study, a series of A-B-A triblock copolymers was synthesized with polyethylene glycol (PEG) as soft center (B) domains and poly(L-lactic acid) (PLLA) as hard end (A) domains via ring-opening polymerization. Copolymer composition and molecular weights were characterized by proton nuclear magnetic resonance spectroscopy (¹H NMR) and gel permeation chromatography (GPC). The thermal and mechanical properties of the copolymers were evaluated by differential scanning calorimetry (DSC) and tensile testing. We established quantitative structure–property relationships using empirical data, demonstrating that PLLA block length played a key role in modulating tensile properties, with a near-linear relationship, while PEG molecular weight critically influenced mechanical stability. An approximate minimum PLLA block length of 20 repeat units was found as a threshold required to maintain structural integrity during in vitro 24 h swelling. These findings provide insights and practical guidance for the design of triblock copolymers with tunable thermal, mechanical, and swelling properties of PLLA-b-PEG-b-PLLA triblock copolymers. Full article

The increasing amount of untreated electronic waste, particularly in the telecommunications sector, is having a negative impact on the environment, not only by increasing the production of greenhouse gases, but also by reducing the availability of resources such as metals. At the same time, these metals are increasingly in demand to meet the manufacturing needs of new technologies. One solution is to recover metals by recycling end-of-life electronic boards. However, current processes are often implemented by large companies but are not suitable for small organizations or those with fewer resources, thus limiting their participation in local electronic waste management. Based on laboratory-scale analyses, this project compares the metal concentration results of three pre-treatments that could be suitable for smaller organizations: magnetic separation, chemical pre-treatment with sodium hydroxide, and centrifugation. The proposed preparation step, after the shredding of telecom electronic boards down to a particle diameter of less than 1 mm, is two-stage centrifugation. This pre-treatment enables metals to be concentrated efficiently and safely prior to hydrometallurgical processing. Full article

Magnetic resonance imaging (MRI) remains one of the most important tests for diagnosing and monitoring various diseases. In recent years, machine learning methods have been widely applied to automate MRI analysis. It supports decision-making by predicting disease and highlighting relevant regions. However, the proper use of feature extraction methods can improve the performance of the model. This paper proposes a WaveletFusion architecture that combines a two-dimensional Haar wavelet decomposition with a convolutional neural network (CNN) for classification. The approach was demonstrated on the Brain Tumor MRI dataset and further examined on the Br35H :: Brain Tumor Detection 2020 (Br35H). The model decomposes each MRI slice into approximation and directional detail subbands and fuses multi-scale wavelet features within the convolutional pipeline. To evaluate the effect of decomposition depth, WaveletFusion variants from one to eight levels were compared with a Baseline CNN model under the same training protocol. The results showed that performance improved progressively with increasing decomposition depth up to level 7, whereas the 8-level configuration consistently declined, indicating that excessive decomposition introduces information loss and over-compression in the deepest approximation pathway. The best-performing configuration, which outperformed both the Baseline CNN and the WaveletFusion variations in five independent runs, was the 7-level WaveletFusion model, achieving a test accuracy of 0.94 ± 0.01 and test macro-F1 of 0.93 ± 0.02. A similar tendency was observed on the Br35H dataset, where the 7-level model achieved a 0.97 ± 0.01 test accuracy and 0.97 ± 0.01 test macro-F1, while the 8-level configuration remained weaker on both datasets. These results show that multi-scale wavelet fusion can improve Brain Tumor MRI classification while maintaining a compact model size and a fair comparison setting, and that the decomposition depth must be selected carefully. Full article

High-entropy oxide (HEO) thin films hold significant potential for applications in spintronics and catalysis; however, their widespread utilization is hindered by weak room-temperature ferromagnetism (RTFM). Herein, we demonstrate a facile vacuum annealing strategy to enhance the RTFM of HEO thin films. (FeNiAlCrMn)O films exhibit a saturation magnetization (M_S) of 5.9 emu/cm³ and a Curie temperature (T_C) of 350 K after vacuum annealing at 1173 K. Mechanistic investigations reveal that the enhanced RTFM originates from an annealing-induced phase transition from rocksalt-to-spinel. Structurally, annealing facilitates cation diffusion from octahedral to tetrahedral sites, forming a highly crystalline, long-range magnetic lattice of spinel ferrite. Electronically, tetrahedral occupation shortens M–O bonds, drives electron transfer toward metal cations, and enhances orbital hybridization, thereby strengthening magnetic exchange coupling. This study provides a simple and effective strategy for tailoring the RTFM of HEO thin films. Full article

An encephalocele is a rare congenital or acquired cranial defect characterized by herniation of intracranial tissue through a defect in the skull base. In human and veterinary medicine, these lesions are frequently associated with abnormalities in neural tube development or structural weakness of the cranial bones, resulting in the protrusion of brain tissue and meninges through anatomical openings such as the cribriform plate. Although this condition has been extensively described in human neurosurgical research, reports on dogs remain limited, and the clinical significance of surgical intervention in cases with communication to the nasal cavity remains unclear. In this case, a young American Cocker Spaniel presented with seizures, prompting advanced diagnostic evaluation. Magnetic resonance imaging revealed a protrusion of the intracranial tissue through a defect in the cribriform plate extending into the nasal cavity. Surgical resection of the protruding tissue was performed, followed by skull base reconstruction. Histopathological examination demonstrated nervous tissue with chronic inflammatory changes without evidence of neoplasia. The patient recovered uneventfully after surgery and remained free of seizure recurrence during follow-up. Surgical management may represent a viable treatment option for seizure disorders in young dogs, particularly when persistent cranio-nasal communication is present, and provides a clinically relevant comparative model for similar cranial base defects described in human pathology. Full article

Multi-modal Magnetic Resonance Imaging (MRI) provides complementary information for clinical diagnosis, yet its large-scale storage, privacy sensitivity, and annotation cost pose significant challenges. Inspired by biological vision systems, which integrate multi-sensory inputs and compress experiences into compact memory representations, we propose a bio-inspired framework termed Contrast-Guided Multi-modal Dataset Distillation (CGMDD). In biological perception, different sensory channels observe the same environment from complementary perspectives, while hierarchical neural processing ensures perceptual consistency across modalities. Meanwhile, memory systems such as the associated medial temporal lobe structures consolidate redundant experiences into efficient representations for long-term storage. Motivated by these principles, CGMDD treats multi-modal MRI as multi-view perceptual signals and introduces a hierarchical cross-modal contrastive learning mechanism that enforces perceptual alignment across modalities, analogous to multi-level processing in the visual cortex. Furthermore, we design a dynamic dataset distillation strategy that mimics memory consolidation by compressing large-scale data into compact, informative synthetic representations through gradient-based optimization. The proposed framework jointly optimizes perceptual alignment and memory compression in an end-to-end manner, achieving a biologically plausible integration of perception and learning. Experimental results on two MRI datasets demonstrate that CGMDD can compress the original dataset to 5% of its size while maintaining competitive performance, even with only 30% of the labels. These findings highlight the effectiveness of bio-inspired mechanisms in building efficient, robust, and privacy-preserving computer vision systems. Full article

In complex marine environments, underwater magnetic anomaly detection is challenging because target magnetic anomaly signals are typically weak and easily overwhelmed by background magnetic noise. Although deep learning-based methods have significantly improved detection capability, most existing approaches still rely on abundant labeled target data, which is difficult to obtain in practical applications. To address this challenge, this paper proposes an unsupervised underwater magnetic anomaly detection method based on Gaussian granular ball one-class classification (GBOC). A density-guided hierarchical partitioning strategy is introduced to divide the latent space into multiple compact high-density regions and construct corresponding Gaussian granular ball representations. This strategy enables more effective modeling of complex background magnetic noise and improves anomaly detection under low signal-to-noise ratio (SNR) conditions. Experimental results show that the proposed method achieves robust performance across different SNR levels in the unsupervised setting. Compared with other methods, it yields a higher detection rate and more stable results under a fixed false alarm rate. Furthermore, a semi-supervised magnetic anomaly detection method is developed by introducing a small amount of prior information on magnetic anomalies. Experimental results demonstrate that the proposed semi-supervised method can further improve detection accuracy while maintaining good robustness and stability. Full article

The corrosion behaviour and environmental durability of injection- and compression-moulded Nd–Fe–B and Sm–Fe–N magnets were investigated. For injection-moulded magnets, the effects of magnetic powder type (Nd–Fe–B and Sm–Fe–N), magnetic powder particle size (100 µm and 400 µm), and polymer binder (PPS and PA12) on corrosion resistance were studied. For compression-moulded magnets with an epoxy binder, the effects of powder type and size were examined. Corrosion resistance was investigated using potentiodynamic polarisation in electrolytes of varying pH (1.8–12.8). The Sm–Fe–N magnets exhibited slightly better corrosion resistance than the Nd–Fe–B magnets, irrespective of the polymer binder. The finer magnetic powders (100 µm) showed lower corrosion resistance due to their larger specific surface area, with a more pronounced effect in the compression-moulded magnets. The type of polymer binder had only a minor effect. The hygrothermal corrosion resistance and thermal stability were evaluated using bulk corrosion (BCT) and thermal shock tests, respectively. Surface corrosion was observed in all magnets after the BCT, with the compression-moulded magnets exhibiting a greater irreversible loss of magnetic properties. The thermal shock test caused a temporary reduction in magnetic properties, with recovery after remagnetisation, demonstrating the good thermal stability of both magnet types. Full article

The synthesis of three novel, valuable bioconjugates obtained by coupling cholesterol bromoacetate with pyridine derivatives via an SN2 reaction was successfully carried out. Each of the products was fully characterized by magnetic nuclear resonance (¹H, ¹³C, APT, ¹H−¹H COSY, ¹H–¹³C HMBC, ¹H–¹³C HSQC), infrared spectroscopy (IR), and high-resolution mass spectrometry (HRMS). Full article

Cardiac magnetic resonance imaging (CMR) is widely used for the diagnosis and functional assessment of cardiovascular diseases because of its noninvasive nature and excellent soft-tissue contrast. However, accelerated cine magnetic resonance imaging (cine MRI) acquisition usually relies on undersampling, which may lead to noise, aliasing artifacts, and detail loss in reconstructed images. To address this issue, we propose a wavelet-guided dynamic Transformer with diffusion priors for cardiac cine MRI reconstruction. Specifically, a diffusion model is introduced into a reduced latent feature space to generate high-frequency prior features with only 8 reverse sampling steps, thereby enhancing detail recovery while maintaining moderate computational cost. In addition, a wavelet-guided dynamic Transformer is designed to capture low-frequency structural information and temporal dependencies across adjacent frames. By combining wavelet-domain decomposition, diffusion priors, and dynamic spatiotemporal modeling, the proposed framework improves reconstruction quality while preserving temporal consistency. Experimental results on multiple cardiac cine MRI datasets show that the proposed method achieves superior reconstruction accuracy and temporal consistency over several competing approaches, while maintaining a favorable balance between computational efficiency and reconstruction performance. These findings indicate that the proposed framework is an effective and robust solution for accelerated cardiac cine MRI reconstruction. Full article

The MnP family of binary compounds presents an intriguingly simple platform to mix-and-match elemental components. Replacement on the transition metal or pnictogen site can alter magnetism, electronic correlations, and electrical properties. Here we report low-temperature properties of CoP, including measurements at magnetic fields exceeding 30 T, revealing de Haas–van Alphen oscillations and a nearly two orders of magnitude increase in resistance. When viewed together with prior work, it is possible to put together a global picture of the role of different atoms in variations in magnetic ordering, lattice coherence, and topological band structure features in this material family. Full article

The static magnetic field from large seafloor exploration platforms severely interferes with weak geological signals. Accurately predicting and compensating for this interference is critical for deep-sea surveys. However, traditional inversion methods using limited spatial measurements have severely ill-posed coefficient matrices, amplifying near-field noise and causing massive divergence during far-field extrapolation. To address this, we propose a reliable and data-efficient magnetic field prediction method utilizing prior-constrained boundary integrals. First, a virtual plane is constructed between the platform and the measurement plane. A differential recursive algorithm extracts the local magnetic field on this plane from limited measurements to serve as physical prior information. Incorporating this knowledge to structurally constrain the boundary integral inversion fundamentally mitigates the ill-posed problem. Simulations and scaled physical experiments demonstrate that this method prevents near-field noise overfitting, achieving enhanced far-field reliability. By maximizing the utility of limited spatial data, the maximum relative error on the far-field prediction plane is reduced from 10.5% to 8.3% in simulations, and from 13.2% to 9.8% in physical experiments. This provides a highly reliable approach for marine magnetic interference compensation. Full article

Oxygen stoichiometry critically governs the phase stability and physical properties of transition-metal oxides, yet a unified understanding of how oxygen-stoichiometry-driven phase reconstruction underlies the cooperative evolution of multiple physical properties in SrCoO_x remains lacking. Here, high-quality epitaxial brown millerite SrCoO_2.5 and perovskite SrCoO_3−δ thin films were grown by pulsed laser deposition under controlled oxygen conditions. Their structural, magnetic, electrical, optical, and photocatalytic properties were systematically compared. SrCoO_2.5 exhibits antiferromagnetic insulating behavior, infrared-dominant transmittance, and higher photocatalytic activity, whereas SrCoO_3−δ shows ferromagnetism, much lower resistivity, and strong optical opacity. First-principles calculations reveal that oxygen-stoichiometry-driven phase reconstruction strongly modifies the electronic structure, accounting for the distinct magnetic, transport, and optical responses. These results establish a direct correlation between oxygen stoichiometry, structural transformation, and multifunctional properties in SrCoO_x, highlighting oxygen-vacancy ordering as an effective route to tailoring correlated oxide functionalities. Full article