Search Results (462)

Perovskite manganites (AMnO₃) and perovskite-like manganites (A’_1−xA_xMnO₃) are complex oxide materials that have attracted significant attention from the scientific community in recent years due to their structural flexibility, mixed-valence state, tunable electronic configuration, and multifunctional properties. This review systematically analyzes the synthesis methods, structural classification, and physicochemical characterization of perovskite manganites, as well as their magnetic, optical, electrical, dielectric, and catalytic properties. The influence of solid-state reactions, sol–gel, Pechini, hydrothermal, co-precipitation, microwave, and other mild chemical approaches on phase purity, morphology, particle size, and oxygen stoichiometry was examined. The structural diversity of perovskite and perovskite-like manganites, including simple ABO₃, double perovskites, multilayer, and low-dimensional systems, was characterized in relation to their functional properties. The review discussed the capabilities of methods for synthesizing and analyzing morphological properties, demonstrating the role of doping, cation substitution, oxygen vacancies, and Jahn–Teller distortions in controlling material properties. Prospects for the application of perovskite manganites in spintronics, magnetocaloric cooling, photocatalysis, gas-sensing devices, and energy conversion and storage systems were analyzed. This review highlights the structure–property–application relationship in perovskite manganites. Full article

Deep tight sandstone reservoirs are characterized by strong microscopic pore structure heterogeneity and commonly exhibit a high-porosity, low-permeability profile, posing significant challenges for effective reservoir evaluation and “sweet spot” prediction. The microscopic pore structure of 209 tight sandstone samples from the deeply buried Huagang Formation in the Xihu Depression, East China Sea Basin, was systematically characterized by integrating multiple analytical techniques, including casting thin sections, scanning electron microscopy (SEM), X-ray diffraction (XRD), nuclear magnetic resonance (NMR), and high-pressure mercury injection (HPMI). The results indicate that the reservoir space is dominated by mesopores (55.48%) and transition pores (32.39%), with macropores (2.09%) and micropores (10.04%) being relatively underdeveloped. A significant vertical heterogeneity in reservoir quality is observed. The H4 member exhibits superior properties, characterized by a higher average movable fluid saturation (averaging 46%) and better pore connectivity. In contrast, the H5 member is more compact, with a notably higher proportion of bound fluid (averaging 47%). The differences in reservoir quality are controlled by a sedimentary–diagenetic coupling mechanism. High-energy, coarse-grained facies underwent a constructive pathway involving chlorite coating protection and dissolution enhancement, forming high-quality pore networks. In contrast, low-energy, fine-grained facies experienced a destructive pathway dominated by intense compaction and cementation, leading to the deterioration of pore structure. The petrophysical properties of the deep reservoirs are primarily governed by the three-dimensional connectivity and spatial distribution of effective “pore-throat assemblages” composed of dominant throats. Accordingly, a “binary” pore structure development pattern is established for the deep tight sandstone reservoirs in the study area. This pattern posits that the reservoir space is heterogeneously composed of a minority of connected “effective percolation assemblages” and a majority of isolated “ineffective assemblages”. Full article

Electric power steering (EPS) motors require low torque ripple, low cogging torque, and smooth torque output to ensure precise control and driving comfort. However, consequent pole permanent magnet synchronous motors (CP-PMSMs), although advantageous in reducing permanent magnet usage, exhibit an imbalanced magnetic flux distribution due to the iron poles, resulting in even-order harmonic components in the back electromotive force (BEMF) and significant torque ripple. In this paper, a rotor pole arrangement for CP-PMSMs is proposed to improve torque characteristics for EPS applications. Symmetric and asymmetric pole arrangements are introduced to modify the magnetic flux distribution and suppress harmonic components generated by the iron poles. In addition, the iron pole arc ratio is selected as a key design variable and analyzed for each model to achieve low torque ripple while maintaining torque performance. The electromagnetic characteristics of the proposed structures are evaluated using finite element analysis under identical operating conditions. The results show that the torque ripple of the proposed models is reduced by approximately 33.3%p and 34.1%p compared with the conventional CP-PMSM, and the cogging torque is also significantly reduced. Although average torque decreases, overall torque characteristics improve due to reduced torque ripple and harmonic components. These results demonstrate that the proposed rotor pole arrangement effectively enhances torque quality in CP-PMSMs without increasing axial length or requiring three-dimensional analysis. Full article

Aiming at the problem that the loss and temperature rise of the pole-suspended rotor low-speed high-torque permanent magnet synchronous motor (LHPMSM) increase in the pursuit of high torque density, and the design cycle is prolonged due to the dependence on thermal post-verification. In this paper, a multi-physics trade-off design method based on weighted heating rate combined with a surrogate model and a multi-objective evolutionary algorithm is proposed. Firstly, the rationality of introducing a weighted heating rate is proved by mathematical proof and thermal network calculation. Secondly, the two-dimensional sensitivity analysis of the key structural parameters of the motor is carried out to identify the most influential structural variables, which are then used to construct a high-precision surrogate model based on gradient boosting regression tree (GBRT). Then, in order to effectively obtain the Pareto solution set of balanced torque performance and heat dissipation performance, the non-dominated sorting genetic algorithm (NSGA-II) is used for multi-objective optimization. Finally, the multi-physical field finite-element simulation verification and a 356kW prototype experimental analysis show that the optimized design significantly improves the torque performance while effectively controlling the temperature rise and realizes the fast compromise design of the multi-physical field of the motor. The effectiveness and advancement of the proposed method to achieve coordinated improvement of high power density and high steady-state thermal margin in motor design are verified. Full article

The core volume ratio (CVR) is a key indicator for evaluating the proportion of edible fraction in stone fruits. Traditionally, CVR is determined through destructive sampling by separately measuring the masses of the core and entire fruit. Recently, low-field nuclear magnetic resonance imaging (LF-NMRI) has been introduced as a non-destructive alternative, but its sparse sampling limits the ability to achieve accurate spatial and volumetric quantification of fruit quality. To address this limitation, we propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits. The method acquires tomographic LF-NMRI sequences along three orthogonal axes. Each sequence is segmented into pulp and core regions using a SwinUNet deep learning model and converted into point clouds for each view. Point clouds from the three orthogonal views are registered via a genetic algorithm to align structural information from complementary perspectives and fused into a unified 3D model through Poisson surface reconstruction. Using prunes as a representative case, the method enables accurate quantification of core and entire fruit volumes, achieving a CVR estimation with a mean absolute error of 0.13% compared to manual measurements. The proposed three-view reconstruction strategy yields a volumetric error of only 0.73%, significantly outperforming single-view (4.57%) and dual-view (3.73%) approaches. This technology provides a robust and accurate non-destructive solution for 3D internal quality analysis of fruits. Full article

Polymer-based composites with magnetic properties are promising materials that are able to combine the usual polymer features (low density, high electrical resistance, enhanced flexibility, and processability, etc.) with magnetic properties typically associated with ferro- or ferrimagnetic metals, alloys or metal oxide. The combination of recycled NdFeB powders with additive manufacturing techniques based on material extrusion enables the production of magnetic composites. The novelty of this approach lies in the use of 3D printing supported by an external magnetic field, which is used to align the particles during the printing process and thus improve the final magnetic properties. This approach represents a sustainable strategy for the recovery of electronic waste, converting it into high-value-added magnetic materials intended for additive manufacturing applications. Micrometric particles made of a Neodymium–Iron–Boron (NdFeB) alloy are compounded with a flexible thermoplastic matrix made of polybutylene adipate-co-terephthalate (PBAT). The NdFeB alloy is recovered from permanent magnets of obsolete hard drives and is demagnetized, ground to powder under an inert atmosphere, and finally sieved to a particle size below 50 µm. The obtained powder is mixed with the polymer using a twin-screw extruder. The composite material containing the NdFeB particles is then processed to obtain a calibrated filament, used for the fused deposition modeling (FDM) three-dimensional (3D) printing of magnetic composites. To improve the composite’s ferromagnetic behavior, the particles were aligned along the stacking direction of the layers during the 3D FDM process by printing directly onto a permanent magnet placed on the build plate. Composites containing up to 50% by weight of recycled NdFeB powder were successfully processed using FDM technology, exhibiting increased stiffness, with the storage modulus rising from 123 to 178 MPa at 20 °C, while magnetic field-assisted printing increased the remanence from 11 to 28 emu/g and improved the reduced remanence from 0.21 to 0.49, corresponding to an estimated fourfold improvement in the magnetic energy product. Full article

Background/Objectives: Accurate automatic segmentation of multimodal magnetic resonance imaging (MRI) is essential for neurosurgical planning and image-guided procedures. However, existing three-dimensional segmentation models often struggle with low lesion-to-tissue contrast, ambiguous tumor boundaries, small enhancing tumor regions, and performance degradation caused by missing imaging modalities. This study aimed to develop a robust segmentation framework that improves cross-modal representation learning, boundary recovery, and segmentation performance under incomplete-input conditions. Methods: We propose PF-CMNet, a Progressive Frequency-Aware Cross-Modal Network with Missing-Modality Distillation for three-dimensional brain tumor segmentation. The network introduces a Cross-Modal Selective Frequency Attention module in the early encoder stage to model modality-specific frequency responses and spatially adaptive cross-modal correlations. A Progressive Cross-Scale Detail Fusion decoder is further employed to aggregate multilevel semantic features and refine high-resolution boundary details. To enhance robustness under missing-modality conditions, a teacher–student distillation strategy transfers full-modality predictions and shallow feature knowledge to a student network trained with random modality dropout. Results: On the MSD Task01_BrainTumour dataset, PF-CMNet achieved an average Dice score of 84.3%, with Dice scores of 79.6%, 82.8%, and 90.4% for enhancing tumor, tumor core, and whole tumor, respectively. On the BraTS2021 dataset, the model achieved an average Dice score of 88.2% and the lowest average 95th percentile Hausdorff distance among the compared methods. In predefined complete-modality absence stress tests, where unavailable MRI sequences were zero-masked to model the absence of input modalities rather than partial image degradation, the distilled model maintained average Dice scores of 78.64%, 82.58%, 58.39%, 82.03%, and 79.29% when FLAIR, T1, T1ce, T2, and T1 + T2 were unavailable, respectively. Conclusions: PF-CMNet provides a unified framework for multimodal brain tumor segmentation, improving full-modality segmentation accuracy, boundary consistency, and robustness to incomplete MRI inputs while maintaining a favorable accuracy–efficiency trade-off. Full article

Admittance-based analysis of wind farm-integrated power systems is inaccurate across varying operating points (OPs) resulting from wind speed fluctuations and shifting grid conditions. Existing methods can be classified as model-driven, which require detailed system modeling and struggle with parameter extraction, and as data-driven, which often lack physical interpretability, suffer from high dimensionality, and provide insufficient coverage of training frequency points. This study introduces an AM reconstruction framework that integrates equivalent circuits with a support vector machine (SVM). The approach first applies vector fitting and an equivalent-circuit transformation to decompose the admittance response into first- and second-order subcircuits, thereby representing the frequency-domain characteristics with low-dimensional, more physically interpretable parameters. Subsequently, an SVM establishes a nonlinear mapping between OPs and equivalent-circuit parameters, enabling the reconstruction of continuous admittance transfer functions for new OPs. This framework transforms the modeling of high-dimensional frequency-domain data into a low-dimensional physical parameter prediction problem, thereby avoiding error accumulation from interpolation over discrete frequency points. The proposed method is validated using a direct-drive permanent magnet synchronous generator (PMSG) wind turbine model connected to the IEEE 14-bus test system. Frequency-domain simulations and error analyses under previously unseen OPs confirm the method’s high prediction accuracy and strong generalization capability. Full article

We study the planar dynamics of a charged particle subjected to a radially repulsive inverted harmonic potential and a perpendicular uniform magnetic field, a configuration that is relevant to nanoscale-charged transport and confinement in low-dimensional systems. The competition between the destabilizing central repulsion and magnetic field-induced rotational motion gives rise to rich trajectory behavior, including spiraling, unbounded escape, and parameter-dependent quasi-confined motion. The governing coupled differential equations of motion are solved analytically. The resulting trajectories are classified as functions of system parameters. The proposed framework provides insight into charge carrier dynamics in nanostructured environments such as quantum wells, 2D materials, and plasma-like nanosystems, where effective repulsive potentials may arise from external gating or collective interactions. In addition, the model offers a classical analogue for interpreting features associated with magnetic confinement in non-equilibrium or unstable regimes. These results contribute to the theoretical foundation for designing and controlling charged particle motion in emerging nanomaterials and devices. Full article

Grain-oriented (GO) silicon steel cores in low-voltage current transformers suffer magnetic degradation from residual stress and increased dislocation density during slitting and winding. This study addresses the gap in systematic optimization of secondary annealing on assembled toroidal cores using a 3² full-factorial design varying temperature (650, 850, 1050 °C) and holding time (60, 90, 120 min) on M4 grade cores. Results showed temperature is the dominant factor, while holding time exhibits a synergistic non-linear effect. The optimal condition (850 °C, 90 min) reduced specific losses from 0.85 W/kg to 0.43 W/kg (49% reduction). Mechanistic analysis confirmed this improvement is driven by complete primary recrystallization (equiaxed grains ~50–60 µm), dislocation annihilation (~10 HV hardness reduction), and reinforcement of the Goss texture ({110} <001>). SEM, EDS, and ICP-OES demonstrated that the Carlite coating remained dimensionally (1.67–1.83 µm) and chemically stable, with beneficial decarburization. Temperatures above 850 °C caused magnetic deterioration due to excessive grain growth. These results provide a validated, industrial framework for recovering magnetic efficiency in wound toroidal cores without compromising coating integrity. Full article

The rapid evolution of next-generation electronics urgently demands high-performance functional materials. Two-dimensional (2D) semiconductors, characterized by tunable bandgaps, magnetic properties, and excellent optical and electronic properties, hold significant potential for applications in nanoelectronic devices, magnetic storage, and optoelectronics. However, the high computational cost of traditional Density Functional Theory (DFT) severely restricts large-scale high-throughput screening. Meanwhile, problems such as insufficient datasets and non-uniform data quality remain prevalent. Against this background, machine learning (ML), which captures intricate nonlinear correlations and accelerates the discovery of novel materials, has emerged as an efficient technical approach. This review systematically summarizes recent advances in ML-driven property prediction for 2D semiconductors. It first elaborates the fundamental properties and classifications of 2D semiconductors, and then compares traditional computational simulations with ML algorithms, clarifying the distinct advantages of data-driven approaches. Subsequently, this work focuses on the latest progress in predicting critical properties, including bandgap, magnetism, and other physical characteristics. For bandgap prediction, classical algorithms such as random forests are compared with deep learning models represented by graph neural networks. The results demonstrate that deep learning performs much better in low-data regimes and complex material systems. For magnetic property prediction, the impact of feature engineering strategies on model accuracy and efficiency is systematically analyzed. In addition, the research progress of other physical property prediction tasks is briefly summarized. Finally, future research directions for machine learning, including standardized materials databases, physics-informed machine learning, multimodal modeling, and the integration of machine learning with experimental and theoretical methods, are outlined to address challenges in data quality, model interpretability, and cross-system generalization ability. This work aims to provide a systematic theoretical foundation and methodological guidance for research on two-dimensional semiconductor materials assisted by machine learning. Full article

Background/Objectives: Early and accurate prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is vital for personalized breast cancer treatment. However, existing deep learning methods are hampered by tumor heterogeneity and semantic misalignment between high-dimensional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and low-dimensional clinical data, which limits pCR prediction performance and generalization. This study addresses these challenges via a novel multimodal network. Methods: We propose a Bidirectional Perceptual Multimodal Interaction Network (BPMINet) based on contrastive learning. BPMINet integrates pre-NAC DCE-MRI and clinical information through three core components: (1) we propose a bidirectional cross-modal attention (BiCMA) fusion mechanism to resolve semantic misalignment and facilitate effective multimodal feature fusion; (2) we design a multimodal contrast-aware feature enhancement (MCFE) module as a key component tightly integrated into the pCR-oriented contrastive learning framework, which serves to boost discriminative power for pCR prediction and improve generalization performance on hard-to-classify samples; (3) we adopt a dual-loss strategy to enable the collaborative optimization of discriminative feature representation and pCR prediction performance. Results: On two publicly available multicenter datasets, BPMINet outperformed all comparative methods across seven evaluation metrics: specifically, it surpassed the top-performing baseline by 5.17% in AUC and 5.24% in accuracy on the MAMA-MIA dataset. More notably, it achieved substantially larger gains of 11.72% in AUC and 7.38% in accuracy on the ISPY1 dataset. Conclusions: BPMINet achieves optimal pCR prediction performance, confirming its superiority and strong generalization ability for multimodal breast cancer pCR prediction. Full article

In this study, the two-dimensional aligned steel fiber-reinforced micro-expansive concrete (2D) was prepared, aiming to address the inherent vulnerabilities of concrete, such as early-age shrinkage cracking and low tensile ductility. For this purpose, the steel fibers and expansive agent were utilized. Furthermore, the planar rotating magnetic field was used to randomly distribute the steel fibers in a two-dimensional plane. In order to verify its superior mechanical and shrinkage properties, the compressive, fracture and drying shrinkage tests were carried out. The results demonstrate that the 2D alignment method enhances the fiber utilization efficiency. Compared with fiber-free groups, the compressive strength and fracture parameters of specimens incorporating steel fibers were improved. Furthermore, compared with randomly distributed steel fiber-reinforced micro-expansive concrete (RD), the 2D alignment method made the cubic compressive strength and fracture energy improve 8–14.2% and 19.4–110%, respectively. Additionally, the advantage of the fiber 2D alignment method was also reflected in the inhibition of drying shrinkage. Compared with normal concrete, the 180-day shrinkage strain of the 2D1.2 group was reduced to 200 με (only 19.5% of that of normal concrete, or 30.6% of that of micro-expansive concrete). Mechanistically, these superior performances are fundamentally governed by a coupling effect: chemical shrinkage compensation and physical alignment constraint. Full article

Ultrathin ferrimagnetic heterostructures have emerged as promising platforms for next-generation spintronic devices, yet the role of two-dimensional substrates in modulating their magnetic properties remains underexplored. Here, we report a comprehensive study of the thickness- and temperature-dependent magnetic behavior of amorphous Fe₇₃Co₈Gd₁₉ films (4–32 nm) deposited on Si, WSe₂ bilayer, and WSe₂ monolayer substrates. Structural integrity and stoichiometry were confirmed via X-Ray Diffraction (XRD), X-Ray Reflectivity (XRR), Raman spectroscopy, and Energy-Dispersive Spectroscopy (EDS) analysis. In-plane magnetometry from 10–300 K reveals that monolayer WSe₂ promotes stronger interfacial spin alignment, with the 4 nm film exhibiting a sharp increase in coercivity below 50 K, where $H_c$ exceeds 23 mT and even surpasses thicker counterparts, alongside enhanced saturation magnetization (∼790 kA/m at 100 K). This dramatic enhancement of coercivity is the most significant result of this work, underscoring the dominant role of interfacial coupling in governing low-temperature magnetic hardness. Conversely, films on bilayer exhibit suppressed magnetization and soft magnetic behavior ($H_c$ < 10 mT) across all temperatures, making them attractive for ultralow-power and high-speed spintronic applications. These findings demonstrate that atomically thin WSe₂ interfaces can modulate coercivity, magnetization, and squareness through proximity effects, establishing a tunable and thermally stable platform for spintronic device applications. Full article

The intense low-frequency magnetic field generated by the Electromagnetic Aircraft Launch System (EMALS) during operation poses a serious EMI threat to electronic equipment within carrier-based aircraft nacelles. To address this, a three-dimensional transient finite element model of a long-primary double-sided linear induction motor is established. Using a quasi-static equivalent method, the 118 Hz magnetic field distribution inside and outside a typical engine nacelle is characterized. Results indicate that due to the skin depth significantly exceeding material thickness, the eddy-current shielding of the aluminum alloy nacelle is inadequate, producing internal field intensities that far exceed standard limits and directly threaten sensitive onboard electronics. Based on the magnetic shunting principle, a composite shielding strategy is proposed: applying a flexible high-permeability coating on the nacelle surface to attenuate the overall field, supplemented by local permalloy shields for core equipment. Simulation verification demonstrates that this approach reduces the internal field to safe levels. It achieves effective shielding performance while balancing engineering feasibility with lightweight requirements, providing a viable pathway for ensuring the reliable protection of carrier-based aircraft in intense electromagnetic environments. Full article