Search Results (12,937)

External stray magnetic fields from permanent magnet synchronous motors (PMSMs) may cause electromagnetic interference to nearby equipment and limit their application in space-constrained systems. To address this issue, this paper investigates the use of laminated Co-based amorphous ribbon shields for stray-field suppression. An efficient equivalent modeling method is proposed for the simulation of such multilayer thin shielding structures, in which the laminated shield is replaced by an equivalent single-layer model while preserving its macroscopic shielding behavior. The method is first assessed in 2-D through comparisons between refined laminated and simplified equivalent models under both linear permeability and nonlinear magnetization-curve descriptions, and is then extended to 3-D PMSM shielding analysis under static and rotating no-load conditions with experimental validation. Results show that the 10-layer amorphous ribbon shield, with a total thickness of 420 μm, achieves a maximum shielding effectiveness of 7.9 dB at a measurement distance of two motor radii. The maximum deviation between simulation and experiment is 7.4%, and the equivalent model reduces computation time by 28% relative to the refined model. This method provides an accurate and efficient approach for the analysis and design of compact low-frequency magnetic shields for PMSMs. Full article

Background: Paramagnetic manganese (Mn(II)) has emerged as a promising alternative to gadolinium-based contrast agents (GBCAs) due to its favorable magnetic properties. Despite extensive research, no Mn-based agent has yet achieved clinical translation. Because free Mn(II) is toxic, macromolecular complexes incorporating stable macrocyclic DOTA chelators conjugated to polysaccharides may enhance coordination stability and improve the safety profile of Mn(II)-based contrast agents. Methods: Two chemical routes, maleimide- and ester-mediated, were evaluated for covalent coupling of DOTA-based macrocyclic ligands to the backbone of selected poly- and oligosaccharides. Subsequently, DOTA-modified carboxymethyldextran, aminodextran, and chitosan oligosaccharide were labeled with paramagnetic Mn(II) under mild conditions. ATR-FTIR confirmed the successful conjugation of DOTA chelators to the sugar backbone. The conjugates were further characterized by DLS, ICP-MS, and FPLC. In vitro relaxivity was measured at high field strength to evaluate MRI performance. In vivo contrast efficacy was first assessed using in ovo MRI in chicken embryos and subsequently evaluated by biodistribution studies in nude mice. Results: In vitro relaxivity studies demonstrated higher signal enhancement of the poly-/oligosaccharide-DOTA-Mn(II) conjugates compared with MnCl₂ and the clinical agent gadoteridol (ProHance^®). In ovo MRI showed persistent vascular enhancement up to 120 min, while in nude mice, contrast enhancement was observed in the liver, kidneys, and gallbladder 40 min post-injection. Conclusions: Mn(II)-tagged sugar-based imaging probes may offer a promising non-gadolinium alternative to GBCAs, with tunable biodistribution profiles depending on carrier molecular weight. Full article

Coated ferromagnetic conductors (CFCs) are widely used in the engineering field, such as transportation, petrochemicals, energy, etc. Owing to long-term exposure to harsh and corrosive environments, involving large temperature differences, cyclic loading and humidity, hidden corrosion occurring under the coatings of CFCs has been found to be one of the most critical defects posing a severe threat to the structural strength and safety of CFCs. Therefore, it is important to conduct rapid imaging and quantitative evaluation of this hidden corrosion via Non-Destructive Evaluation (NDE) techniques. A magnetic field-viewing film (MFVF) characterizes magnetic fields by displaying corresponding color shifts, offering a direct visual representation of the magnetic field intensity. In light of this, this paper proposes an MFVF-based probe composed of multiple micro-sensor units for fast imaging of hidden corrosion in CFCs. An image-processing technique based on the modified Canny algorithm is subsequently proposed for identification of corrosion opening profiles in MFVF images. Based on the identification results, an assessment of hidden corrosion parameters is conducted. It is inferred from the experimental results that the opening area, depth and volume of hidden corrosion can be quantitatively evaluated, with an average accuracy of 86.1%. Full article

This study investigates unsteady magnetohydrodynamic free convection flow past a rotating vertical plate embedded in a Darcy–Forchheimer porous medium. The formulation incorporates Hall current, thermal radiation, viscous dissipation, Joule heating, and an Arrhenius-type chemical reaction with activation energy to represent thermo-reactive transport in an electrically conducting fluid. The coupled nonlinear equations governing momentum, thermal energy, and species concentration are transformed into dimensionless form and solved numerically using the Crank–Nicolson scheme. Grid independence and validation tests confirm the accuracy and stability of the numerical procedure. The results show that electromagnetic forces, rotation, porous resistance, and thermo-reactive effects significantly influence wall shear stress, heat transfer, and mass transport. In particular, the interaction between magnetic field strength and Hall current alters near-wall transport behavior, highlighting the role of electromagnetic coupling in rotating porous systems. The study provides physical insight relevant to the design and analysis of transport processes in high-temperature energy systems, rotating reactors, and porous thermal management devices. Full article

With advancements in weak magnetic detection technology, the electromagnetic wake signals induced by UUVs in stratified seawater are becoming stable interference sources for detection equipment. This study developed a numerical model combining fluid dynamics and electromagnetism to examine the electromagnetic wake evolution of the UUV system under varying propeller propulsion coefficients, and formation mechanism of the wake electromagnetic field is revealed. The flow field results were validated using PIV and relevant literature. The flow characteristics of the near-field wake are analyzed by visualizing the vortex structure. Additionally, this study investigates the attenuation law of far-field wake using electromagnetic field intensity attenuation curves. The wake’s electromagnetic field frequency characteristics were examined through the normalized amplitude spectrum. Results indicate that the near-field wake vortex structure resembles a propeller’s topological structure. The electric field intensities in the near-field and far-field are approximately on the order of 10⁻⁴ V/m and 10⁻⁵ V/m, respectively, while the magnetic field intensities are around 10⁻¹⁰ V/m and 10⁻¹¹ V/m. The electromagnetic interference spectrum within the wake typically shows high intensity in the low-frequency band. A high-precision magnetometer can detect the electromagnetic field’s intensity and frequency characteristics. It offers theoretical support for developing advanced anti-interference algorithms in engineering practice. Full article

Blueberries are widely consumed due to their richness in nutrients, yet they are also prone to quality deterioration after being harvested, even at cold temperatures. Non-thermal physical technology is an important auxiliary method worth considering for maintaining the quality of this fruit while refrigerated. In this study, a static magnetic field (SMF) was applied as a complementary pretreatment strategy prior to cold storage of blueberries. The optimal SMF parameters were identified as 5 mT exposure for 12 h, as this significantly retarded decay and softening. The contents of ascorbic acid, total polyphenols, flavonoids and proanthocyanidins were elevated by 20.0%, 17.7%, 23.9%, and 9.1%, respectively. Concurrently, DPPH (1,1-diphenyl-2-picrylhydrazyl) radical-scavenging capacity, catalase (CAT), and superoxide dismutase (SOD) activity markedly improved. Targeted metabolomic analysis revealed that SMF pretreatment significantly regulated polyphenol metabolic pathways and redirected polyphenol biosynthesis toward more stable and functional compounds, including three hydroxycinnamic acids, quercetin, dihydromyricetin, glycosylated hesperetin, and acylated delphinidin derivates. The synergistic effect of these SMF-elevated phenolics and the reinforced antioxidant system preserved the overall cold-storage quality of blueberries. These findings underscore the potential of SMF pretreatment as an effective physical technique for reducing postharvest blueberry losses. Full article

This paper presents a real-time magnetic adhesion force estimation framework for wall-climbing robots equipped with Halbach permanent magnet arrays (PMAs) and air-gap–adjustable mechanisms. Accurately computing the magnetic adhesion force between a PMA and a large ferromagnetic surface is challenging due to the nonlinear magnetization behavior of soft magnetic materials and the strongly coupled, highly nonuniform magnetic fields generated by Halbach arrays. Conventional analytical models fail to capture these effects, while finite element methods (FEM) incur prohibitive computational cost for real-time applications. To address this, we propose an analytical magnetic-force estimation model based on the magnetostatic MoI (Method of Images), which replaces the unknown magnetization inside the steel plate with an equivalent image magnet distribution that satisfies boundary conditions at the air–steel interface. The method avoids solving complex magnetization in soft magnetic media and enables a unified force computation for arbitrarily oriented magnet elements. Additionally, complex Halbach PMA geometries are approximated through cuboid-element segmentation into cuboid magnet array, allowing efficient force evaluation. Comparative studies demonstrate that the proposed method achieves accuracy comparable to FEM while reducing computation time by several orders of magnitude. Experimental validation using a linear Halbach array and a large steel plate proved that the framework can reliably estimate magnetic adhesion force across varying air-gap distances, meeting the real-time requirements of air-gap–adjustable wall-climbing robots. Full article

Heavy metal contamination of foods remains a persistent global challenge for food safety and public health, driven by industrialization, mining activities, intensive agriculture, and ongoing environmental degradation. This scoping review synthesizes peer-reviewed literature on the occurrence of priority toxic metals—arsenic, cadmium, lead, mercury, and nickel—in food matrices, with emphasis on contamination pathways, analytical detection strategies, and documented human health effects. The reviewed studies reveal widespread accumulation of heavy metals in staple foods, including cereals, vegetables, seafood, and processed products, with concentrations frequently approaching or exceeding international regulatory limits, particularly in regions exposed to strong anthropogenic pressure. Conventional laboratory-based techniques, such as atomic absorption spectrometry and inductively coupled plasma methods, remain the reference standards for quantitative determination and regulatory compliance; however, their application to large-scale or continuous monitoring is often constrained by cost, infrastructure, and operational complexity. Consequently, increasing attention has been directed toward emerging detection approaches, including portable X-Ray fluorescence, Raman/SERS spectroscopy, electrochemical biosensors, electronic tongues, and in situ magnetic measurements, as complementary tools for rapid screening and field-based surveillance. Among these, environmental magnetism and in situ magnetic techniques stand out as non-destructive, low-cost proxies capable of identifying metal-associated particulate contamination linked to food production systems. Chronic dietary exposure to heavy metals is consistently associated with neurotoxicity, nephrotoxicity, carcinogenicity, and oxidative stress, underscoring the need for integrated, multi-tiered monitoring frameworks to support early detection, risk assessment, and prevention. Full article

Compact stars serve as natural systems where matter exists at densities far beyond those achievable in laboratory experiments. Among them, magnetars are expected to possess interior magnetic fields that may reach values of the order of ${10}^{17}$–${10}^{18}$ G. These extreme conditions are expected to alter the microscopic and macroscopic properties of dense matter. In this review, we examine how strong magnetic fields affect fermionic matter through mechanisms such as Landau quantization and anomalous magnetic moment interactions. We further discuss the behavior of magnetized hadronic matter within relativistic mean-field approaches and consider the possible emergence of additional degrees of freedom, including hyperons, $\Delta$ resonances, meson condensates, and quark matter. The consequences of these effects for neutron star structure and observational constraints are also briefly outlined. Full article

Knowledge about the condition of electrical equipment in energy networks is of great importance to network operators. Partial discharges are a key parameter for evaluating the health of the insulation. While a quantifiable PD measurement for offline tests is state of the art, it is costly and labour-intensive. It, therefore, makes sense to carry out permanent monitoring during operation. At the medium-voltage level in the European interconnected grid, comprehensive monitoring of PD is not implemented. This study presents a novel sensor concept that is used to detect PD in medium-voltage switchgear and cables: the so-called Magnetic Flux Concentrator Sensor (MFCS). It is an inductive sensor concept with high sensitivity in the frequency range of a few MHz, like well-established High-Frequency Current Transformers (HFCTs) but with better magnetic saturation properties in specific use cases. The highly permeable ferrite core of the MFCS is unconventionally shaped, resulting in a higher-saturation field strength. Therefore, this sensor is not driven into saturation by the operating currents of typical MV power cables. Using the MFCS and conventional HFCT in a suitable combination enables direction-selective PD detection. This work presents the sensor concept and the method for directional detection of the PD location, as analysed and evaluated theoretically and practically with laboratory experiments. Full article

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

Electromagnetic hydrodynamic (EMHD) mixed convective flow of tetra hybrid nanofluid (TeHNF) in a Darcy-Forchheimer porous medium in a vertical channel with thermal radiation is considered in the paper. The electric and magnetic fields are homogeneous, magnetic perpendicular to the walls of the channel, and electric perpendicular to the plane formed by the directions of the magnetic field and the basic current. The channel walls are impermeable, and they are at constant but different temperatures. The basic equations that describe this problem are ordinary nonlinear differential equations (ODEs), and they are transformed into dimensionless ODEs by introducing dimensionless quantities, which are analytically solved using the homotopy perturbation method (HPM). The relations for velocity and temperature distributions, Nusselt numbers and shear stresses on the channel walls were determined. These relations are functions of introduced physical parameters that characterize the observed problem. For TeHNF, where the base fluid is water and the nanoparticles are made of aluminum oxide, titanium dioxide, magnesium oxide and magnetite, a part of the obtained results is given. Velocity and temperature plots are presented in the form of graphs, and Nusselt numbers and shear stresses are presented in the form of tables. Based on the analysis of the obtained results, appropriate conclusions were drawn. It was concluded that an increase in the Hartmann number as well as an increase in the porosity factor decrease the fluid velocity and shear stress, and increase the fluid temperature and Nusselt numbers. Higher values of the Forchheimer factor and higher heat radiation correspond to lower fluid velocities, lower temperatures, lower values of shear stresses and Nusselt numbers. By increasing the value of the Grashof number, the velocity of the fluid increases, and so do the shear stresses. TeHNF shows advantages over simpler hybrid nanofluids and commercial fluids. Full article

In order to protect the high-sensitivity optical lens of the “magnetic field and velocity field imager” in extreme deep space environments, this paper proposes a new type of dual redundant planetary differential lens cover drive mechanism. In view of the critical vulnerability that traditional single-motor direct drive is prone to sudden mechanical jamming and catastrophic single-point failure (SPF) in severe tasks such as Jupiter exploration, this study constructs a “dual input single output (DISO)” rigid decoupling architecture from the perspective of physical topology. Through theoretical analysis and kinematic modeling, the adaptive decoupling mechanism of the two-degree-of-freedom (2-DOF) system under unilateral mechanical stalling is revealed. Dynamic analysis shows that in the nominal dual-motor synergy mode, the system shows a significant “kinematic load-sharing effect”, thus greatly reducing the sliding friction and gear wear rate. In addition, under the severe dynamic fault injection scenario (maximum gravity deviation and sudden jam superposition of a single motor), the cold standby motor is activated and the dynamic takeover is quickly performed. The high-fidelity transient simulation based on ADAMS verifies that although the fault will produce transient global torque spikes and pulsed internal gear contact forces at the moment, all extreme dynamic loads remain well within the structural safety margin. The output successfully achieved a smooth transition, which is characterized by a non-zero-crossing velocity recovery. This research provides an innovative theoretical basis and a practical engineering paradigm for the design of high-reliability fault-tolerant mechanisms in deep space exploration. Full article

The field of nanoparticle-based biotechnology has undergone substantial advancement, characterized by progress in targeted drug delivery systems, the development of innovative diagnostic and imaging platforms, the expanded adoption of environmentally sustainable (“green”) synthesis approaches, and an increasing emphasis on the integration of emerging technologies such as artificial intelligence and nanorobotics. Conventional nanoparticle synthesis often involves toxic reducing agents; however, recent advances promote eco-friendly green synthesis methods utilizing biological systems such as bacteria, fungi, algae, yeast, plants, and actinomycetes. These biological approaches are safe, sustainable, cost-effective, and capable of producing highly stable Nanoparticles (NPs). The interaction of nanomaterials with biological systems is crucial for developing intracellular and subcellular drug delivery technologies with minimal toxicity, governed by nano–bio interface mechanisms such as cellular translocation, surface wrapping, embedding, and internal attachment. Key factors influencing NP behavior include morphology, size, surface area, surface charge, and ligand chemistry. Magnetic nanoparticles, particularly iron-based forms, exhibit unique superparamagnetic properties that are strongly influenced by particle size, as explained by the Néel relaxation mechanism, in which thermal energy induces flipping of magnetic moments. Nanoparticles demonstrate diverse modes of action, including antimicrobial activity, reactive oxygen species (ROS)-induced cytotoxicity, genotoxicity, and plant growth promotion. NP performance and biological effects are strongly dependent on their size, shape, dosage, and concentration. This critical review article aims to elucidate evolution, classification, preparation methods, and multifaceted applications of nanoparticles Full article

Artificial intelligence and machine learning techniques can be used for performing magnetic testing on spacecraft that has historically been difficult and risky to perform. Some of the difficulty arises from the need to take these measurements from within the turbulent near-field area of the spacecraft. Some methods of testing require the spacecraft to be hoisted in the air and swung while the measurements are being taken so that any magnetic signatures in the test area can be removed. These new artificial intelligence and machine learning techniques can be used to determine the magnetic torque of complex magnetic systems. Here we will describe a test method that collects such data and poses much less risk to the spacecraft. We will also show some combinations of hyper-parameters that can be used to increase the speed and accuracy of the models. Some models were able to achieve over 96.6% accuracy of multipole determination on simulated data and over a 99.99% accuracy of dipole moment determination on simulated data. Applications include attitude control systems (ACS), science instrument locations, and data analysis. Full article