This study focuses on the reliability issue of magnetic fluid (MF) in the magnetic fluid sealing technology for the upper guide bearing (UGB) of hydro-generators and proposes selection schemes for MF suitable for different models of hydro-generators. By analyzing the performance indicators of five base fluids and MFs, including the acid value, flash point, oxidation stability, magnetorheological performance, breakdown voltage, dielectric loss factor and volume resistivity, the influencing factors of the insulating performance of MFs and their mechanism in sealing the UGBs of hydro-generators are investigated. The results show that, when the spindle speed is below 27 rpm, the viscosity of the MF is dominated by the magnetic field strength, while, when the speed exceeds 27 rpm, the viscosity of the MF is dominated by the shear rate. In addition, the addition of magnetic nanoparticles (MNPs) causes the breakdown voltage of the base carrier liquid to fluctuate in the range of 31.2–55.9 kV, the dielectric loss factor to fluctuate in the range of 2.5 × 10−4–6.7 × 10−3, and the volume resistivity to fluctuate in the range of 2.8 × 1011–2.6 × 1012 Ω·m. The research results provide a theoretical basis for the application of high-efficiency and stable magnetic fluid sealing technology.

Regrettably, glioblastoma multiforme (GBM) remains the deadliest form of brain cancer, with a very unfavorable prognosis for life expectancy for the patient. We report, for the first time, the green colloidal synthesis of cobalt-doped magnetic iron oxide nanoparticles (Co-MNPs) as aqueous ferrofluids, using two anionic polysaccharide biopolymers, hyaluronic acid (HA) and carboxymethyl cellulose (CMC), as surfactants. These ferrofluids based on magnetite nanoparticles (HA@Co-MNP and CMC@Co-MNP) demonstrated superparamagnetic properties and magnetic-to-thermal conversion upon exposure to an alternating magnetic field (AMF), with the extent of conversion dependent on surfactant type. In addition, the ferrophase acted as a nanozyme, mimicking peroxidase-like activity in response to hydrogen peroxide, which is present at higher levels in tumor cells. The coupling of magnetic-heat capabilities with biocatalytic behavior enhances glioblastoma cell elimination and suppresses 3D neurospheroid growth. The results also showed that active targeting based on the HA biopolymer shell, due to its affinity for CD44 membrane receptors overexpressed in GBM, outperformed CMC-coated ferrofluid analogs. These magnetocatalytic-responsive nanoplatforms offer a broad avenue for the diagnosis and therapy of numerous cancers, potentially improving patients’ quality of life and prognoses.

Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with simplified motion patterns, thereby limiting physical plausibility and generalization. We propose Sim-DDNet, a simulation-data-based dual-domain network that combines k-space-based motion simulation with a joint image-k-space reconstruction architecture. Motion-corrupted data were generated from T2-weighted Alzheimer’s Disease Neuroimaging Initiative brain MR scans using a k-space replacement scheme with three to five random rotational and translational events per volume, yielding 69,283 paired samples (49,852/6969/12,462 for training/validation/testing). Sim-DDNet integrates a real-valued U-Net-like image branch and a complex-valued k-space branch using cross attention, FiLM-based feature modulation, soft data consistency, and composite loss comprising L1, structural similarity index measure (SSIM), perceptual, and k-space-weighted terms. On the independent test set, Sim-DDNet achieved a peak signal-to-noise ratio of 31.05 dB, SSIM of 0.85, and gradient magnitude similarity deviation of 0.077, consistently outperforming U-Net and U-Net++ across all three metrics while producing less blurring, fewer residual ghost/streak artifacts, and reduced hallucination of non-existent structures. These results indicate that dual-domain, data-consistency-aware learning, which explicitly exploits k-space information, is a promising approach for physically plausible motion artifact correction in brain MRI.

Stress detection is an effective way to prevent pipeline failure, but stress recognition alone can hardly meet the safety and maintenance requirements of pipelines. Rather, improving the accuracy of stress detection and quantification has long been a top priority in the engineering sector. In the present study, stress detection models for pipelines were developed under varying magnetic excitation intensities, and the influence of a changing magnetic excitation field on stress recognition capacity was investigated. The variation law of the accuracy of stress detection under different excitation intensities was determined and validated experimentally. The results showed that at an excitation intensity of 2.5 of kA/m, the polarity of weak magnetic signals flipped when used to detect stress below 40 MPa, making the stress quantification difficult. The stress recognition capacity was the greatest under an excitation intensity of 7.5 kA/m for the stress below 40 MPa and the greatest under an excitation intensity of 5 kA/m for the stress of 40–160 MPa. Our research findings offer theoretical clues for choosing an appropriate excitation intensity for stress detection. The findings provide technical support for pipeline integrity assessment and risk warning, playing an important role in ensuring the safe operation of oil and gas transportation systems.

Magnetic flux leakage (MFL) testing is a widely used non-destructive method for detecting defects in ferromagnetic pipelines. However, demagnetizing fields in ferromagnetic materials can distort MFL signals, reducing detection accuracy. This study integrates demagnetizing components into the classical magnetic charge model using magnetic charge and dipole theories to assess the impact of demagnetization on MFL signals. The behavior of MFL signals under demagnetization, particularly for rectangular defects, is analytically characterized. The generation mechanism of the demagnetizing field is examined, and explicit expressions for triaxial demagnetizing components in cylindrical pipelines are derived. The effects of geometric parameters, such as inner and outer diameters and pipeline length, on demagnetizing components are systematically studied. The influence of demagnetization on MFL signal transmission is also explored. MFL scanning experiments on rectangular defects of different sizes validate the theoretical model, revealing that demagnetization attenuates the axial and radial components while enhancing the circumferential component. The proposed model improves prediction accuracy, reducing errors in the axial and radial components by 14.9% and enhancing the circumferential signal by 15%. Experimental MFL waveforms align closely with the model, confirming its validity and effectiveness.

Two diruthenium(II,II) naphthyridine complexes coordinated with 4-trifluoromethylbenzoate (O2CPh-4-CF3) and 3,5-bis(trifluoromethyl)benzoate (O2CPh-3,5-diCF3) ligands, formulated as [Ru2(npc)2(O2CPh-4-CF3)2] (4; npc = 1,8-naphthyridine-2-carboxylate) and [Ru2(npc)2(O2CPh-3,5-diCF3)2] (5), respectively, were synthesized and structurally characterized. Single-crystal X-ray diffraction analysis revealed that both 4 and 5 form a direct metal–metal bond between the two Ru ions (2.2893(8) and 2.2896(7) Å, respectively) and adopt a paddlewheel-type structure in which two npc and two trifluoromethyl-substituted benzoate ligands are coordinated to a Ru24+ core with a cis-2:2 arrangement. The temperature dependence of the magnetic susceptibility measurements of 4 and 5 exhibited very large zero-field splitting (D = 242 and 246 cm−1, respectively) of the triplet ground state of the Ru24+ core, similar to that of [Ru2(npc)2(O2CPh)2] (3; D = 238 cm−1). Owing to the effects of the trifluoromethyl substituents, compared with 3, 4 and 5 showed (i) a significant blue shift of the absorption bands in the visible region and (ii) a positive shift of the redox potentials, with both shifts becoming more pronounced as the number of trifluoromethyl substituents increased. These experimental results are in good agreement with the electronic structure results obtained from density functional theory calculations.

Magnetic nanoparticles (MNPs) perturb magnetic field homogeneity, influencing transverse relaxation and the full width at half maximum (FWHM) of nuclear magnetic resonance (NMR) spectra. In Nuclear Magnetic Resonance (NMR), this appears as decay of the free induction decay (FID) signal, whose relaxation rate determines spectral FWHM. In D2O containing MNPs, both nanoparticles and solvent molecules undergo Brownian motion and diffusion. Under a vertical main field (B0), MNPs respond to their magnetization behavior, evolving toward a dynamic steady state in which the time-averaged distribution of local field fluctuations remains stable. The resulting spatial magnetic field can thus characterize field homogeneity. Within this framework, Monte Carlo simulations of spatial field distributions approximate the dynamic environment experienced by nuclear spins. NMR experiments confirm that increasing MNP concentration and particle size significantly broadens FWHM, while stronger B0 enhances sensitivity to MNP-induced inhomogeneities.

Background: The study of non-Newtonian fluids in thin channels is crucial for advancing technologies in microfluidic systems and targeted industrial coating processes. Nanofluids, which exhibit enhanced thermal properties, are of particular interest. This paper investigates the complex flow and heat transfer characteristics of a Sutterby nanofluid (SNF) within a thin channel, considering the combined effects of magnetohydrodynamics (MHD), Brownian motion, and bioconvection of microorganisms. Analyzing such systems is essential for optimizing design and performance in relevant engineering applications. Method: The governing non-linear partial differential equations (PDEs) for the flow, heat, concentration, and bioconvection are derived. Using lubrication theory and appropriate dimensionless variables, this system of PDEs is simplified into a more simplified system of ordinary differential equations (ODEs). The resulting nonlinear ODEs are solved numerically using the boundary value problem (BVP) Midrich method in Maple software to ensure accuracy. Furthermore, data for the Nusselt number, extracted from the numerical solutions, are used to train an artificial neural network (ANN) model based on the Levenberg–Marquardt algorithm. The performance and predictive capability of this ANN model are rigorously evaluated to confirm its robustness for capturing the system’s non-linear behavior. Results: The numerical solutions are analyzed to understand the variations in velocity, temperature, concentration, and microorganism profiles under the influence of various physical parameters. The results demonstrate that the non-Newtonian rheology of the Sutterby nanofluid is significantly influenced by Brownian motion, thermophoresis, bioconvection parameters, and magnetic field effects. The developed ANN model demonstrates strong predictive capability for the Nusselt number, validating its use for this complex system. These findings provide valuable insights for the design and optimization of microfluidic devices and specialized coating applications in industrial engineering.

This study investigates the magneto-optical properties of a ferrofluid using an accessible Ferrocell device. Our findings demonstrate that the ferrofluid’s behavior is critically dependent on its concentration. At high concentrations, the medium is optically dense, with inter-particle scattering and absorption dominating, which prevents the formation of clear light patterns. However, with intermediate dilution, the system enters a “pattern formation zone” where the magnetic field effectively aligns the nanoparticles, creating complex, visible light patterns like horocycles. The appearance of these patterns provides evidence of field-induced ordering and structural coloration. The colors observed are not due to pigments, but result from the interaction of light with the periodic structures formed by the aligned nanoparticles. Our analysis, supported by the Mueller matrix framework, confirms that the ferrofluid acts as a retarder. The birefringence induced by the magnetic field varies across the film, leading to a chromatic dispersion that selectively suppresses certain wavelengths. This process explains how a specific color, such as blue, can be blocked at one location while others pass through, creating structural colors observed in the patterns.

To develop single molecule magnets, a dinuclear complex [Dy2(HOBQ)4Cl6] (1) was prepared from the reaction of DyCl3 with benzo[h]quinolin-10-ol (HOBQ). Each Dy(III) ion shows a compressed octahedral geometry and the two Dy(III) ions in 1 are bridged by two Cl− ligands to construct a dinuclear structure with the four HOBQ ligands on the axial positions and six Cl− ligands in the equatorial plane. Magnetic measurements showed that complex 1 is a field-induced single molecule magnet having an obvious magnetic hysteresis loop with an energy barrier of 71(2) K. These experimental results are corroborated by the ab initio complete active space self-consistent field (CASSCF) calculations which also interpret the magneto-structural correlation. It is a typical example to achieve Dy(III) SMM through regulating coordination geometry, i.e., lengthening equatorial coordination bonds and shortening axial ones to form a compressed octahedral geometry.