Magnetic and thermodynamic effects of heisenberg spin modification in an antiferromagnetic mixed-spin Ising-heisenberg C60-like nanostructure

This study investigates the thermo-fluidic dynamics of a magnetised hybrid Ag-TiO₂/EG-water nanofluid over a bi-directionally stretching/shrinking surface, a configuration relevant to advanced thermal management in biomedical, aerospace, and electronics cooling. To address the limitations of traditional numerical approaches in multi-parameter optimization, novel Artificial Neural Network (ANN) optimized with the Levenberg–Marquardt algorithm (LMA) is developed. The model incorporates realistic physical effects, including temperature-dependent viscosity and thermal conductivity, surface suction, and Joule heating. A high-fidelity dataset was generated by solving the transformed governing equations using MATLAB's bvp4c solver, covering the parameter ranges: magnetic parameter (1 ≤ M ≤ 5), mixed convection (−1 ≤ λ ≤ 6), variable viscosity (0.1 ≤ a ≤ 1.5), thermal conductivity (0.1 ≤ b ≤ 0.5), stretching ratio(0.1 ≤ ε ≤1), suction parameter (−1 ≤ S ≤ 1), and nanoparticle volume fraction (0.01 ≤ ϕ ≤ 0.1). The velocity and temperature data, varying with M , ε , λ , a , and b , were divided into 70% training, 15% validation, and 15% testing sets for ANN-LMA modelling. The framework achieved absolute 10 −3 –10 −9 and MSE between 10 −10 –10 −7 , demonstrating high predictive accuracy. Results reveal that the magnetic field enhances vertical velocity in shrinking flows but reduces it in stretching flows, while horizontal velocity is suppressed in both cases. Temperature rises with magnetic and mixed convection effects, and variable conductivity causes hybrid nanofluids to exhibit up to 45% higher thermal elevation than mono nanofluids. Notably, a 10% Ag + TiO₂ mixture enhances heat transfer by 45%, compared to 18.6% for 10% Ag alone. The novelty of this work lies in its integrated AI-driven framework that accurately captures coupled multiphysics interactions, providing a rapid and reliable predictive tool for the design of advanced thermal-MHD systems. • A high-fidelity computational framework is developed for MHD hybrid nanofluid flow over a bi-directional deformable surface. • Variable viscosity, thermal conductivity, Joule heating, suction, and mixed convection effects are incorporated for realistic modelling. • A data-assisted ANN–LMA model accurately predicts thermo-fluidic behaviour with absolute errors as low as 10 −9 . • Hybrid Ag–TiO₂ nanofluid exhibits significantly enhanced thermal performance compared to conventional nanofluids. • Heat transfer is improved by 18.6% with Ag nanoparticles and by 45% using Ag–TiO₂ hybrid nanoparticles.

Utilizing fabry-perot cavity filled with magnetic fluid and harmonic vernier effect to implement ultra-sensitive magnetic field vector detection

Research on the magnetic field enhancement effect and mechanisms of double-layer capacitor

Energy consumption represents an increasing critical challenge for particle physics research laboratories like CERN. This issue is particularly significant in large accelerator facilities, where normal conducting magnet technology plays a fundamental role in the beamline design. In this context, research, development, and study of high-temperature super conducting (HTS) magnets offer promising solutions to improve energy efficiency. HTS materials can substantially reduce energy consumption and operational costs enabling higher operating temperatures compared to conventional superconductors. When combined to superferric magnets designed, this technology can offer simpler, cost-effective designs, exploiting the iron yoke pole shape for magnetic field quality reducing the influence of the high temperature superconductor large magnetization effect. In this paper, a combined superferric (sextupole and quadrupole) HTS magnet for the FCC-ee main ring is described. The main goal of this proposal is to produce the same performances of the current resistive magnet configuration while reducing magnet length in the accelerator. This design increases the dipole filling factor, which offers a slight decrease in overall energy consumption through reduced RF cavity power requirements or improved beam luminosity at constant RF power consumption. Furthermore, a preliminary thermo-mechanical study is reported to have an initial look at the power consumption of the magnets. This project targets high-energy, low-field particle accelerators, leveraging the use of iron yokes to minimize the required HTS material, hence the cost of the magnet, and reduce even more the manufacturing technologies needed. This approach balances cost efficiency with performance, offering significant progress in sustainable accelerator technology.

Predicting the relative roles of gravitational collapse and stellar feedback in star formation within extreme, low-density environments—such as the tidal tails produced by galaxy mergers—remains a fundamental challenge. These environments provide unique natural laboratories for testing star formation theories under conditions analogous to the early universe. However, existing models often fail to reconcile large-scale gravitational dynamics with localized feedback processes in such diffuse media. To bridge this gap, a reproducible, open-sciencebased theoretical framework is presented that integrates public, multi-wavelength observational datasets with high-resolution resistive magnetohydrodynamic (MHD) simulations. Our methodology is built on archival data from three flagship observatories: the James Webb Space Telescope (JWST), which is used to study young stellar populations and newly formed clusters. This telescope provides high-resolution infrared imaging and spectroscopy, enabling precise measurements of stellar ages, masses, and dust extinction. Atacama Large Millimetre/submillimetre Array (ALMA): used to trace cold molecular gas and analyze kinematic structures. These public datasets are used as quantitative constraints in resistive magnetohydrodynamic (MHD) simulations that incorporate magnetic fields, radiative cooling, sub-grid star formation, and stellar feedback, ensuring that the simulation results remain consistent with observational reality. Using the open-source code PLUTO, we model the formation of tidal structures while resolving key plasma physics, including localized resistivity to capture magnetic reconnection effects. Synthetic observations are directly generated from simulation outputs using radiative transfer post-processing, enabling point-by-point comparison with real data. To rigorously quantify agreement between model and observation, we implement a Bayesian inference framework that propagates observational uncertainties and yields posterior constraints on key parameters (e.g., magnetic field strength, feedback coupling efficiency). Through this integrated pipeline, the aim is to determine whether star formation efficiency in lowdensity tails is regulated by gravitational confinement from tidal compression or by localized feedback. Expected outcomes include quantitative estimates of virial stability parameters for observed gas complexes, spatial correlation analyses to gauge feedback coupling efficiency, and statistically robust constraints on uncertain model parameters. This framework is fully reproducible: all data are public, simulation codes are opensource, and analysis scripts will be archived with a DOI upon acceptance. By transparently linking theory and observation, this approach provides a methodological blueprint for studying star formation in interacting systems, with direct implications for galaxy evolution models and future observational strategies.

Improving heat transmission is a modern challenge that affects a wide range of industries, including electronics, heat exchangers, biochemical reactors, and others. Nanofluids (NFs) hold considerable potential as a valuable tool in an effort for increased energy transfer efficiency. Therefore, the objective of this work is to learn the way to use Nanofluids (NFs) to improve heat transmission. This study examines the flow and heat transfer characteristics of Williamson hybrid nonfluids (HNF) containing graphene oxide (GO) and copper (Cu) nanoparticles. The HNF is applied across a thin needle with an applied magnetic field. Viscous dissipation effects and dual slip boundary conditions are imposed to account for energy dissipation and slip effects. Applying similarity transformations, the governing partial differential equations are converted into non-linear ordinary differential equations, numerically solved using the Bvp4c technique in MATLAB. The study identifies that increments in factors such as the magnetic factor M, needle size a , and velocity slip factor β, lead to a reduction in the velocity profile. The variations in factors such as a and thermal slip factor γ correspond to an increase in the temperature profile. A 200% increase in M results in approximately a 25% reduction in velocity, whereas the temperature increases by nearly 18% for a comparable rise in the Eckert number Ec. The hybrid nanofluid (Cu–GO/H2_22O) shows enhanced thermal transport compared to its mono-nanofluid counterpart. Skin friction increases with M and We, while the Nusselt number decreases with larger γ. The investigation focuses on potential applications such as polymer ejection for fiber technology and blood flow dynamics. These results highlight the strong coupling between magnetic effects, non-Newtonian rheology, and dual-slip mechanisms, offering insights for applications involving microscale controlled cooling and precision fluid transport. Graphical and tabular results are presented and discussed, providing a visual and quantitative analysis of the observed trends.

Purpose The purpose of this study is to develop a unified numerical and intelligent predictive framework for analyzing unsteady mixed convection of a Carreau fluid over a rotating cone in the presence of magnetic, viscous dissipation and Soret–Dufour cross-diffusion effects. The work seeks to clarify the influence of these coupled mechanisms on momentum, heat and mass transfer characteristics and to establish an artificial neural network (ANN)-based surrogate model for rapid and accurate prediction of thermal performance metrics relevant to rotating machinery, thermal management devices and other engineering systems involving non-Newtonian double-diffusive transport. Design/methodology/approach An unsteady MHD mixed-convection model for Carreau fluid flow over a rotating cone is developed by incorporating viscous dissipation and Soret–Dufour cross-diffusion effects. Under boundary-layer assumptions, the governing partial differential equations are reduced to coupled nonlinear ordinary differential equations using similarity transformations. These equations are solved numerically with MATLAB’s BVP4C solver under suitable boundary conditions, and the numerical results are validated against published benchmark data. The generated high-fidelity data set is subsequently used to construct a feed-forward ANN trained by the Levenberg–Marquardt algorithm to obtain fast and reliable predictions of the principal heat- and mass-transfer characteristics. Findings This study reveals that heat and mass transfer in Carreau fluid flow over a rotating cone are highly sensitive to magnetic, elastic, thermal and cross-diffusion effects. Magnetic forcing intensifies the velocity field, while the Dufour effect raises the temperature distribution within the boundary layer. Higher Prandtl, Schmidt and Soret numbers strengthen the surface heat and mass transfer rates, whereas larger Weissenberg and Eckert numbers weaken the Nusselt number. Variations in buoyancy-related parameters produce notable rotational flow modulation. The ANN surrogate shows excellent agreement with the numerical solutions, with a regression coefficient exceeding 0.999 and MSE on the order of 10–6. Originality/value The study’s value lies in presenting a unified analysis of unsteady mixed convection of a Carreau fluid over a rotating cone under the combined effects of magnetic forcing, viscous dissipation and Soret–Dufour double diffusion. While these mechanisms have often been studied separately, their coupled influence in this rotating-cone configuration is addressed here within a single framework. The work further adds value by integrating high-fidelity numerical solutions with an ANN surrogate for rapid prediction of key transport quantities, offering both physical insight and a practical predictive tool for thermal design and optimization in rotating engineering systems.

A refined two-dimensional formulation is presented for the multifield analysis of laminated doubly-curved shells with full coupling between mechanical elasticity, electricity, magnetism, and hygro-thermal effects, under thermodynamic equilibrium conditions. The unknown variables are expanded along the thickness direction using higher-order theories and a unified formulation, following the Equivalent Layer-Wise (ELW) approach. This framework enables the structure to accommodate arbitrary values of multifield variables at the top and bottom surfaces, as well as general distributions of multifield surface loads. The fundamental equations are derived from the Hamiltonian principle in curvilinear principal coordinates, considering generally anisotropic layers obtained through analytical homogenization. Moreover, an arbitrary smooth through-thickness variation of the material properties is introduced within each layer. A semi-analytical Navier solution is developed for simply-supported shells. In the post processing stage, an innovative procedure based on Generalized Differential Quadrature (GDQ) and Generalized Integral Quadrature (GIQ) is employed to reconstruct the multifield response of the doubly-curved solid by solving the three-dimensional balance equations along the thickness direction. The model is validated through representative examples, with numerical predictions compared against 3D finite element solutions obtained from commercial software. Further analyses are carried out to explore additional coupling effects, examining the influence of multifield interactions on both the mechanical and multifield response of the structure, as well as the impact of material gradation. Overall, the proposed model provides an efficient tool for investigating the response of laminated structural components with multifield properties, while capturing coupling phenomena that cannot be addressed with conventional commercial software.

This investigation examines the momentum, thermal, and species transport characteristics of an ethylene glycol-based copper nanofluid over a rotating disk configuration, incorporating the combined influences of an aligned magnetic field and Arrhenius activation energy. The mathematical model, comprising nonlinear partial differential equations for momentum conservation, energy balance, and concentration distribution, is reduced to a system of ordinary differential equations through appropriate similarity transformations. Numerical solutions are obtained using the BVP5C algorithm, yielding detailed velocity, temperature, and concentration profiles across the boundary layer. The principal novelty of this work lies in the simultaneous consideration of activation energy effects and aligned magnetic field orientation on nanofluid behavior in rotating disk geometry, a combination not previously addressed in the literature. A systematic parametric study examines how key physical quantities like magnetic field intensity, nanoparticle volumetric concentration, activation energy parameter, and Schmidt number—influence the heat and mass transfer characteristics of the system. The results demonstrate that increasing magnetic field strength produces a retarding effect on fluid motion, with both radial and tangential velocity components diminishing as the Lorentz force intensifies. Furthermore, the activation energy parameter exhibits a pronounced influence on species transport, significantly modifying concentration distributions and mass transfer rates at the disk surface. These findings contribute to the fundamental understanding of nanofluid behavior under coupled magnetic and chemical reaction effects, with potential implications for thermal management systems and biomedical applications

Controlling the subpicosecond coherent spin and valley dynamics with anomalous magnetic proximity effect

The chiral magnetic effect (CME) in relativistic heavy-ion collisions originates from a chirality imbalance among quarks within metastable QCD vacuum domains and may be linked to charge-parity violation, which is believed to play a crucial role in the matter-antimatter asymmetry of the universe. Over the past two decades, extensive experimental efforts at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider have been devoted to the search for evidence of the CME. Recent advances have greatly improved our understanding of background contributions that can mimic CME-like signals. In particular, analyses utilizing techniques designed to suppress flow-related backgrounds indicate that the CME signal at the RHIC, if present, is small. To further investigate potential background sources, particularly those associated with strong electromagnetic fields, we estimate the contribution from coherent photon-nuclear interactions. These interactions are driven by intense electromagnetic fields produced in ultrarelativistic heavy-ion collisions, with cross sections that scale with the field strength. Notably, the polarization of the incident photons is aligned with the electric field, which is oriented along the impact parameter direction and perpendicular to the magnetic field. Consequently, such processes can generate charge-dependent correlations that mimic key features of the CME signal, yet originate from different physics mechanisms and are distinct from flow-induced backgrounds. In this study, we quantitatively assess the influence of these coherent photon-nuclear interactions on the precision measurement of the CME, aiming to improve the separation of the genuine CME signal from these background contributions.

Purpose This study aims to develop an electro-thermal magnetohydrodynamic (ET-MHD) model of sperm transport in the cervical canal, treating cervical mucus as a Jeffrey fluid to capture memory and relaxation effects. It also examines the influence of thermal effects, magnetic and electric fields and wall waviness on sperm motion, heat transfer, trapping and pumping in fertile and infertile mucus. Design/methodology/approach The cervical canal is modeled as a two-dimensional wavy channel, and the swimming sperm as a deforming sheet. The mucus is considered an electrically conducting Jeffrey fluid. Under creeping-flow conditions, closed-form solutions are derived for velocity, temperature, pressure gradient, pressure rise, propulsive velocity and work done. Parametric effects of Hartmann number, Brinkman number, electric field, fluid relaxation and wave amplitude are studied for fertile and infertile mucus. Findings Magnetic field and fluid memory reduce sperm-induced flow, propulsion and bolus trapping, with stronger suppression in infertile mucus. Fertile mucus shows higher flow and propulsion, especially without magnetic effects. Temperature increases due to Joule heating and viscous dissipation, while elasticity lowers it. Magnetic forces reduce trapped bolus size, electric field has little effect on trapping and stronger electromagnetic fields increase pressure demand. Pumping improves in the retrograde region but declines with increasing fluid elasticity. Originality/value To the best of the authors’ knowledge, this study presents the first analytical ET-MHD Jeffrey fluid model for sperm transport in the cervical canal, offering useful insights for fertility assessment and thermally sensitive drug-delivery applications.

Destructive quantum interference (DQI) is an intriguing phenomenon in molecular devices known for ultralow conductance. A consequent prospect is the spin filtering induced by spin splitting of DQI features. Here we demonstrate that in magnetic DQI molecular junctions consisting of acene molecules and ferromagnetic electrodes, a weak magnetic proximity effect (MPE) on molecular ends may induce a distinct spin splitting of DQI dips in spite of the subtle spin splitting of eigenstates. The mechanism analysis reveals an additional contribution from spin splitting of wave functions, which comes out as a result of the coexistence of MPE and the electron-electron interaction. The results of model calculations are further verified by first-principles calculations. This work provides a strategy to achieve efficient spin filtering at low bias without large intrinsic magnetic moments or strong magnetic fields and opens a new arena for spin-dependent DQI in molecular spintronics.

Interfacial engineering remains a critical challenge in flexible spintronics where simultaneously optimizing crystalline quality, magnetic robustness, and proximity effects is difficult. By depositing cobalt films under constant adatom kinetic energy onto flexible substrates with contrasting enthalpies of fusion-muscovite mica (high ΔHf) and perfluoroalkoxy alkane (PFA, low ΔHf)-we demonstrate a thermodynamics-driven strategy to control growth mode, surface roughness, microstructure, and spin coupling. Atomic force microscopy and X-ray diffraction reveal layer-by-layer growth with low roughness on mica (Ra ≈ 1.7-2.3 nm) versus island-like rough morphologies on PFA (Ra up to 47.7 nm). Magnetic measurements show a 50% enhancement of coercive force and mechanical flexibility on PFA, while Pt capping layers amplify magnetic proximity effects more significantly on rougher PFA interfaces. We introduce the Thermal-Informed Roughness Activation (TIRA) model, linking substrate enthalpy and adatom energy to interfacial roughness and spintronic properties. This framework offers practical design rules for optimizing flexible spintronic devices by balancing crystallinity, magnetic coupling, and bendability.

The magnetic proximity effect induces spin splitting in graphene through interfacial exchange coupling, enabling control of spin-resolved band structure, most clearly revealed near charge neutrality where low carrier density enhances spin-dependent transport signatures. Here, we use cobalt contacts to induce magnetic proximity in graphene and probe spin-resolved bands with pure spin currents, observing a gate-tunable inversion of the nonlocal spin signal near the charge neutrality point. Similar inversions occur at satellite neutrality points in graphene-boron nitride aligned superlattices, demonstrating that proximity-induced spin splitting governs spin transport across both primary and reconstructed bands. In a bilayer graphene superlattice device, where a bandgap enhances energy-selective spin filtering, we observe spin polarizations approaching 50% and nonlocal spin resistances exceeding 300 Ω, nearly two orders of magnitude larger than those away from the charge neutrality point. Such electrically controlled spin polarization via proximity interactions at low carrier densities opens opportunities for low-power spintronic devices.

Hybrid nanofluids possess exceptional thermal conductivity, but one of the major concerns with nanoparticles is agglomeration. While the usage of surfactants or dispersants can be used to mitigate this issue, numerical investigation and sensitivity analyses can be more affordable when attempting to optimize and design a thermal device. The consideration of thermal radiation with conductive and convective heat transfer and appropriate nanoparticles may provide a greater solution without compromising the efficacy of hybrid nanofluids. In the present work, the concept of magnetohydrodynamics (MHD) is used to examine the impact of thermal radiation on a stable, two-dimensional, incompressible hybrid fluid consisting of nanoparticles (MWNCT)-Fe3O4 and water flowing over a vertical surface. The flow is governed by established equations of fluid dynamics, which use the Rosseland diffusion model to incorporate radiation effects. The implicit finite difference (IFD) was used to solve the mathematical equations. Sensitivity analyses were conducted as functions of volume fraction, radiation and magnetic variables. This study also examines the streamlines and isotherm lines with respect to the volume fraction, radiation parameter and magnetic parameter of the heat source. The results indicate that for a fixed radiation parameter, increasing the nanoparticle volume fraction by up to 20% leads to a reduction of approximately 37% in the skin friction coefficient, while the corresponding Nusselt number increases by nearly 50%. Furthermore, the introduction of a magnetic field parameter significantly suppresses wall shear stress and modifies the thermal boundary layer thickness, demonstrating the competing interaction between Lorentz-force-induced momentum damping and radiation-enhanced thermal diffusion. These quantified trends highlight the sensitivity of coupled momentum and heat transport to combined magnetic and radiative effects in hybrid nanofluid systems.

The objective of this study is to examine the influence of nonlocal thermoelasticity parameters on an orthotropic medium subjected to magnetic fields and rotational effects, within the framework of Green-Naghdi thermoelasticity theory (Type III). A time-dependent thermal load is applied to the free surface of the medium, and analytical solutions for the resulting thermal stresses, displacement, and temperature fields are derived using the normal mode analysis and eigenvalue approach techniques. Numerical simulations, implemented through MATHEMATICA programming, are conducted for a representative material to validate the theoretical model. The results are presented graphically to highlight the effects of various parameters, including time, non-locality, magnetic intensity, and rotational speed, on the thermoelastic response. These findings offer significant insights for advanced engineering and scientific applications, especially in geophysics, aerospace, and biomedical engineering, where complex multiphysical interactions and nonlocal effects play a critical role. The study also contributes to the ongoing development of generalized thermoelastic models capable of accurately capturing wave propagation and heat conduction behaviours in anisotropic and heterogeneous materials.

Abstract The Curie point depth (CPD), a key indicator of the lithosphere's thermal structure, is typically estimated using spectral analysis or interface inversion methods. However, these approaches often neglect the effects of remanent magnetization, leading to substantial uncertainty. To address this limitation, we propose a Curie‐Physics Informed Neural Network for high precision CPD estimation under strong remanence conditions. Our approach integrates realistic geological modeling by synthesizing a double magnetic interface with spatially correlated remanent magnetization. Synthetic magnetic anomalies are then efficiently generated using frequency domain computations. Furthermore, to ensure geological rationality, we incorporate a spectral constraint into the network, leveraging it as physical information to guide and regularize the training process. The effectiveness of our method is validated on both synthetic magnetic anomalies and field data from the Ordos region, China, demonstrating an improvement in precision under remanence conditions compared to conventional approaches.

Voltage-induced magnetization switching based on the voltage-controlled magnetic anisotropy (VCMA) effect is expected to be the ultimate low-power-consumption writing method for spintronic devices such as non-volatile magnetoresistive random-access memory. However, for conventional VCMA-driven dynamic magnetization switching, in which sub-nanosecond voltage pulses induce bidirectional switching by inducing a half precession of magnetization, even a small variation in the pulse widths of the order of several picoseconds can cause switching failure. This has become a major obstacle for developing voltage-controlled magnetoresistive random-access memory. Here we report VCMA-driven static magnetization switching by exploiting an artificial antiferromagnetic trilayer structure with interlayer exchange coupling. By applying bipolar voltages to the antiferromagnetic structure, we can demonstrate repeatable bidirectional switching. Unlike conventional dynamic switching, VCMA-driven static magnetization switching is induced in a wide range of pulse widths. This unconventional writing method is expected to be a key for developing various ultralow-power spintronic devices.