Physics

Abstract This study proposes a thermal connection/isolation strategy to achieve less than 1 K pre-cooling required for Dilution Refrigerator (DR) operation in an integrated cryogenic system composed of a 4 K GM cryocooler, evaporative cooler, an Adiabatic Demagnetization Refrigerator (ADR), and a DR. We install a gravity-assisted thermosiphon-based passive heat switch (PHS) above the Gadolinium Gallium Garnet ADR stage, above the Still, and above the Mixing Chamber (MXC). An energy-balance model was used to estimate the attainable temperatures of the refrigeration chain, whereas convection and conduction models were used to design the PHS. In the OFF mode, considering conduction, the lower boundary warms from 2.2 K to 3.0 K in approximately 1.7 h, and the initial conduction heat inflow is estimated at ~ 319 μW—sufficiently low relative to the preparation time between cycles, indicating adequate thermal isolation. In the ON mode, a natural-convection correlation yields an effective heat transport of 11 mW, corresponding to a switching performance ratio about 36 times higher than in the OFF state. Experimentally, the Still pressure of 1.6 kPa was achieved, confirming the conceptual validity of the thermosiphon PHS. These results provide practical guidance for DR-interface design and robust pre-cooling in systems with small He-3 inventory, especially when combined with MXC pre-cooling pathway optimization and reduction of parasitic heat loads.

Quasi-three-level dual-wavelength narrow-linewidth lasers are vital for precision applications, but reabsorption-induced energy transfer inefficiencies hinder performance, creating a critical optimization gap. This study investigates their emission characteristics under reabsorption effects, developing a theoretical model for intracavity-pumped dual-wavelength lasing that incorporates pump beam waist position. Modal dynamics and output of 912/1064 nm lasing in Nd:GdVO4/Nd:YVO4 were characterized via simulation and experiment. Optimal Nd:GdVO4 doping and geometry mitigated reabsorption, suppressing parasitic absorption and enhancing power extraction. With a pump waist at z0I = 2.5 mm, a 912 nm output reached 0.05 W with a narrow linewidth of 0.28 nm; shifting to z0II = 3 mm yielded a 1064 nm output at 0.037 W with a linewidth of 0.63 nm, and the side-mode suppression ratio of both wavelengths exceeded 30 dB. These findings guide laser system design, enhance power extraction, and hold potential for advancing precision measurement and optical communication technologies.

Based on classical kinematic theory, this study carried out a thorough quantitative examination of the ping-pong ball rocket’s kinematic behavior. Using Newton’s laws of motion, the study thoroughly investigated how the ping-pong ball’s initial release height, the container’s radius, the container’s coefficient of restitution, and the air resistance coefficient affected the ball’s rebound height. The results indicate that the maximum rebound height of the table tennis ball increases with the initial release height of the container, and does so in an approximately linear manner. The maximum rebound height also increases with the coefficient of restitution of the container, following a trend that is initially gradual and then becomes steep. The maximum rebound height decreases as the base radius of the container increases, with the trend declining sharply at first and then slowly. The maximum rebound height decreases with an increase in the air resistance coefficient, showing an almost linear decreasing trend. Carefully planned experiments were carried out to verify these theoretical conclusions, and Tracker software was used to analyze the results. The reliability of the suggested model is confirmed by the experimental results, which closely match theoretical expectations. The kinematic features of the ping-pong ball rocket are better understood, thanks to this analysis, which also offers a solid framework for the theoretical modeling and experimental validation of related physical systems.

This study focuses on the effect of the magnetic dipole on magnetohydrodynamics. Magnetic Resonance Imaging (MRI) utilizes specialized hospital equipment designed for MRI scans to produce detailed internal images of the body by aligning protons within the body’s cells. This current research focuses on the method used, which encompasses thermal radiation, mixed convection, and steady flow. Sensitivity analysis to optimize heat transfer forms the novelty of this study. Furthermore, flow characteristics are assessed to analyze the thermal dipole effect. The study formulates the governing partial differential equations and utilizes similarity transformations to convert them into ordinary differential equations, which MATLAB’s bvp4c solver then solves. The primary outcome of the study is the demonstration of the influence on velocity and temperature profiles of the sheet for the dipole configured between parameters of 0.06 and 0.18. An increase in the magnetic dipole parameter results in a Nusselt number increase of around 3% and a skin friction reduction of almost 14%. When compared with response surface methodology, artificial neural networks were more accurate in prediction. Comparisons with existing literature also validated the study’s results. This type of study provides insight into the manipulation of thermal radiation for enhanced heat transfer, offering valuable information for designing heat transfer systems, particularly in thermal management systems. Overall, the study delivers on advanced control of magnetic systems and boundary layer flow in heat transfer.

Methylal has attracted attention as a promising organic solvent for polymers and thus is widely applied in industrial fields, but its solubility is not yet fully understood at the atomic scale. In this work, we report a classical molecular dynamics study of the solvation of polyvinyl butyral (PVB) in methylal solvent, comparing it with other conventional solvents, such as water, methanol, and ethanol. From the analysis of gyration radius, our results demonstrate that PVB20 adopts a folding and unfolding conformation in methylal and other solvents, respectively. We reveal through the analysis of the radial distribution function and structural factor that the strength of intermolecular interaction between the PVB and the solvent increases as the molecular size of the solvent molecule decreases, thereby showing the lowest intermolecular interaction in methylal, and moreover, hydrogen bonding between the solvent molecules is absent in methylal, which is in contrast to water, methanol, and ethanol solvents. Our calculations of solvation free energy and mixing energy confirm that methylal has superior solubility for the PVB polymer to other solvents, highlighting the significance of methylal in dissolving polymer.

Predictive multiplicity and chaotic dynamics represent two fundamental challenges in machine learning that have evolved independently despite their conceptual connections. We bridge this gap by introducing horizon-constrained Rashomon sets, a theoretical framework that characterizes how model multiplicity evolves with prediction horizon in chaotic systems. Unlike static prediction tasks where the Rashomon set remains fixed, chaos induces exponential divergence among initially similar models, fundamentally transforming the nature of predictive equivalence. We prove that the effective Rashomon set contracts exponentially with lead time at a rate determined by the maximum Lyapunov exponent and introduce Lyapunov-weighted metrics that provide tighter bounds on predictive disagreement. Leveraging these insights, we develop decision-aligned selection algorithms that choose among near-optimal models based on downstream utility rather than forecast accuracy alone. Extensive experiments on synthetic chaotic systems (Lorenz-96 and Kuramoto–Sivashinsky) and real-world applications (wind power, traffic, and weather) demonstrate that our framework improves decision quality by 18%–34% while maintaining competitive predictive performance. This work establishes the first rigorous connection between chaos theory and predictive multiplicity, providing principled guidance for deploying machine learning in safety-critical chaotic domains.

This paper proposes a method for enhancing antenna gain based on a zero-refractive-index metasurface, which can improve the gain of antennas under bending conditions and mitigate the gain reduction caused by deformation in flexible antennas. The metasurface enhances gain by suppressing magnetic field changes caused by bending. The key novelty is to leverage the dual-band near-zero refractive index behavior to actively regulate the wave vector so that electromagnetic waves passing through the metasurface tend to be redirected toward the surface normal even under conformal bending or oblique incidence. A flexible dual-band antenna was designed to verify the effect of the metasurface on improving antenna gain. The proposed antenna operates in the frequency ranges of 2.43–2.47 and 5.72–5.9 GHz. The effect of the metasurface is analyzed through the fabrication and measurement of the antenna. This study reveals that when the antenna is flat, its gain at 2.45 and 5.75 GHz is 7.53 and 9.32 dBi, respectively. In bending conditions, the metasurface significantly enhances the antenna’s gain, with greater bending leading to more pronounced improvements. The maximum increase in the gain reaches 1.69 and 2.74 dBi, respectively. When the bending radius is reduced to 40 mm, the received power increases from 68 and 13.7 to 89.7 and 21.3 mW, respectively. The received power of the antenna increases by 31% and 55%, respectively, under bending conditions. The experimental results indicate that, after bending, the antenna with the metasurface can receive more power, consistent with the improved gain results.

Given the current situation, humans must mitigate greenhouse gas emissions from the use of fossil fuels and adopt alternative energy sources. These sources are intermittent and depend on factors such as geographical position and time of year. Therefore, the efficient use of the energy harvested by their respective devices enables us to determine whether they are viable alternatives and to what extent they can improve environmental conditions. This paper presents a comparative and qualitative energetic analysis using the substance-field tool, part of ARIZ. The analysis qualitatively evaluates different solar receivers to understand how energy flows may interact between their components. The receivers selected for the analysis are experimental equipment for concentrated solar energy applications. By studying the expected interactions between their components and redesigning these systems to channel useful energy and minimize losses, two innovative receivers were developed: the first one was capable of heat treatment and surface processing, and the second one was capable of additive manufacturing printed circuit boards. These systems were protected by utility model patents. It was concluded that this type of analysis enables the design of creative concepts that effectively harness energy from a specific energetic field. By using this tool, we can qualitatively analyze the expected heat flows within systems and between their components, thereby enabling us to propose alternatives to redirect these flows most effectively. The results are achieved by maximizing the efficient use of harvested energy, thereby enhancing environmental conditions.

Accurate estimation of battery state of charge (SOC) and state of health (SOH) is essential for the safe control, effective scheduling, and reliable operation of electric vehicles and energy storage systems based on batteries. Although SOC and SOH are physically interdependent, they are still treated as separate estimation tasks in many existing studies, which limits prediction consistency and weakens the use of degradation information during charge-state estimation. To address this limitation, a Cross Attention-Based Multitask Transformer for Battery Health Prediction (CA-MT-BHP) is presented, meant for joint SOC and SOH valuation. The novelty of the suggested framework lies in three aspects: first, task-specific Transformer pathways are used to learn short-term electrical dynamics for SOC and long-term degradation behavior for SOH; second, a cross-attention mechanism is introduced to explicitly align SOC-specific and SOH-specific feature representations; and third, interpretability is incorporated through Local Interpretable Model-Agnostic Explanations to support transparent battery-state prediction. The model uses voltage, current, power, time, and battery temperature for SOC learning, while cumulative variables such as ampere-hour, watt-hour, chamber temperature, battery temperature, and time are used for SOH learning. The experimental results conducted on the Panasonic and NASA battery datasets show excellent predictive performance, with a testing mean absolute error (MAE) of 0.010% and testing root mean square error (RMSE) of 0.014% for SOC prediction, and a testing MAE of 0.008% and RMSE of 0.010% for SOH prediction, where R2 = 99.5% and R2 = 99.6%, respectively. The comparative and ablation analyses further demonstrate that explicit SOC–SOH feature alignment not only enhances robustness but also boosts the stability of the estimation. These outcomes validate the potential of the CA-MT-BHP framework to serve as an accurate, scalable, and interpretable methodology for more advanced battery management systems.

Occurrence condition for non-spherical shape mode of acoustically oscillated lipidic-coated bubble was numerically investigated by solving both the Keller–Miksis equation and dynamic equation for distortion amplitude of spherical harmonics. To simply model the lipidic-coated bubble, apparent surface tension is introduced into the numerical model. The numerical simulation indicated that the growth rate of distortion amplitude in an ultrasonic cycle is an important factor to predict the shape mode of a non-spherically oscillating bubble under MHz ultrasound. According to the occurrence condition of non-spherical oscillation modes derived from the growth of distortion amplitude, larger degree of spherical harmonics is dominant with increasing the equilibrium bubble radius, and the degree is not significantly impacted by pressure amplitude. When the apparent surface tension is reduced to model lipidic-coated bubbles, shape instability occurs in smaller bubbles, yielding numerical results that are consistent with experimental observations conducted previously by other researchers.

We present a micro-structured Golay-cell THz array detector with the sensitive mechanism from the THz optical radiation to thermal absorption, to pressure deformation, and then to optical phase, realizing highly sensitive, room temperature operation. The device features novel micro gas-cavity architecture with a carbon nanotube absorption layer and a flexible reflective membrane, which transduces absorbed THz radiation into a measurable optical phase signal (caught by a Hartmann wavefront sensor) via membrane deformation. Multiphysics simulations were carried out to optimize key parameters of the cavity size, membrane thickness, and cavity height, and an optimal geometry was revealed with maximum displacement responsivity and moderate response speed. Based on simulations, a prototype array detector was fabricated and verified its THz response. The uncooled detector exhibits high displacement responsivity of around 6.93 mm/W at a frequency of 0.1 THz and realizes a clear spatially resolved response. The observed membrane deformation agrees with simulation predictions and scales linearly with incident THz power. Pixel response uniformity is up to an average value of around 83%, literally demonstrating its viability for THz imaging. Our work breaks the strict cooling requirements and the dilemma of only being able to achieve single point detection and offers a promising path toward array THz imaging.

Phase engineering of two-dimensional transition metal dichalcogenides enables precise modulation of their electronic properties and holds great promise for next-generation electronic devices. However, achieving spatially controlled and reversible phase transitions reliably remains a key challenge for practical applications. Here, we demonstrate a localized and controllable phase transition from the semiconducting 2H to the metallic 1T′ phase in MoTe2 via laser irradiation. This transition, confirmed by Raman spectroscopy and atomic force microscopy, leads to a remarkable increase in conductivity, with the current response enhanced by up to three orders of magnitude. Furthermore, we reveal that the laser-induced 1T′ phase exhibits spontaneous reversion to the original 2H phase over time when stored under vacuum, accompanied by a corresponding decay in electrical conductance. This reversible and tunable phase-switching behavior highlights the potential of laser-controlled phase patterning for applications in reconfigurable electronics, particularly in memristive devices. Our work not only establishes a method for spatially defined phase engineering but also provides critical insights into the dynamics of phase stability, offering a foundation for the design of future controllable phase-change systems.

This study focuses on structural biomimetics, using bamboo as the biomimetic object. Owing to the porous and lightweight characteristics of bamboo, its macroscopic structure was simplified and simulated to design a pipeline. The changes in pore structure and quantity gradient (number ratio of inner-to-outer pores) parameters were imported into ABAQUS for simulation. The photopolymerization three-dimensional printing technology was used to prepare test specimens for investigating the compression performance of bamboo-like structural pipelines. Under the same pore structure, the simulation and experimental results were consistent. The pipeline structure at a quantity gradient ratio of 11:17 exhibited the most stable compression performance among different quantity gradients. Under the same quantity gradient, the optimal comprehensive compression performance and lightest material structure were achieved when the pore structure became circular. The change in the quantity gradient significantly impacted the compression performance of the pipeline. Therefore, the quantity gradient was the main factor affecting the compression performance of the pipeline.

Deep Reinforcement Learning (DRL) is a powerful paradigm for discovering non-linear control strategies in complex fluid dynamics. However, its application to high-fidelity simulations is often hampered by prohibitive sample inefficiency and the risks of “cold-start” exploration. To overcome these limitations, an Expert-Guided Soft Actor-Critic (EG-SAC) framework is proposed, which synergistically integrates prior knowledge from a classical, particle swarm optimization-optimized proportional-integral-derivative controller into the DRL agent. The framework employs a two-stage learning process, beginning with an offline phase where behavioral cloning initializes the policy and expert demonstrations pre-fill the replay buffer and an online fine-tuning stage where a composite loss function, featuring a decaying expert-regularization term, provides continuous guidance to the agent. This framework is applied to active flow control for a circular cylinder at a Reynolds number (Re) of 100. The results demonstrate a threefold advantage: first, the EG-SAC agent achieves immediate operational safety, exhibiting near-expert performance from the first epoch, while the standard SAC agent initially suffers from detrimental, high-drag explorations; second, the sample efficiency is significantly enhanced, with asymptotic convergence accelerated by ∼19%; finally, the converged EG-SAC policy achieves a drag reduction of 7.93%, surpassing the 5.21% achieved by the best linear controller and demonstrating the ability to discover superior non-linear control laws. The superior performance stems from an enhanced base pressure recovery via a further elongated recirculation bubble. This work presents a robust and efficient methodology for applying DRL to fluid mechanics, bridging the gap between the stability of classical control and the high-performance adaptability of data-driven methods.

Laser-induced breakdown spectroscopy (LIBS) has emerged as a key technique for standoff quantitative analysis in industrial intelligent detection, enabling component measurement under hazardous conditions. However, remote LIBS applications, particularly for trace elements in materials like steel, face significant challenges due to signal attenuation and low signal-to-noise ratio at distances of several meters. This study addresses these issues by developing a remote LIBS system with a 10 m measurement distance and proposing a novel transformer-CNN hybrid method for quantitative analysis of trace elements. Using 40 standard steel samples, a dataset of 2400 spectral data points was constructed. Spectral preprocessing was optimized through wavelet transform, with Coif5 identified as the most effective function for enhancing signal characteristics. The TransCNN-LIBS model integrates transformer architectures to capture global spectral dependencies and CNN layers for local feature extraction, achieving high accuracy quantitative analysis for four trace elements in steel, which are phosphorus (P), sulfur (S), copper (Cu), and molybdenum (Mo). Comparative evaluations demonstrate superiority over conventional methods, including PLS, BPNN, and standalone CNN or transformer models. The root mean square error and mean relative error of the proposed method outperform those in the experiments. This study provides a reliable framework for accurate, non-contact elemental analysis, advancing the remote LIBS into industrial automation processes.

Wood surface defect detection is plagued by challenges in multi-scale feature fusion, small object recognition, and other aspects. To address these issues, this paper proposes SGCDT-YOLO, an improved YOLOv11n algorithm integrated with the Selective Weighted Spatial Reduction (SWSR), Guided Expansion and Dimensional-Oriented Selective Fusion Enhancement Network (GEDOSFEN), Dual-Stage Gradient Receptive Field Aggregation (DSGRFA), and triplet attention modules. A dual-branch SWSR module is designed to enable content-aware downsampling, preserving discriminative information in critical defect regions while suppressing irrelevant areas. GEDOSFEN is proposed to build bidirectional diffusion paths via intermediate-scale features, facilitating effective interaction between deep semantic information and shallow texture details across detection scales. Inspired by dilated-wise residual and dilated re-param block, the DSGRFA module is designed to decompose receptive field expansion into regional feature construction and semantic filtering, extending the perceptual range with low computational overhead. Triplet attention is incorporated to establish cross-dimensional correlations among channel, height, and width, enhancing the unified perception of multi-morphology defects. Experimental results show that SGCDT-YOLO achieves 78.12% mAP@0.5 and 40.42% mAP@0.5:0.95, representing improvements of 13.41% and 5.43% over the baseline, respectively, with precision at 76.62% (+10.25%), recall at 68.52% (+6.76%), and F1-score at 72.34% (+8.36%).Meanwhile, the number of parameters is reduced by 10.47% compared with the baseline model. Comparative tests reveal a 12.22–16.02% mAP@0.5 improvement over mainstream algorithms. Visualization analyses of inference results, heatmaps, and feature maps confirm the algorithm’s high accuracy in defect localization and classification.

This study investigates the orbital stability and chaotic dynamics of a diffusive prey–predator ecological model. The stability properties of the system are analyzed through Jacobian-based linear stability analysis at the equilibrium points by evaluating the corresponding eigenvalues. This analysis reveals different dynamical regimes, including centers, saddles, and spiral behaviors, each exhibiting distinct stability properties. Phase portraits are employed to illustrate the qualitative structure of the system trajectories and to visualize the orbital stability of the ecological interactions. To assess the sensitivity of the system to initial conditions, numerical simulations are performed using the fourth-order Runge–Kutta method. The results show that small perturbations in initial conditions can produce significantly divergent trajectories, indicating the presence of chaotic dynamics. Furthermore, specific parameter values responsible for the onset of chaos are identified through bifurcation diagrams, Lyapunov exponent analysis, two-dimensional phase portraits, and time-series representations. The occurrence of positive Lyapunov exponents provides strong evidence of chaotic behavior and confirms the effectiveness of this approach in characterizing complex ecological dynamics. The bifurcation and Lyapunov analyses further demonstrate that the system undergoes transitions among stable, periodic, and chaotic regimes as key parameters vary. The emergence of irregular time series and disordered phase portraits further supports the existence of chaos in the system. This study enhances the understanding of diffusive prey–predator interactions and demonstrates the effectiveness of various tools in uncovering chaotic ecological dynamics.

The aerodynamic optimization of cars requires close collaboration between aerodynamicists and stylists, while slow, expensive simulations remain a bottleneck. Surrogate models have been shown to accurately predict aerodynamics within the design space for which they were trained. However, many of these models struggle to scale to higher resolutions because of the three-dimensional nature of the problem and data scarcity. We propose progressive multi-resolution training (PMRT), a probabilistic multi-resolution training schedule that enables training a U-Net to predict the drag coefficient (cd) and high-resolution velocity fields (512 × 128 × 128) in 24 h on a single NVIDIA H100 graphics processing unit, 7× cheaper than the high-resolution-only baseline, with similar accuracy. PMRT samples batches from three resolutions based on probabilities that change during training, starting with an emphasis on lower resolutions and gradually shifting toward higher resolutions. Since this is a training methodology, it can be adapted to other high-resolution-focused backbones. We also show that a single model can be trained across five datasets from different solvers, including a real-world dataset, by conditioning on the simulation parameters. We train both cd-only and cd + velocity models to cover applications where flow fields are either unavailable or unnecessary. In the DrivAerML dataset, our models achieve a cdR2 value of ≈0.974, matching literature baselines at a fraction of the training cost. The reduced training time keeps the time-to-first prediction on new data short, which is essential for real-world iterative vehicle development.

To address the challenge of predicting mass erosion during the penetration of kinetic projectiles into reinforced concrete, this paper proposes a coupled calculation method. This method is based on existing mass erosion theories regarding thermal melting stripping and thermal softening cutting, while simultaneously considering the shear-plastic hinge resistance encountered by the projectile during direct contact with the reinforcement. Solved via multi-scale discretization, this method discretizes the entire penetration process into microsecond-level steps (10−6 s) on a temporal scale and employs micro-scale grid division on the projectile surface layer on a spatial scale. The calculated results show good agreement with experimental data, with a deviation of 2.74% between the predicted and experimental penetration depths for reinforced concrete. The study finds that as the initial penetration velocity increases, the thermal melting mechanism dominates mass loss, although the proportion of mass loss induced by the cutting mechanism exhibits an increasing trend. The presence of reinforcement mitigates the total mass loss of the projectile during the penetration process. Furthermore, the proportions of cutting mass loss, melting mass loss, and total mass loss all demonstrate a decreasing trend as the projectile mass increases. Further analysis reveals the existence of critical thresholds for the projectile’s initial velocity and concrete strength concerning reinforcement protective efficacy; when both exceed these thresholds, the reinforcement’s contribution to the concrete’s anti-penetration protection becomes negligible. The coupled calculation method presented in this paper provides a design basis for optimizing reinforcement and enhancing cost-effectiveness.

Achieving both high sensitivity and a wide operating frequency band is a challenge for acoustic emission (AE) sensors. This paper demonstrates the design of a dual-resonance AE sensor based on a piezoelectric lead zirconate titanate (PZT) disk-ring nested configuration. Firstly, the effects of the size parameters of the individual PZT disk and ring on the impedance spectra are investigated. The simulation results suggest that the radius of the disk and the width of the ring play more important roles in determining the resonance frequency. The bandwidth can be effectively extended by coupling the resonance peaks of the disk and the ring in the nested configuration. Moreover, the sensitivity is further optimized to a peak value of 99.5 dB over a wide bandwidth of 181–1000 kHz. The disk-ring nested configuration provides a solution for designing high-performance AE sensors.

Evaporation plays a critical role in ecological and environmental processes, yet computational investigations have thus far been limited by the lack of water models with quantum-mechanical accuracy that are also computationally efficient. To address this challenge, we employ the recently developed neuroevolution potential (NEP), which is trained on extensive many-body polarization (MB-pol) reference data and achieves a favorable balance between accuracy and efficiency. Using NEP-MB-pol in molecular dynamics simulations, we perform a systematic study of water evaporation. We first establish the vapor–liquid equilibrium, finding that the liquid and vapor densities at different temperatures, as well as the fitted critical points, are in excellent agreement with reference values, underscoring the predictive capability of the employed model. We then revisit the microscopic mechanism of evaporation. Our MD simulations show that an evaporating molecule must remain in a highly energetic pre-evaporation state for several 100 fs. A successful evaporation involves the cooperative interactions of at least four water molecules, with the last collision occurring within a short time window of ∼56 fs before evaporation. Finally, motivated by recent intriguing experimental observations, such as the photomolecular effect, we investigated the impact of external electric fields on water evaporation. In contrast to experimental findings, we did not observe a consistent effect from green light on water evaporation, i.e., the photomolecular effect was not reproduced. This may be attributed to the negligence of quantum effects in our simulation. Overall, our study provides new microscopic insight into the evaporation process and offers valuable guidance for experimental studies and potential industrial applications.

In an electromagnetic resonant cavity containing a magnetic element, the cavity and magnon modes can establish a dissipative-type coupling by their mutual coupling to a third, highly dissipative mode. This dissipative coupling has been predominantly observed in semi-open cavities coupled to traveling waves. Here, we show that contrary to common expectations, even in typical nearly closed cavities, both coherent and dissipative couplings exist and interplay to determine the behaviors of cavity and magnon modes. Furthermore, their different positional dependences allow one to manipulate their interplay by changing the location of the magnon system inside the cavity. This work significantly broadens the scope of systems in which dissipative coupling can be realized and manipulated.

The development of wireless communication has grown interest in the high-performance microwave-absorbing materials. This paper presents the design of a quad-band metamaterial absorber based on a hybrid resonator integrating elliptical and square rings. The unit cell of the absorber consists of a square split-ring resonator, an elliptical SRR, and a square resonant strip, with an FR-4 dielectric substrate and a continuous metallic ground layer at the bottom. Simulation results reveal that this structure achieves near-perfect absorption efficiencies of 99.41%, 99.99%, 99.93%, and 99.25% at the resonant frequencies of 6.41, 11.69, 15.55, and 17.82 GHz, respectively. The analysis of surface current and electric field distributions indicates that each absorption peak arises from distinct mechanisms, including the synergistic interaction between the elliptical ring and the inner resonant strip, as well as the dominant contribution of the external ring resonant structure. Angular response analysis demonstrates that the absorber maintains high absorption efficiency within the oblique incidence range of 0°–45°, exhibiting excellent angular stability. The equivalent circuit of the proposed MMA was designed using ADS software, which demonstrates the designability of the metamaterial absorber. The results were validated by both waveguide and arch reflectivity tests. Compared with previously reported counterparts, the proposed design features a simple configuration, flexible frequency band selection, and high absorption efficiency, thus holding promising application potential in the fields of microwave stealth and electromagnetic protection.