Accepted
, , Received Date: 2025-06-13
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Magnesium and aluminum are abundant metals in the Earth's crust and widely utilized in industrial engineering. Under high pressure, these elements can form elemental compounds into single substances, resulting in a variety of crystal structures and electronic properties. In this study, the possible structures of magnesium-aluminum alloys are systematically investigated in a pressure range of 0–500 GPa by using the first-principles structure search method, with energy and electronic structure calculations conducted using the VASP package. Bader charge analysis elucidates atomic and interstitial quasi-atom (ISQ) valence states, while lattice dynamics are analyzed using the PHONOPY package via the small-displacement supercell approach. Eight stable phases(MgAl3-Pm-3m, MgAl3-P63/mmc, MgAl-P4/mmm, MgAl-Pmmb, MgAl-Fd-3m, Mg2Al-P-3m1, Mg3Al-P63/mmc, Mg3Al-Fm-3m) and two metastable phases (Mg4Al-I4/m, Mg5Al-P-3m1) are identified. The critical pressures and stable intervals for phase transitions are precisely determined. Notably, MgAl-Fd-3m, Mg2Al-P-3m1, Mg4Al-I4/m and Mg5Al-P-3m1 represent newly predicted structures. Analysis of electronic localization characteristics reveals that six stable structures(MgAl3-Pm-3m, MgAl3-P63/mmc, MgAl-Pmmb, MgAl-Fd-3m, Mg2Al-P-3m1 and Mg3Al-P63/mmc) exhibit electronic properties of electrides. The ISQs primarily originate from charge transfer of Mg atoms. In the metastable phase Mg4Al-I4/m, Al atoms are predicted to achieve an Al5–valence state, filling the p shell. This finding demonstrates that by adjusting the Mg/Al ratio and pressure conditions, a transition from traditional electrides to high negative valence states can be realized, offering new insights into the development of novel high-pressure functional materials. Furthermore, all Mg-Al compounds display metallic behaviors, with their stability attributed to Al-p-d orbital hybridization, which significantly contributes to the Al-3p/3d orbitals near the Fermi level. Additionally, LA-TA splitting is observed in MgAl3-Pm-3m, with a splitting value of 45.49 cm–1, confirming the unique regulatory effect of ISQs on lattice vibrational properties. These results elucidate the rich structural and electronic properties of magnesium-aluminum alloys as electrodes, offering deeper insights into their behavior under high pressure and inspiring further exploration of structural and property changes in high-pressure alloys composed of light metal elements and p-electron metals.
, , Received Date: 2025-05-27
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Terahertz waves have broad application prospects in fields such as food quality, biomedicine, and security communication. However, the dispersion and loss during transmission limit the development of terahertz systems. This study focuses on the dispersion characteristics of microstrip lines in the terahertz low-frequency range. By combining theoretical modeling, numerical simulation, and experimental verification, the dispersion mechanism and key influencing factors of microstrip lines are systematically analyzed, providing theoretical support for low dispersion, high-performance terahertz integrated circuits and systems. This study is based on electromagnetic field theory, dividing microstrip line dispersion into dielectric dispersion, geometric dispersion, and conductor dispersion, and introducing a modified model to overcome the limitations of traditional quasi-static theory in the high frequency range. In this study, the CST time-domain finite difference simulation and terahertz time-domain pulse reflection (TDR) technology are employed to conduct multidimensional simulation and examine three different dielectric constant substrates (2.2, 3, 4.5), wire widths (100–1600 μm), lengths (10–150 mm) and other parameters. The pulse broadening coefficient is introduced to quantitatively evaluate the dispersion characteristics of microstrip lines. The results indicate that the increase in substrate dielectric constant significantly enhances the dispersion effect. When εr increases from 2.2 to 4.5, the increase in equivalent dielectric constant leads to a decrease in pulse transmission speed; When the wire width increases from 100 μm to 1600 μm, the pulse broadening coefficient dominated by geometric dispersion increases from 3.12 to 5.12, with an increase of 38%. However, when the wire length increases from 10 mm to 150 mm, the cumulative dispersion increases the broadening coefficient from 2.12 to 3.18, with an increase of 33%, verifying the sensitivity of width to dispersion control. The simulation result once again shows that due to the small skin depth of terahertz waves on metal surfaces, the difference in conductivity among the three conductor materials of gold, silver, and copper (4.1×107–6.3×107 S/m) can be ignored in terms of dispersion effect. According to the actual measurement and fitting results, the geometric dispersion of microstrip lines is more significant than the dispersion loss caused by length accumulation. In addition, simulation, experimental testing, and theoretical analysis are all in good consistency with each other. The conclusion indicates that optimizing the design of microstrip lines requires priority control of the dielectric constant and wire width of substrate material to suppress the synergistic effect of geometric dispersion and dielectric dispersion, providing quantifiable design criteria for high bandwidth and low distortion transmission in terahertz communication systems, and laying experimental and theoretical foundations for the engineering application of terahertz integrated circuits.
, , Received Date: 2025-07-23
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In neutral beam injection (NBI), which is a primary auxiliary heating method for tokamak plasmas, the negative hydrogen ion source (NHIS) functions as a critical front-end component governing neutral beam quality. The performance of NHIS remains a key challenge. This work presents a three-dimensional (3D) fluid model, which is developed for a double-driver NHIS to simulate and optimize surface-generated negative hydrogen ion density. A comparison of plasma parameters between the NHIS with Cs and without Cs shows that surface generation yields negative ion density one order of magnitude higher than volume generation. However, the presence of the magnetic filter field induces asymmetry in negative ion density within the extraction region. To improve this asymmetry, two approaches are proposed: (1) increasing the power of one of the drivers and (2) adding a spacer plate to the expansion region. After increasing the power of Driver I from 50 to 56 kW, the H– density asymmetry at the y = 25 cm intercept on the xy-plane (z = –22 cm) decreases from 0.04 to 0.01, and the value of H– density increases. Following the addition of a spacer plate, the H– density asymmetry further decreases to 0.004, but the value of H- density also shows a significant reduction. Finally, adding a magnetic shield to the back plate of the expansion region further optimizes H– density from 1.48×1017 m–3 to 2.50×1017 m–3, yielding a 69% increase downstream. This is because increased plasma transport into the expansion region enhances the dissociation rate of H2 molecules, thereby yielding more H atoms. The attenuation of the magnetic filter field in the driver region after adding a magnetic shield also enhances the symmetry of the H– density.
, , Received Date: 2025-08-31
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Carbon quantum dots, as an emerging zero-dimensional carbon-based nanomaterial, have shown great potential applications in fields such as biomedicine, sensing detection, and LED lighting due to their excellent photoelectric properties, good biocompatibility, and ease of functionalization. Traditional synthesis methods like hydrothermal and microwave approaches often face challenges such as harsh reaction conditions, long reaction times, high energy consumption, and difficulties in controlling the optical properties of the products. The plasma electrochemistry method, which utilizes reactions between carbon source molecules and high-density active electrons, ions, and reactive species generated during the interaction of plasma with liquid, can efficiently drive the rapid synthesis and modification of carbon quantum dots. This method possesses the advantage of tunable multiple reaction parameters under mild conditions, providing a novel research method for synthesizing and modifying carbon quantum dots. This article first elucidates the growth mechanism of carbon quantum dots synthesized via plasma electrochemical methods and highlights the unique advantages of this approach in controlling product properties by regulating multidimensional parameters. Then, it reviews research progress of the regulation of the fluorescence quantum yield and wavelength of carbon quantum dots based on the adjustment of plasma reaction parameters. Finally, this article presents the application progress and prospects of plasma-prepared and plasma-modified carbon quantum dots in biomedicine, optoelectronic devices, pH sensing, and other fields.
, , Received Date: 2025-07-21
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, , Received Date: 2025-06-21
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Quasi-particle excitation in a Bose-Einstein condensate leads to quantum entanglement between real bosonic atoms in the system. By using spectral expansion method, the eigenvalues and eigenstates of Bogoliubov-de Gennes equation are numerically calculated in a quasi-one-dimensional infinite square well potential. For the low-energy collective excitations of the quasi-particles, we explore the dependence of quantum entanglement entropy of the Bose-Einstein condensate on scattering length. Our results show that the entanglement entropy increases slowly with the increase of the scattering length, and such an increasing trend can be well described by a power function. These results are analogous to those in a one-dimensional uniform BEC, where the entanglement entropy of the Bogoliubov ground state is approximately proportional to the square root of the scattering length. This work provides a viable way for investigating many-particle entanglement in a quasi-one-dimensional trapped Bose-Einstein condensate where the quantum entanglement is closely related to the interaction strength between particles.
, , Received Date: 2025-07-28
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Nonequilibrium heat transport and quantum thermodynamics in quantum light-matter interacting systems have received increasing attention recently. Consequently, quantum thermal devices, such as heat valve and head diode, have been realized. Recently, it has been discovered that the anisotropic light-matter interactions can greatly modify the eigenvalues and corresponding eigenvectors of hybrid quantum systems, leading to nontrivial quantum phase transitions, quantum metrology, and nonclassicality of photons. To explore the influences of anisotropic light-matter interactions on quantum transport, we investigate quantum heat flow in the nonequilibrium anisotropic Dicke model. In this model, an ensemble of qubits collectively interacts with an anisotropic photon field, moreover, each component interacts with bosonic thermal reservoirs. The quantum dressed master equation (DME) is included to properly study dissipative dynamics of the anisotropic Dicke model. Within the eigenbasis of the reduced anisotropic Dicke system, the strong qubit-photon couplings can be handled. Our results demonstrate that anisotropic qubit-photon interactions are crucial for modulating steady-state heat flow. In particular, it is found that under strong coupling the heat flow is dramatically suppressed by a large anisotropic qubit-photon factor. While under moderate coupling, the anisotropic qubit-photon interactions enhance the heat flow. Moreover, the increase in the number of qubits amplifies the flow characteristics, with the peaks increasing and the valleys decreasing. Besides, we derive two analytical expressions of heat flows in the thermodynamic limit approximation with limiting anisotropic factors. These heat currents exhibit the cotunneling heat transport pictures. They also serve as the upper boundaries for the heat flows in the anisotropic Dicke model with finite qubit numbers. We also analyze the thermal rectification effect in the anisotropic Dicke model. It is found that a large temperature bias, a large anisotropic qubit-photon factor, and nonweak qubit-photon coupling are helpful in achieving the giant thermal rectification factor. We hope that these results can deepen the understanding of nonequilibrium heat transport in the anisotropic quantum light-matter interacting systems.
,
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In recent years, the application of machine learning in materials science has significantly accelerated the discovery of new materials. In particular, when combined with traditional methods such as first-principles calculations, machine learning models have proven effective in screening potential high-performance materials from existing databases. However, these approaches are largely constrained within known chemical spaces and struggle to enable the active design of entirely novel material structures. To overcome this limitation, generative models have emerged as a promising tool for inverse material design, offering new avenues to explore unknown structural and property spaces. Although existing generative models have achieved initial progress in crystal structure generation, achieving property-guided material generation remains a significant challenge. This review first introduces representative generative models recently applied to materials generation, including CDVAE, MatGAN, and MatterGen, and analyzes their fundamental capabilities and limitations in structural generation. We then focus on strategies for incorporating target properties into generative models to achieve property-directed structure generation. Specifically, we discuss four representative approaches: Con-CDVAE based on target property vectors, SCIGEN with integrated structural constraints and guidance mechanisms, a fine-tuned version of MatterGen leveraging adapter-based property control, and a CDVAE latent space optimization strategy guided by property objectives. Finally, we summarize the key challenges faced by property-guided generative models and provide an outlook on future research directions. This review aims to offer researchers a systematic reference and inspiration for advancing property-driven generative approaches in material design. This work aims to provide researchers with a systematic reference and insight into the advancement of property-driven generative methods for materials design.
,
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Utilizing fractional vortex beams (FVBs) as information carriers can significantly enhance the capacity of communication systems. However, the small gap difference among adjacent fractional orbital angular momentum (FOAM) modes makes FVBs highly sensitive to atmospheric turbulence. Therefore, precise measurement of distorted FOAM modes is crucial for practical FVBs-based communication systems. To fully utilize the beam intensity information and the triangular diffraction pattern information, we propose a dual-channel deep learning model with a hybrid architecture combining convolutional neural network (CNN) and Vision Transformer(ViT). The beam intensity information is extracted using CNN, while the diffraction pattern information is extracted using ViT. Then, by fusing the complementary feature information from the intensity distribution of FVBs and their triangular diffraction patterns, this model enables effective identification of FOAM modes. The results show that the proposed model only requires a relatively small number of samples to reach the convergence value, namely 100 sets of data under weak turbulence and 400 sets of data under strong turbulence. Moreover, within a transmission distance of 1000 m, the proposed model can identify 101 FOAM modes with a mode spacing of 0.1 with an accuracy of 100% under weak and moderate turbulences, and maintains 98.12% accuracy under strong turbulence. Furthermore, the model can expand the detection range of turbulence intensity with only a minimal loss in accuracy, exhibiting strong generalization ability under unknown atmospheric turbulence strengths, thus providing a novel approach for accurately identifying FOAM modes.
,
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Memristors exhibit controllable nonlinear characteristics, generating chaotic signals that are characterized by randomness, sensitivity, and unpredictability, thereby demonstrating significant potential applications in information encryption and signal processing. With the integration of chaos theory and electronic technology, constructing memristive hyperchaotic systems has become a hot topic in nonlinear science and information security. This paper aims to overcome the limitation of monotonous dynamic characteristics in traditional chaotic systems by designing novel memristor-based hyperchaotic systems with richer dynamic behavior and higher application value. It conducts characteristic analysis, theoretical verification, application exploration, and hardware implementation to support its engineering applications. Building upon the classical Chen system, this work innovatively incorporates cubic nonlinear magnetically controlled memristor model as a feedback element. By establishing a mathematical model of the memristor and coupling it with the state equations of the Chen system, a four-dimensional memristor-based hyperchaotic system is designed. First, by integrating numerical computation with differential equation theory, a comprehensive mathematical model is established to analyze fundamental properties, such as symmetry and dissipativity, thereby validating the system’s rationality. Second, the system’s dynamical behaviors are analyzed, including attractor phase diagrams, Lyapunov exponents, power spectra, parameter effects, transient dynamics, and coexisting attractors. Simultaneously, variational methods are applied to analyze unstable periodic orbits within the system. A symbolic coding approach based on orbital characteristics is established to convert orbital information into symbolic sequences, and orbital pruning rules are explored to provide a basis for optimal orbital control. Furthermore, a digital image encryption method is proposed based on this system. Using chaotic sequences as keys, image pixels are scrambled and diffused. The effectiveness of encryption is validated through histogram analysis, correlation analysis, information entropy evaluation, and testing of anti-attack capabilities. Finally, a DSP-based digital circuit hardware platform is constructed to run the system, and researchers compare hardware experimental results with software simulation outcomes. Findings reveal that the introduction of memristors induces linearly distributed equilibrium points in phase space, generating hidden attractors that enrich the system’s chaotic behavior. Dynamic behaviors simulation confirm the rich dynamics of this four-dimensional memristorbased hyperchaotic system. The proposed digital image encryption method demonstrates robust security performance. The DSP hardware experiments and software simulations yielded highly consistent attractor phase diagrams, validating the system’s correctness and feasibility.
,
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The viscosity of matter under extreme conditions, i.e., warm dense matter (WDM) and hot dense matter (HDM), has significant applications in various areas, such as the design of inertial confinement fusion targets, the study of astrophysical structure evolution, and the investigation of interfacial instability and mixing development under extreme conditions. Since the temperature and pressure range accessible by experimental techniques for viscosity measurement is very limited, the acquisition of viscosity data under extreme conditions mainly relies on theoretical calculations. This work introduces a variety of molecular dynamics (MD) methods and models for calculating the viscosity of WDM and HDM, including quantum MD (QMD), orbital-free MD (OFMD), average atom model combined with hypernetted chain (AAHNC), effective potential theory combined with average atom model (EPT+AA), hybrid kinetics MD (KMD), integrated Yukawa viscosity model (IYVM), Stanton-Murillo transport model (SMT), pseudoion in jellium (PIJ), one-component plasma model (OCP), and random-walk shielding-potential viscosity model (RWSP-VM). Simultaneously, the viscosity of a variety of elements obtained by these methods are shown, ranging from low to high atomic number (Z), i.e., H, C, Al, Fe, Ge, W, U. The accuracy and the applicability of each method are detailed analyzed by comparison. RWSP-VM, which is based on physical modeling and independent of MD data, has comparable accuracy to simulation data over a wide range of temperature and pressure, and is an effcient method for obtaining viscosity data of WDM and HDM. This work will pave the way to the calculation of shear viscosity at extremes, and may play an important role in promoting the relevant applications. The data calculated from RWSP-VM in this work are openly available at https://www.scidb.cn/s/ZrERJf.
, , Received Date: 2025-07-25
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,
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Two-dimensional (2D) magnetic materials refer to nanomaterials with an extremely thin thickness that can maintain long-range magnetic order. These materials exhibit significant magnetic anisotropy, and due to the quantum confinement effect and high specific surface area, their electronic band structures and surface states undergo remarkable changes. As a result, they possess rich and tunable magnetic properties, showing great application potential in the field of spintronics. 2D magnetic materials include layered materials, where layers are stacked by weak van der Waals forces, and non-layered materials, which are bonded via chemical bonds in all three-dimensional directions. Currently, most research focuses on 2D layered materials, but their Curie temperatures are generally much lower than room temperature, and they are always unstable in air. In contrast, the non-layered structure enhances the structural stability of the materials, and the abundant surface dangling bonds increase the possibility of modifying their physical properties. Such materials are attracting increasing attention, and significant progress has been made in their synthesis and applications. This review first systematically summarizes various preparation methods for 2D non-layered magnetic materials, including but not limited to ultrasound-assisted exfoliation, molecular beam epitaxy, and chemical vapor deposition. Meanwhile, it systematically reviews the 2D non-layered intrinsic magnetic materials obtained in various types of materials in the past five years, as well as a series of novel physical phenomena emerging under the ultrathin limit, such as thickness-dependent magnetic reconstruction dominated by quantum confinement effects and planar topological spin textures induced by 2D structures. Furthermore, it also discusses the critical role played by theoretical calculations in predicting new materials through high-throughput screening, revealing microscopic mechanisms by analyzing magnetic interactions, as well as some important methods for modifying magnetism. Finally, from the perspectives of material preparation, physical mechanisms, device fabrication, and theoretical calculations, the current challenges in the field are summarized, and the application potential and development directions of 2D non-layered magnetic materials in spintronic devices are prospected. This review aims to provide comprehensive references and insights for researchers engaged in this field, fostering further exploration of the novel magnetic properties of 2D non-layered magnetic materials and their applications in spintronic devices.
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Non-Thermal Plasma (NTP), as an advanced technology capable of efficiently synthesizing and modifying materials under near-ambient temperature, has attracted significant attention in the field of energy materials in recent years. Owing to high electron temperature and low bulk gas temperature, NTP can significantly enhance the electrochemical performance of electrode materials by introducing vacancies, enabling heteroatom doping, and regulating multiscale defects such as porosity and surface roughness, while avoiding thermal damage. The plasma-material surface interaction is a complex system involving mutual influences between the plasma and the material. A deep understanding of this mechanism is essential for achieving precise control over defect type, density, and spatial distribution via NTP modification. This review systematically summarizes the applications of NTP in etching and doping processes for energy materials, with a particular emphasis on defect generation and its role in plasma–surface interactions. Finally, the major challenges associated with the large-scale application of NTP technology are discussed, and future perspectives are outlined.
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