Accepted
, , Received Date: 2025-05-27
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The ground-state topological properties of ultracold atoms in composite scalar–Raman optical lattices are systematically investigated by solving the two-component Gross–Pitaevskii equation through the imaginary time evolution method. Our study focuses on the interplay between scalar and Raman optical lattice potentials and the role of interatomic interactions in shaping real-space and momentum-space structures. The competition between lattice depth and interaction strength gives rise to a rich phase diagram of ground-state configurations. In the absence of Raman coupling, atoms in scalar optical lattices exhibit topologically trivial periodic density distributions without forming vortices. When only Raman coupling exists, a regular array of vortices of equal size will appear in one spin component, while the other spin component will remain free of vortices. Strikingly, when scalar and Raman lattices coexist, the system develops complex vortex lattices with alternating large and small vortices of opposite circulation, forming a staggered vortex configuration in real space. In momentum space, the condensate wave function displays nontrivial diffraction peaks carrying a well-defined topological phase structure, whose complexity increases with the depth of the optical potentials increasing. In spin space, we observe the emergence of a lattice of half-quantized skyrmions (half-skyrmions), each carrying a topological charge of ±1/2. These topological textures are confirmed by calculating the spin vector field and integrating the topological charge density. Our results demonstrate how the combination of scalar and Raman optical lattices, together with tunable interactions, can induce nontrivial real-space spin textures and momentum-space topological features. These findings offers new insights into the controllable realization of topological quantum states in cold atom systems.
, , Received Date: 2025-06-21
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, , Received Date: 2025-05-18
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, , Received Date: 2025-04-19
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In recent years, polar magnets M2Mo3O8 (M: 3d transition metal) have emerged as a research focus in condensed matter physics and materials science due to their unique crystal structures, multiple continuous magnetoelectric-coupled state transitions, and potential applications. Notably, Co2Mo3O8 exhibits a significant second-order nonlinear magnetoelectric coupling effect in its ground state, corresponding to a unique microscopic magnetoelectric coupling mechanism and practical value. In this work, based on a molecular field phenomenological model, two distinct antiferromagnetic sublattices for the Co2Mo3O8 system constructed and the temperature-dependent variations of its spontaneous magnetic moment, spin-induced ferroelectric polarization, first-order linear magnetoelectric coupling coefficient, and second-order nonlinear magnetoelectric coupling coefficient are presented. Particularly, the parameters t = –1 and o = –0.96 indicate distinct exchange energies between the magnetic sublattices associated with tetrahedron (Cot) and octahedron (Coo). The Co2+ ions in these two sublattices, which are characterized by different molecular field coefficients, synergistically mediate a spin-induced spontaneous polarization of PS~0.12 μC/cm2 through the exchange striction mechanism and p-d hybridization mechanism in Co2Mo3O8. In addition, the significant second-order magnetoelectric coupling effect with a coefficient peaking at 70 × 10–19 s/A near the TN in Co2Mo3O8, with this coefficient being significantly larger than those of isostructural Fe2Mo3O8 (1.81 × 10–28 s/A) and Mn2Mo3O8, implies that this enhancement primarily arises from the weaker inter-sublattice antiferromagnetic exchange between its two sublattices, leading to a stabilizes metastable spin configuration. This also indicates that the Co2Mo3O8 system possesses stronger irreversibility stability and exhibits a pronounced magnetoelectric diode effect, providing a solid theoretical and computational foundation for developing magnetoelectric diodes.
, , Received Date: 2025-06-27
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Magnesium-ion batteries (MIBs) are regarded as a promising alternative to lithium-ion batteries (LIBs) due to their material abundance, cost-effectiveness, and improved safety. The development of high-performance anode materials is crucial for the advancement of MIBs. In this work, the feasibility of boron-doped graphene/blue phosphorene heterojunctions BiGr/BP (i = 0, 1, 2, 3, 4) as potential anode materials for MIBs is systematically investigated using the density functional theory. Our results show that the average binding energies of BiGr/BP (i = 0, 1, 2, 3, 4) are negative, suggesting their suitability for experimental synthesis. The analyses of band structure and density of states reveal that BiGr/BP (i = 0, 1, 2, 3, 4) exhibit high conductivity, as the 2p orbitals of carbon and boron dominantly contribute to the density of states at the Fermi level. Magnesium (Mg) adsorption capacity rises with the increase of boron doping concentrations, indicating stronger interactions between the heterojunctions and Mg. At the highest doping concentration (i = 4), the adsorption energy of Mg adsorbed in the interlayer is –3.38 eV, demonstrating substantial potential for Mg storage. The ab initio molecular dynamics (AIMD) simulations at 300 K show minor fluctuations in total energy, confirming the thermal stability of B4Gr/BP. Climbing image nudged elastic band (CI-NEB) method is used to determine two diffusion pathways of Mg in the B4Gr/BP interlayer. Along Path II, the maximum diffusion barrier is 0.47 eV, suggesting rapid Mg diffusion in the B4Gr/BP interlayer. The average open-circuit voltage is 0.37 V, ensuring the safety of the charge-discharge process. The theoretical capacity is 286.04 mAh/g, which is twice that of the B4Gr/MoS2 system. In summary, boron doping significantly enhances the Mg storage capacity. Specifically, B4Gr/BP appears to be a promising candidate for high-performance anodes in MIBs, owing to its excellent stability, conductivity, Mg storage capacity, and electrochemical properties.
, , Received Date: 2025-06-25
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, , Received Date: 2025-07-05
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In general cases of strong field excitation, the Stark effect has a significant influence on transient two-photon transitions, and the analytic description of this process is quite challenging. By combining analytical solutions and numerical simulations, the transient two-photon transition processes excited by weak and strong chirped pulses are systematically investigated, showing the important influences of parameters such as light field intensity, chirp factor, and detuning on the time-domain evolution of two-photon transition probabilities. Firstly, an approximate analytical expression is derived for the amplitude of the time-domain two-photon transition probability by using the second-order perturbation theory. This analytical solution indicates that the transient two-photon transition process under weak field excitation is similar to the Fresnel rectangular edge diffraction effect. As the light field intensity increases, the influence of the Stark effect on two-photon transitions also intensifies. Secondly, through a series of approximations, the approximate analytical solutions of the Schrödinger equation under strong field interactions are obtained. The analytical solutions show that the strong field Stark effect induces energy level to split, which disrupts the symmetry of the time-domain two-photon transition probability distribution, and its frequency domain process is similar to the “double-slit interference” effect. The research results indicate that the efficiency of population transfer during strong field excitation is closely related to the light field intensity, while the chirp factor can not only regulate the efficiency and time position of population transfer but also change the oscillation frequency of the population probability in the time domain. This work offers new insights into describing the time-domain evolution of the population probability under strong field excitation and lays a scientific basis for research on two-photon microscopy imaging.
Sandwich-type flexural vibration ultrasonic transducer based on the structure of acoustic black hole
, , Received Date: 2025-06-13
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Based on the advantages of the acoustic black hole (ABH) structure in energy focusing and displacement amplification during the regulation of flexural waves, a new type of ABH sandwich-shaped flexural vibration transducer is proposed in this work. This transducer consists of a sandwich-shaped flexural vibration transducer and an ABH probe. Based on the Timoshenko beam theory, the theoretical model of the overall flexural vibration of the transducer is established by the transfer matrix method, and the calculated results are consistent with the finite element simulation results. The impedance frequency response characteristics, vibration modes, radiation acoustic field and vibration displacement of this transducer are discussed by the finite element method, and a comparative analysis is conducted with the catenary-shaped transducer. The results show that the maximum sound pressure and vibration displacement of the ABH transducer under the same mode are greater than those of the catenary-shaped transducer, indicating that the ABH structure can efficiently enhance the displacement of flexural vibration and the radiation performance of the transducer, and is expected to be utilized as a small-scale acoustic chemical reactor. Finally, a prototype of this transducer is fabricated, then its impedance characteristics and vibration modes are experimentally measured. The experimental results are in agreement with the simulation results.
, , Received Date: 2025-05-15
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In this paper, a design method is presented for frequency-phase composite reconfigurable metasurfaces. N PIN diodes are introduced into the metasurface unit. The on-off states of these PIN diodes regulate the resonance characteristics of the unit, constructing 2N switchable reflection phase states. After optimizing structural parameters, these reflection phase curves show that there is a 180° phase difference between different frequency bands. By regulating frequency and phase regulation, the operational bandwidth of reconfigurable phase-shifting metasurface is effectively expanded. Based on this method, an ultra-wideband 1-bit phase-shifting metasurface unit is designed. Its 1-bit phase regulation band covers 5.4—13.0 GHz, with a relative bandwidth of 82.6%. Lumped capacitors are adopted and their positions are optimized to precisely adjust current distribution, enabling low-loss performance of the unit. The unit with a thickness of only 0.09 λ features low profile, low cost, and low loss. A 16 × 16 unit array is further constructed. Through coding regulation, the metasurface can generate scattering-controllable beams and orbital angular momentum vortex waves. Experimental results show that the metasurface can achieve a radar cross section reduction of over 10 dB in the ultra-wideband range, demonstrating dynamic beam steering capability and high-efficiency low-scattering performance. This design offers new insights into applying reconfigurable metasurfaces to broadband communication, radar stealth, and intelligent electromagnetic environment regulation.
, , Received Date: 2025-05-26
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A liquid-electrode discharge system excited by an alternating-current sinusoidal voltage is employed to investigate the discharge modes with varying liquid conductivity (σ). The results indicate that with σ increasing, the discharge transitions from the uniform mode to the pattern mode, which undergoes various self-organized patterns such as gear, circular saw, discrete spots, single-arm spiral, and concentric rings on the liquid surface. The voltage and current waveforms reveal that the discharge occurs only in the negative half-cycle of applied voltage (when the liquid acts as the instantaneous anode). After gas breakdown, the discharge current rises rapidly to a peak, and then slowly decreases. For the uniform mode, the current decreases monotonically. However, during the current decreasing in the pattern mode, there appears a plateau in which the current keeps almost invariant with time. As σ increases, the values of the peak current and the plateau increase, and the breakdown moment advances. In addition, fast photographyachieved through an intensified charge-coupled device (ICCD) shows that regardless of the discharge mode, a uniform disk is initially generated on the liquid surface, and various non-uniform patterns are formed during the plateau stage. Based on the reaction-diffusion model, numerical simulations are carried out through changing ion strength and current strength, which are related to the variables m and l. The simulated discharge modes are well in line with those obtained in the experiments. Moreover, spectral line intensity ratios related to electron temperature and electron density are determined through the spectra emitted from the discharge near the liquid surface. By fitting the spectra, gas temperature and molecular vibration temperature are obtained, which show an increasing trend with σ increasing.
, , Received Date: 2025-05-12
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Efficiently identifying multiple influential nodes is crucial for maximizing information diffusion and minimizing rumor spread in complex networks. Selecting multiple influential seed nodes requires to take into consider both their individual influence potential and their spatial dispersion within the network topology to avoid overlapping propagation ranges (“rich-club effect”). Traditional VoteRank method has two key limitations: 1)the voting contributions from a node is assumed to be consistent to all its neighbors, and the influence of topological similarity (structural homophily) on the voting preferences observed in real-world scenarios is neglected, and 2) a homogeneous voting attenuation strategy is used, which is insufficient to suppress propagation range overlap between selected seed nodes. To address these shortcomings, IMVoteRank, an improved VoteRank algorithm featuring dual innovations, is proposed in this work. First, a structural similarity-driven voting contribution mechanism is introduced. By recognizing that voters (nodes) are more likely to support candidates (neighbors) with stronger topological relationships with them, the voting contribution of neighbors is decomposed into two parts: direct connection contribution and a structural similarity contribution (quantified using common neighbors). A dynamic weight parameter θ, adjusted based on the candidate node's degree, balances these components, significantly refining vote allocation accuracy. Second, we devise a dynamic group isolation trategy. In each iteration, after selecting the highest-scoring seed node vmax, a tightly-knit group (OG) centered around it is identified and isolated. This involves: 1) forming an initial group based on neighbor density shared with vmax, 2) expanding it by merging nodes with more connections inside the group than outside, and 3) isolating this group by setting the voting capacity (Va) of all its members to zero and virtually removing their connections from the adjacency matrix. Neighbors of vmax not in OG have their Va values reduced by half. This strategy actively forces spatial dispersion among seeds. Extensive simulations using the susceptible-infected-recovered (SIR) propagation model on nine different real-world networks (ECON-WM3, Facebook-SZ, USAir, Celegans, ASOIAF, Dnc-corecipient, ERIS1176, DNC-emails, Facebook-combined) demonstrate the superior performance of IMVoteRank. Compared with seven benchmark methods (Degree, k-shell, VoteRank, NCVoteRank, VoteRank++, AIGCrank, EWV), IMVoteRank consistently achieves significantly larger final propagation coverage (infected scale) for a given number of seed nodes and transmission probability (β = 0.1). Furthermore, seeds selected by IMVoteRank exhibit a consistently larger average shortest path length (Ls) in most networks, which proves their effective topological dispersion. This combination of high personal influence potential (optimized voting) and low redundancy (group isolation) directly translates to more effective global information dissemination, as evidenced by the SIR results. Tests on LFR benchmark networks further validate these advantages, particularly at transmission rates above the epidemic threshold. IMVoteRank effectively overcomes the limitations of traditional voting models by integrating structural similarity into the voting process and employing dynamic group isolation to ensure seed dispersion. It provides a highly effective and physically reliable method for identifying multiple influential nodes in complex networks and optimizing the trade-off between influence strength and spatial coverage. Future work will focus on improving the computational efficiency of large-scale networks and exploring the influence of meso-scale community structures.
, , Received Date: 2025-04-04
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Electroosmosis drives a large slip velocity at the interface by altering the electrokinetic double layer effect at the fluid-solid interface, thereby generating high shear rates within the channel. In this paper, molecular dynamics simulations are used to construct an electroosmotic flow nanochannel model, and the fluid flow characteristics and wall slip reduction properties within graphene charged-wall nanochannels are investigated. The results show that the electroosmotic flow changes the structure of the bilayer to increase the mobility of its diffusion layer, and at the same time, the ions in the diffusion layer under the action of the applied electric field undergo directional migration and drive the overall fluid flow through the viscous effect, which enhances the mobility performance. After the introduction of ions, Na+ is adsorbed at the wall surface, which weakens the adsorption force between the fluid and the wall surface and enhances the driving force of the fluid in the confined domain space, thus increasing the slip length and flow rate. Finally, by modulating the charge size on the upper and lower wall surfaces, asymmetric channel wall charges are formed. The electric field gradient superimposed on the applied electric field further enhances the driving force of ions, changes the distribution of the of Na+ adsorption layer and the migration behavior of Cl–, thereby increasing the transport of the solution in the channel. Therefore, in this paper, a method is proposed to realize the ultrafast transport of solution in the channel by modulating the asymmetric wall charge of graphene, successfully achieving the slip reduction effect of the electroosmotic flow of solution in the graphene channel. A theoretical basis is laid for the fast and energy-saving transportation of microfluidics in the nano-limited space.
, , Received Date: 2025-04-17
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,
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Currently, it is a great challenge to accurately diagnose global properties of dusty plasmas from limited data. Based on machine learning, a novel diagnostic of various global properties in dusty plasma experiments is developed from individual particle dynamics. It is found that, for both two-dimensional (2D) dusty plasma simulations and experiments, the global properties of the screening parameters κ and the coupling parameter Γ can be accurately determined purely from the position fluctuations of individual particles. Hundreds of independent Langevin dynamical simulations are performed with various specified κ and Γ values, leading to abundant individual particle position fluctuation data, which can be used for training, validating, and testing various convolutional neural network (CNN) models. To confirm the feasibility of this diagnostic, three different CNN models are designed to determine the κ value. For the simulation data, all these CNN models have excellent performance in determining the κ value, i.e., the averaged determined κ value is nearly the same as the specified κ value. For the experiment data, the distributions of the determined κ values always exhibit one prominent peak, whose locations well agree with the determined κ value from the widely accepted phonon spectra fitting method. Furthermore, this diagnostic method is further developed to determine the κ and Γ values simultaneously, with the satisfactory outcome using the input 2D dusty plasma data from both simulations and experiments. The excellent performance of the CNN models developed here clearly indicates that, using machine learning, all information of global properties of 2D dusty plasmas can be obtained purely from individual particle dynamics.
,
Abstract +
Nitrate molten salt is widely used as high-efficiency thermal storage material for advancing concentrated solar power (CSP) technology, which is due to their many excellent properties such as thermal stability, high energy density, low viscosity and liquefaction temperature. However, the measurements of properties for nitrates are not convenient at high temperature melting state for long time period, which can cause the corrosion of storage vessel made by stainless steel by nitrates salt, and the theoretical simulations are also faced to large challenge of complicate models and long computing period for optimizing the performance of nitrate molten salts. In this study, an empirical electron theory (EET) of solids and molecules is used to investigate the valence electron structure, cohesive energy, and melting points of MNO3 (M = Li, Na, K) and their decomposition byproducts (nitrites) systematically for revealing the mechanism of these properties. The calculated bond lengths, cohesive energy, and melting points of nitrate molten salt agree with the experimental ones. The study reveals the strongly dependence of physical properties upon the valence electron structure. The bonding strength and ability strongly depend upon the covalent electron pairs nα. The cohesive energy exhibits a positive correlation with the number of valence electrons nc. The melting mechanism is originated from the melting broken of M-O (M = Li, Na, K) bond by the vibrating of thermal phonon at melting temperature, it is suggested the atomic cluster of NO3 is still stabilized during the melting process. In binary nitrate molten-salts, the calculated liquidus lines match the measured ones in their binary phase diagrams well. The liquid temperatures show significant positive correlation with the weighted average of number of covalent electron pairs (nM-O) on M-O bond. The thermodynamic simulation models are used systematically to predict the viscosity, electrical conductivity, and thermal conductivity of the binary nitrate molten-salts. Based on calculations of EET and thermodynamic simulations, the binary nitrates molten salts are optimized with compositions of 0.5LiNO3-0.5NaNO3、0.5LiNO3-0.5KNO3 and 0.6NaNO3-0.4KNO3, which are suggested as good candidates for advanced molten salts with high thermal and electrical conductivity, low viscosity, and low liquefaction temperature.
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