Accepted Papers
Recent catalogue
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Vol.74 No.1
2025-01-05
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Vol.73 No.24
2024-12-20
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Vol.73 No.23
2024-12-05
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Vol.73 No.22
2024-11-20
- All Archive
GENERAL
2025, 74 (1): 010201.
doi: 10.7498/aps.74.20240711
Abstract +
When energetic heavy ions are incident on negatively charged structure that collects and deposits ions, ion sputtering will occur. Metal wire is a structure commonly used for accelerating ions, the incidence of continuous high-throughput ions can cause surface loss of metal wire, affecting the service performance and lifespan of the metal wire. The SRIM software commonly used for calculating sputtering yield cannot consider the multi-body interaction problem contained in the alloy crystal structure. So, there is a significant error in calculating the sputtering yield of high-energy ions incident on alloy target. Based on the molecular dynamics method and Langevin temperature control model, the calculation model of ion sputtering parameters of energetic metal ions incident on alloy target is established in this work. The model is used to calculate the sputtering yield under the conditions of intact surface lattice of the target material and long-term incident surface lattice damage. The damages to the cathode metal wire under different incident ion fluences are further calculated, and the cross-sectional characterization of the metal wire is carried under typical working condition. The results show that the discrepancy between the experimental value and the theoretical value is less than 10%, which verifies the accuracy and applicability of the theoretical model. Based on this model, the search direction for sputtering resistant materials is proposed, meanwhile, a theoretical optimization is carried out to improve the service life of metal wire, and a method of using Ni-Ti alloy to improve the service life of metal wires is proposed, which is of great significance for predicting the service life of the metal wire under different conditions.
GENERAL
2025, 74 (1): 010301.
doi: 10.7498/aps.74.20240933
Abstract +
GENERAL
2025, 74 (1): 010501.
doi: 10.7498/aps.74.20241471
Abstract +
Brain diseases often occur simultaneously with critical changes in neural system and abnormal neuronal firing. Studying the early warning signals (EWSs) of critical changes can provide a promising approach for predicting neuronal firing behaviors, which is conducible to the early diagnosis and prevention of brain diseases. Traditional EWSs, such as autocorrelation and variance, have been widely used to detect the critical transitions in various dynamical systems. However, these methods have limitations in distinguishing different types of bifurcations. In contrast, the EWSs with power spectrum have shown a significant advantage in not only predicting bifurcation points but also distinguishing the types of bifurcations involved. Previous studies have demonstrated its predictive capability in climate and ecological models. Based on this, this study applies the EWS with power spectrum to neuronal systems in order to predict the neuronal firing behaviors and distinguish different classes of neuronal excitability. Specifically, we compute the EWSs before the occurrence of saddle-node bifurcation on the invariant circle and subcritical Hopf bifurcation in the Morris-Lecar neuron model. Additionally, we extend the analysis to the Hindmarsh-Rose model, calculating the EWSs before both saddle-node bifurcation and supercritical Hopf bifurcation. This study contains the four types of codimension-1 bifurcations corresponding to the neuronal firing. For comparison, we also calculate two types of conventional EWSs: lag-1 autocorrelation and variance. In numerical simulations, the stochastic differential equations are simulated by the Euler-Maruyama method. Then, the simulated responses are detrended by the Lowess filter. Finally, the EWSs are calculated by using the rolling window method to ensure the detection of EWS before bifurcation points. Our results show that the EWS with power spectrum can effectively predict the bifurcation points, which means that it can predict neuronal firing activities. Compared with the lag-1 autocorrelation and the variance, the EWSs with power spectrum not only accurately predict the neuronal firing, but also distinguish the classes of excitability in neurons. That is, according to the different characteristics of the power spectrum frequencies, the EWS with power spectrum can effectively distinguish between saddle-node bifurcations and Hopf bifurcations during neuronal firing. This work provides a novel approach for predicting the critical transitions in neural system, with potential applications in diagnosing and treating brain diseases.
GENERAL
2025, 74 (1): 012101.
doi: 10.7498/aps.74.20241201
Abstract +
The nuclear mass model has significant applications in nuclear physics, astrophysics, and nuclear engineering. The accurate prediction of binding energy is crucial for studying nuclear structure, reactions, and decay. However, traditional mass models exhibit significant errors in double magic number region and heavy nuclear region. These models are difficult to effectively describe shell effect and parity effect in the nuclear structure, and also fail to capture the subtle differences observed in experimental results. This study demonstrates the powerful modeling capabilities of MLP neural networks, which optimize the parameters of the nuclear mass model, and reduce prediction errors in key regions and globally. In the neural network, neutron number, proton number, and binding energy are used as training feature values, and the mass-model coefficient is regarded as training label value. The training set is composed of the multiple sets of calculated nuclear mass model coefficients. Through extensive experiments, the optimal parameters are determined to ensure the convergence speed and stability of the model. The Adam optimizer is used to adjust the weight and bias of the network to reduce the mean squared error loss during training. Based on the AME2020 dataset, the trained neural network model with the minimum loss is used to predict the optimal coefficients of the nuclear mass model. The optimized BW2 model significantly reduces root-mean-square errors in double magic number and heavy nuclear regions. Specifically, the optimized model reduces the root-mean-square error by about 28%, 12%, and 18% near Z = 50 and N = 50; Z(N) = 50 and N = 82; Z = 82 and N = 126, respectively. In the heavy nuclear region, the error is reduced by 48%. The BW3 model combines higher-order symmetry energy terms, and after parameter optimization using the neural network, reduces the global root-mean-square error from 1.86 MeV to 1.63 MeV. This work reveals that the model with newly optimized coefficients not only exhibit significant error reduction near double magic numbers, but also shows the improvements in binding energy predictions for both neutron-rich and neutron-deficient nuclei. Furthermore, the model shows good improvements in describing parity effects, accurately capturing the differences related to parity in isotopic chains with different proton numbers. This study demonstrates the tremendous potential of MLP neural networks in optimizing the parameters of nuclear mass model and provides a novel method for optimizing parameters in more complex nuclear mass models. In addition, the proposed method is applicable to the nuclear mass models with implicit or nonlinear relationships, providing a new perspective for further developing the nuclear mass models.
SPECIAL TOPIC—Correlated electron materials and scattering spectroscopy
2025, 74 (1): 012501.
doi: 10.7498/aps.74.20241412
Abstract +
Inelastic neutron scattering is a pivotal technique in materials science and physics research, revealing the microscopic dynamic properties of materials by observing the changes in energy and momentum of neutrons interacting with matter. This technique provides important information for quantitatively describing the phonon dispersion and magnetic excitation of materials. Inelastic neutron scattering spectrometers can be divided into triple-axis spectrometers and time-of-flight spectrometers, according to the method of selecting monochromatic neutrons. The former has high signal-to-noise ratio, flexibility, and precise tracking capabilities for specific measurement points, while the latter significantly improves experimental efficiency through various measures. The application of inelastic neutron scattering spectrometers is quite extensive, playing an indispensable role in advancing frontier scientific research in the study of mechanisms in various materials such as magnetism, superconductivity, thermoelectrics, and catalysis. The high-energy inelastic spectrometer at the China Spallation Neutron Source is the first time-of-flight neutron inelastic spectrometer in China, achieving high resolution and multi-energy coexistence with its innovative Fermi chopper design. Additionally, the number of available single neutron beams in the experiment of this facility has reached the international leading level.
SPECIAL TOPIC—Correlated electron materials and scattering spectroscopy
2025, 74 (1): 012801.
doi: 10.7498/aps.74.20241178
Abstract +
ATOMIC AND MOLECULAR PHYSICS
2025, 74 (1): 013201.
doi: 10.7498/aps.74.20240900
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ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS
2025, 74 (1): 014201.
doi: 10.7498/aps.74.20241305
Abstract +
In order to improve the electromagnetic shielding performance of the single-layer metal mesh transparent conductive films (SMMTCFs) based on the crack template method, the preparation of double-layer metal mesh transparent conductive films (DMMTCFs) by using the crack template method is studied. The double-layer cracked templates are prepared by spin-coating crack glue on both sides of the transparent substrate and by pulling the transparent substrate from the cracked adhesive solution with a certain rate to obtain the corresponding double-layer cracked templates, respectively. After obtaining the double-layer crack templates by the spin-coating method and the pulling method, respectively, the corresponding DMMTCF samples are obtained by metal deposition and degumming process. First, the performances of single-layer and double-layer metal mesh samples prepared by the spin-coating method under the same conditions are measured and compared with each other, and the optical transmittance of the double-layer structure decreases by nearly 10.9% compared with that of the single-layer structure, while the electromagnetic shielding effectiveness in the Ku band (12–18 GHz) increases by 30 dB. In addition, the double-layer metal mesh sample prepared by the pulling method is also tested. Compared with the single-layer metal mesh sample prepared under the same conditions, the double-layer structure can improve electromagnetic shielding effectiveness in the Ku band by 20 dB under the premise of losing 8.38% optical transmittance. The measurement results show that the electromagnetic shielding performance of the double-layer metal mesh transparent conductive films can be significantly improved at the expense of some optical transmittance performances. Through the preparation and performance study of DMMTCFs based on the cracked template method, the low-cost advantage of the cracked template method can be fully utilized to prepare DMMTCFs with high electromagnetic shielding performance.
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS
2025, 74 (1): 014202.
doi: 10.7498/aps.74.20241126
Abstract +
The Casimir effect, a macroscopic manifestation of quantum phenomena, arises from zero-point energy and thermal fluctuations. When two objects are brought into close proximity, the Casimir effect manifests as a repulsive force, while at greater separations, it transforms into an attractive force. There exists a specific distance at which the Casimir force vanishes, which is referred to as the stable Casimir equilibrium. Stable Casimir equilibrium arises from the curve minimum value of the Casimir energy, which can create spatial trapping. The manipulation of stable Casimir equilibrium provides promising applications in fields such as tunable optical resonators and self-assembly. This work presents a scheme for achieving tunable Casimir equilibrium in a dual-liquid system. The system comprises a multilayered stratified structure with a gold substrate. Above the gold substrate, a stratified liquid system is formed due to the immiscibility between organic solutions and water. The lower-density solution is at the top, while the higher-density solution is at the bottom. Our results suggest that a stable Casimir equilibrium for a suspended gold nanoplate can be realized, when the suspended gold nanoplate is immersed in organic solution of toluene or benzene. Moreover, the height of the suspended gold nanoplate, determined by the stable Casimir equilibrium, can be precisely tuned by changing the thickness of the water layer. The effects of finite temperature and ionic concentration on the Casimir equilibria are also analyzed in this work. The results suggest that the separation height of Casimir equilibrium decreases with the increase of temperature. Interestingly, when the Debye shielding length is comparable to or smaller than the separation length, the ion concentration in water significantly affects the Casimir pressure allowing for extensive modulations of Casimir equilibrium. This work opens up a new avenue for adjusting Casimir equilibrium and has important applications in “quantum trapping” of micro-nano particles.
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS
2025, 74 (1): 014203.
doi: 10.7498/aps.74.20241458
Abstract +
Fractional-order vortex beams possess fractional orbital angular momentum (FOAM) modes, which theoretically have the potential to increase transmission capacity infinitely. Therefore, they have significant application prospects in the fields of measurement, optical communication and microparticle manipulation. However, when fractional-order vortex beams propagate in free space, the discontinuity of the helical phase makes them susceptible to diffraction in practical applications, thereby affecting the accuracy of OAM mode recognition and severely limiting the use of FOAM-based optical communication. Achieving machine learning recognition of fractional-order vortex beams under diffraction conditions is currently an urgent and unreported issue. Based on ResNetA, a deep learning (DL) method of accurately recognizing the propagation distance and topological charge of fractional-order vortex beam diffraction process is proposed in this work. Utilizing both experimentally measured and numerically simulated intensity distributions, a dataset of vortex beam diffraction intensity patterns in atmospheric turbulence environments is created. An improved 101-layer ResNet structure based on transfer learning is employed to achieve accurate and efficient recognition of the FOAM model at different propagation distances. Experimental results show that the proposed method can accurately recognize FOAM modes with a propagation distance of 100 cm, a spacing of 5 cm, and a mode spacing of 0.1 under turbulent conditions, with an accuracy of 99.69%. This method considers the effect of atmospheric turbulence during spatial transmission, allowing the recognition scheme to achieve high accuracy even in special environments. It has the ability to distinguish ultra-fine FOAM modes and propagation distances, which cannot be achieved by traditional methods. This technology can be applied to multidimensional encoding and sensing measurements based on FOAM beam.
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