In Press
In Press catalogue
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Vol.74 No.23
2025-12-05
2025, 74 (23): 230101.
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2025, 74 (23): 230201.
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With the technological advantages of high thrust, high specific impulse, long life, variable specific impulse, and high efficiency, the variable specific impulse magnetoplasma rocket engine has become the essential advanced propulsion system for the deep space exploration and manned space flight in the future. In the variable specific impulse magnetoplasma rocket engine, the ion cyclotron resonance heating stage is linked with the helicon plasma source. The operation status of the helicon plasma source has a direct influence on the ion heating process in the ion cyclotron resonance heating stage. It is of great significance for the testing and the optimization of the engine performance to reveal the influence of the ionization process on the ion heating process. In this paper, a multi-fluid model in which the ion cyclotron resonance heating stage is linked with the helicon plasma source is developed. The numerical simulations with different input currents of helicon plasma source and different pressures are performed to analyze the effect of the operation status in the helicon plasma source on the ion energy density in the ion cyclotron resonance heating stage. The results show that the discharge mode of the helicon plasma source gradually changes with the increase of the input current and that the plasma density jump appears while the ion temperature remains basically unchanged. With the plasma density jump and nearly identical ion temperature the ion energy density jump also appears in the simulation domain. Similar to the results of the simulation under different input currents of the helicon plasma source, the plasma density and the ion energy density also jump when the pressure increases. However, the ion temperature decreases due to the discrepancy between the input frequency and the resonance frequency. With the numerical model and the input conditions of this study, the ionization process in the helicon plasma source is decoupled with the ion heating process in the ion cyclotron resonance heating stage. The energy gain of a single ion in the ion cyclotron resonance heating stage does not change with the operation status of the helicon plasma source, thereby accounting for the ability of the engine to work in multi mode.
2025, 74 (23): 234201.
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The key security of quantum key distribution (QKD) is guaranteed by the basic principle of quantum mechanics, which makes it possible to combine information theory security communication with one-time encryption. The key is usually encoded on the polarization dimension or phase dimension of a single-photon. It is considered that the birefringence effect of single-mode fiber leads to a random variation of polarization state, which would induce the bit error rate. So it is of great significance to keep the single-photon linear polarization state stable for both polarization encoding QKD system and phase encoding QKD system. By using the single-photon polarization modulation technology, the single-photon linear polarization state periodically varies with the external modulation signal. The flicker noise is suppressed effectively, and the signal-to-noise ratio (SNR) of single-photon counting is increased as indicated by the phase-sensitive detection with a lock-in amplifier (LIA). The error signal is generated by demodulating the modulated single photons and it is used to lock an arbitrary 1550 nm single-photon linear polarization state to the optical axis of in-line polarizer (ILP). The modulation frequency reaches up to 5 kHz, which can eliminate the influence of low frequency flicker noise. The LIA demodulates the single-photon pulses by using 78.1 Hz filter bandwidth, with a time constant of 1 ms and a filter slope of 24 dB. The SNR of error signal is 20. The zero-crossing point of error signal represents the single photon’s linear polarization state aligned to the optical axis of ILP. The linear slope around the zero-crossing point for the polarization state angle versus the error signal amplitude is 1.267 rad/V. When the negative feedback loop does not work, the polarization drift of single-photon pulses is 0.082 rad due to the random environmental noise. However, by using the single-photon polarization modulation technology, the polarization drift of stable single-photon pulses is limited to 0.0011 rad within 2000 s through the precise control with a polarization rotator, and the corresponding Allan deviation reaches the minimal value of 6.7×10–5 at an integration time of 128 ms. The advantages for the single-photon polarization modulation technology are as follows: i) the linear polarization state drift is compensated in real-time at the single-photon level; ii) single frequency polarization modulation can be extended to multiple frequency polarization modulation in order to achieve simultaneous locking of multiple linear polarization states of single-photon pulses; iii) these 1550 nm single-photon pulses with the 0.0011 rad linear polarization state stability can be directly used as the single-photon source in either polarization encoding or phase encoding QKD system.
2025, 74 (23): 235201.
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Capacitively coupled plasma sources, which are widely used in the etching and deposition processes of semiconductor manufacturing, have the advantages of simple structure, low cost, and the ability to generate large-area uniform plasma. To meet the requirements of advanced processes, fluid models are usually required to simulate plasma sources and optimize their important plasma parameters, such as density and uniformity. In this work, an independently-developed capacitively coupled plasma fast simulation program is employed to conduct three-dimensional fluid simulations of a dual-frequency capacitively coupled Ar/CF4 plasma source, with the aims of verifying the effectiveness of the program and investigating the influence of discharge parameters such as gas pressure, high and low-frequency voltages, low frequency, and background component ratios. The simulation results show that the program has an extremely high simulation speed. As the low-frequency voltage increases, the plasma density initially remains approximately constant and then significantly increases, while the plasma uniformity initially rises and then significantly decreases. In this process, the γ-mode heating of the low-frequency source increases and becomes the dominant mode in replace of the α-mode of high-frequency source. As the lower frequency increases, plasma density initially remains approximately constant and then slightly increases, while the plasma uniformity does not change much. this is because the γ-mode heating is frequency independent, while the α-mode heating is much lower than high-frequency source. As the high-frequency voltage increases, the plasma density significantly increases, while the plasma uniformity initially rises and then significantly decreases, the α-mode heating of high-frequency source is significantly enhanced in this process. As the pressure increases, the plasma density significantly increases, and the plasma uniformity also rises significantly, the reason is the more complete collision between particles and background gases. As the Ar ratio in background gases increases, the plasma density changes slightly, the density of Ar-related particles generally increases and the density of CF4-related particles generally decreases, although there are some non-monotonic changes in particle densities, which reflects the mutual promotion between some ionization and dissociation reactions.
2025, 74 (23): 235202.
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The pantograph-catenary system (PCS) serves as the exclusive means of power supply for high-speed trains. As train speeds increase, traction power rises, and operations take place in complex and variable environments, pantograph arcing has become more frequent. This phenomenon is accompanied by changes in physical properties and increased hazards, which seriously threaten the safety of high-speed railways. This paper systematically reviews the recent researches on pantograph arc, and outlines physical characteristics, experimental techniques, and simulation methods. The study focuses on analyzing the effects and mechanisms of operating parameters and environmental conditions on pantograph arc, summarizes prevention strategies, and explores applications such as arc energy utilization. Existing research has sufficiently examined how operational parameters affect arc hazards, yet studies on arc physical properties and evolution mechanisms remain limited, particularly regarding special conditions such as icing. Current protection methods also require adaptation to complex environments to meet the growing demands for arc management. Two future research priorities are proposed: first, clarifying the physical properties of an arc under special environments and establishing the correlation among “environmental conditions, an arc’s physical properties, and its behavior” to enable accurate prediction; second, developing an efficient arc prevention system through the approach of “source suppression, interface protection, and process intervention”. This review aims to provide theoretical and practical guidance for realizing reliable current collection and effective arc control in high-speed railway PCS in China.
2025, 74 (23): 235203.
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Atmospheric-pressure low-temperature plasma has been widely used in various fields such as biomedicine, environmental protection, and nanomanufacturing, and the key physicochemical processes in these applications involve the interactions between plasma and aqueous solutions. However, such plasma-liquid interactions are very complex, involving a wide range of gas-liquid phase reactions as well as coupled mass transfer processes. These intricate mechanisms make it challenging for existing experimental techniques to provide a systematic understanding, thereby highlighting the critical role of simulation studies. Over the past decade, significant progress has been made in the simulation of plasma-solution interactions. Researchers have basically solved the problems of scarce transport and reaction parameter data, established various types of simulation models, and actively explored new simulation methods based on intelligence algorithms. These advances have greatly deepened our understanding of this field. Thus, this paper reviews recent developments in simulation studies of plasma-solution interactions from three perspectives, namely parameter acquisition, model construction, and intelligent algorithms, with the aim of providing useful insights for researchers.
2025, 74 (23): 235204.
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Dielectric barrier discharge technology can generate cold plasma at atmospheric pressure, which contains abundant active particles and shows great potential for fresh produce sterilization applications. However, water droplets frequently adhere to the surfaces of fruits and vegetables, which changes key parameters including the gas gap width, dielectric distribution, and gas-phase composition, consequently affecting the effectiveness of plasma applications. Currently, plasma-droplet interactions with contact angle as a variable remain unexplored, and the underlying mechanisms by which adhering droplets affect the electrochemical characteristics of dielectric barrier discharge require further investigation. In this work, we develop an atmospheric-pressure helium dielectric barrier discharge simulation model with an He-O2-N2-H2O reaction system. This model is used to study how water droplets (with contact angles of 45°, 90°, and 135°) adhering to the surface of the specimens affect both the steady-state discharge structure and active particle distribution, as well as their underlying mechanisms. The results show that the steady-state discharge intensity is significantly weakened both at the droplet surface and in the region above it, with the greatest reduction occurring at a contact angle of 135°. During the main positive breakdown phase, the polarized electric field at the droplet surface significantly enhances both electron impact ionization and secondary electron emission, thereby promoting gas-phase breakdown in the region above the water droplet. During the main negative breakdown phase, this polarized electric field accelerates electron migration toward the liquid surface, which intensifies plasma ambipolar diffusion and consequently leads to the formation of an annular discharge suppression zone around the water droplet. During the secondary positive discharge phase, even though the water droplet becomes polarized and a radially inward electric field is generated near the liquid surface, the resulting seed electron scavenging effect suppresses discharge in the region above the water droplet. Due to the stronger polarized electric fields generated at the surfaces of water droplets with larger contact angles, both the discharge enhancement and suppression effects become more pronounced with the increase of contact angle. Regarding the chemical species distribution, active particles and electrons exhibit a synergistic distribution relationship. On the surface of the specimens, He+ ions undergo electric field-driven migration, resulting in a highly non-uniform spatial distribution. The evaporation of water droplets provides more reactant sources for OH generation, thereby increasing its total deposition quantity. Because the bond energy of O2 is lower than that of N2, oxygen (O) demonstrates a more uniform distribution and a greater total deposition quantity than nitrogen (N). On the surfaces of water droplets, the active particles exhibit a gradually decreasing distribution from the center to the edge. Notably, the total deposition quantity of He+ continuously increases with larger contact angles increasing due to the aggregation effect of the polarized electric field. This study systematically elucidates the influence mechanisms of adhering water droplets on the electrochemical processes in dielectric barrier discharge, providing theoretical guidance for relevant applications of plasma-droplet systems.
2025, 74 (23): 235205.
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Fluid simulations of capacitively coupled plasmas (CCPs) are crucial for understanding their discharge physics, yet the high computational cost results in a major bottleneck. To overcome this limitation, we develop a deep learning-based surrogate model to replicate the output of a one-dimensional CCP fluid model with near-instantaneous inference speed. Through a systematic evaluation of three architectures, i.e. feedforward neural network (FNN), attention-enhanced long short-term memory network (ALSTM), and convolutional-transformer hybrid network (CTransformer) it is found that the sequence-structured ALSTM model can achieve the optimal balance between speed and accuracy, with an overall prediction error of only 1.73% for electron density, electric field, and electron temperature in argon discharge. This study not only achieves significant simulation acceleration but also reveals that the model can accurately extrapolate from low-pressure conditions dominated by complex non-local effects to high-pressure conditions governed by simple local behavior, whereas the reverse extrapolation fails. This finding suggests that training under low-pressure conditions enables the model to capture more comprehensive physical features. From the perspective of model weights, both low-pressure and high-pressure models assign important weights to the sheath region. However, the low-pressure model exhibits higher weight peaks in the sheath, indicating stronger ability to capture the essential physics of sheath dynamics. In contrast, the high-pressure model, because of its lower weighting in the sheath region, may fail to adequately resolve complex sheath dynamics when predicting under new operating conditions, thereby limiting its extrapolation capability with high fidelity. To ensure the reliability of this data-driven model in practical applications, we establish a trust boundary with a normalized mean absolute spatial error of 5% for model performance through systematic extrapolation experiments. When the model's extrapolation error falls below this threshold, the spatial distribution curves of predicted parameters such as electron density and electron temperature closely match the true physical distributions. However, once the error exceeds this critical point, systematic deviations such as morphological distortion and amplitude discrepancies begin to appear in the predicted spatial distributions, significantly deviating from the true physical laws. In the future, we will develop neural network models capable of processing high-dimensional spatial data and combining multi-dimensional input features such as various discharge gases, ultimately realizing a dedicated AI model for the field of capacitively coupled plasmas.
2025, 74 (23): 235206.
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Octafluorocyclobutane (C4F8)-based fluorocarbon plasmas have become a cornerstone of nanometre-scale etching and deposition in advanced semiconductor manufacturing, owing to their tunable fluorine-to-carbon (F/C) ratio, high density of reactive radicals, and superior material selectivity. In high-aspect-ratio pattern transfer, optical emission spectroscopy (OES) enables in-situ monitoring by correlating the density of morphology-determining radicals with their characteristic spectral signatures, thereby providing a viable pathway for the simultaneously optimizing pattern fidelity and process yield. A predictive plasma model that integrates kinetic simulation with spectroscopic analysis is therefore indispensable. In this study, a C4F8/O2/Ar plasma model tailored for on-line emission-spectroscopy analysis is established. First, the comprehensive reaction mechanism is refined through a systematic investigation of C4F8 dissociation pathways and the oxidation kinetics of fluorocarbon radicals. Subsequently, the radiative-collisional processes for the excited states of F, CF, CF2, CO, Ar and O are incorporated, establishing an explicit linkage between spectral features and radical densities. Under representative inductively coupled plasma (ICP) discharge conditions, the spatiotemporal evolution of the aforementioned active species is analyzed and validated against experimental data. Kinetic back-tracking is employed to elucidate the formation and loss mechanisms of fluorocarbon radicals and ions, and potential sources of modelling uncertainty are discussed. This model has promising potential for application in real-time OES monitoring during actual etching processes.
2025, 74 (23): 235207.
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The inverse problem of low-temperature plasmas refers to determining discharge parameters such as voltage amplitude and frequency from plasma characteristics, including plasma density, electric field and electron temperature. Within the framework of fluid description, it is usually very challenging to address inverse problems by using traditional discretization methods. In this work, physics-informed neural networks (PINNs) are introduced to solve the inverse problem of atmospheric-pressure radio-frequency plasmas. The loss function of the PINNs is constructed by embedding three components: the main governing equations (continuity equation, Poisson equation, and drift–diffusion approximation), the discharge parameters to be inferred (voltage amplitude and frequency in this study), and additional electric field data. The well-trained PINNs can accurately recover the discharge parameters with errors within about 1%, while providing the full spatiotemporal evolution of plasma density, electric field, and flux. Furthermore, the effects of sampling positions, sampling sizes, and noise levels of the electric field data on the inversion accuracy of voltage amplitude and frequency are systematically investigated. The results demonstrate that PINNs are capable of achieving precise inversions of discharge parameters and accurate prediction of plasma characteristics under given experimental or computational data, thereby laying a foundation for the intelligent control of low-temperature plasmas.
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