Accepted
Reactivity enhancement with hybrid discharge mode enabled by microstructure-induced field distortion
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Abstract +
To investigate the enhancement mechanism of atmospheric-pressure oxygen pulsed discharge in a parallel-plate dielectric barrier discharge (DBD) with microstructures fabricated on the dielectric surface of the highvoltage electrode, this paper systematically analyzes the electron transport processes, the formation and evolution of electric fields, and the spatial distribution of particles using a two-dimensional fluid model. The introduction of microstructures induces significant electric field distortion, generating a strong transverse electric field that locally confines and focuses electrons beneath the micro-structured region, leading to the formation of a stable corona-mode discharge. Simultaneously, the reduced local discharge gap near the microstructure enhances the longitudinal electric field, resulting in a temporal asynchrony between the corona discharge under the microstructure and the parallel-plate discharge in the adjacent flat regions. As the geometric dimensions of the microstructures increase, a secondary discharge is triggered, further modulating the overall discharge behavior. Under conditions where the corona discharge is suppressed due to higher protrusions, the secondary discharge effectively compensates by increasing both the high-energy electron fraction and the spatially averaged density of reactive oxygen atoms. Simulation results reveal that the corona discharge and the secondary discharge significantly elevate electron density, electron temperature, and the proportion of highenergy electrons, thereby intensifying the discharge activity. These findings provide deep insight into the micro-mechanisms of microstructure-induced discharge enhancement and offer valuable guidance for the design of highly efficient plasma devices with tailored geometric features.
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Achieving a balance between low density, high strength, and good ductility remains a major challenge in the development of structural materials. Ti-based bulk metallic glasses (BMGs) have attracted considerable attention due to their exceptionally high specific strength; however, the intrinsic strength–plasticity trade-off has hindered their practical applications. Based on a quasicrystal-derived structural heredity and minor-element microalloying, this work realizes a synergistic enhancement of specific strength and plasticity in Ti-based BMGs. The resulting ((Ti40Zr40Ni20)72Be28)97Al3 BMGs exhibits an ultrahigh specific strength of 5.34 × 105 N·m·kg-1, setting a new record for Ti-based BMGs, together with a plastic strain of 13%, breaking the conventional strength–plasticity limitation of BMGs. Structural analyses reveal that Al microalloying effectively inherits and modulates the short-range order originating from the quasicrystalline structure, thereby achieving the observed synergistic enhancement in both strength and plasticity. This work provides new insights into composition design and lightweight structural applications of Ti-based BMGs.
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The finite - difference time - domain (FDTD) modeling of targets with infinitely thin graphene sheets poses a challenge due to the existence of surface current and the inability of longitudinal discretization. When analyzing the electromagnetic properties of targets via FDTD method, spatial discretization of the target is essential. In the case of macroscopic electromagnetic targets that incorporate ‘infinitely thin’ graphene interfaces, this interface cannot be longitudinally partitioned. Moreover, a surface current exists on the interface, rendering the conventional calculation methods for the tangential electric field on the interface inapplicable. To address this issue, we put forward a novel Equivalent Source Current (ESC) approach. The proposed method enables the graphene sheet to retain a two - dimensional structure and be positioned on the surface of the Yee cell during the spatial discretization of the FDTD method(Fig.2). Subsequently, the surface current on the graphene sheet is approximated as a source volume current. Then, the active Maxwell's equations are discretized at the tangential electric - field nodes on the graphene surface(Fig.2, Fig.3), thereby obtaining a modified formula for the electric - field. By introducing intermediate variables and integrating the Shift Operator (SO) method, which is employed to handle issues related to dispersive media, to process the correction formula, an FDTD iterative formula for calculating the tangential electric field at the graphene interface is deduced. This ultimately enables the FDTD calculations for targets with ‘infinitely thin’ graphene sheets. Excellent agreement between our FDTD results and analytical solutions in several numerical examples validates the proposed method. The methodological framework proposed in this study can be generalized and applied to the ‘zero-thickness’ dispersive interfaces with surface current distributions (such as metallic films and two-dimensional transition metal sulfides). This allows for a convenient numerical analysis of the electromagnetic properties of structures incorporating conductive dispersive interfaces.
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Proton radiography is an effective technique for diagnosing field distributions in plasmas. However, due to the complexity of electromagnetic field structures, reconstructing electromagnetic fields from proton radiographs is extremely challenging and often requires some simplified symmetry assumptions about the fields. Here, we present a machine learning approach to reconstruct three-dimensional (3D) magnetic field distributions from complex proton radiographs without relying on such assumptions.
To enable this, we construct the target 3D magnetic fields by linearly superposing multiple elementary magnetic structures generated from the Weibel instability. Each element is characterized by eight parameters—structural parameters (a, b, B0), spatial coordinates (x0, y0, z0), and rotation angles (θ, ϕ)—resulting in 80 degrees of freedom in total. Parameters were uniformly sampled within ±25% of their baseline values, and a dataset of 50,000 magnetic field–proton radiograph pairs was generated through forward simulation using GEANT4. All proton radiographs reside in the caustic regime, exhibiting multiple asymmetric caustics and significant flux concentrations.
A lightweight three-layer convolutional neural network (CNN) was designed for the reconstruction task. The network consists of an input layer, three convolutional modules (the first two following a ”convolution–batch normalization–max pooling” cascaded structure, and the third is simplified to a single convolutional layer), a flattening layer, a dropout layer, and an output layer. Bayesian optimization was applied to determine the optimal hyperparameters. The model was trained on 40000 samples, with 5000 samples for validation and 5000 for testing.
On the test set, the CNN achieves a mean absolute percentage error (MAPE) of 8.5% in predicting the 80 magnetic parameters, below the 12.9% random-guessing threshold. Prediction errors for most parameters follow near-zero-mean Gaussian distributions, with relative standard deviations under 6%. The reconstructed fields show high spatial agreement with the reference fields, and corresponding proton images match the originals with a cosine similarity of 0.89.
This study demonstrates that our CNN-based proton radiography reconstruction method can effectively reconstruct complex 3D magnetic fields without symmetry assumptions or manual parameter tuning, offering a novel tool for diagnosing electromagnetic fields in high-intensity laser-plasma interactions. Future work may incorporate multi-angle proton radiography and transfer learning from experimental data to enhance the method’s practicality and robustness.
To enable this, we construct the target 3D magnetic fields by linearly superposing multiple elementary magnetic structures generated from the Weibel instability. Each element is characterized by eight parameters—structural parameters (a, b, B0), spatial coordinates (x0, y0, z0), and rotation angles (θ, ϕ)—resulting in 80 degrees of freedom in total. Parameters were uniformly sampled within ±25% of their baseline values, and a dataset of 50,000 magnetic field–proton radiograph pairs was generated through forward simulation using GEANT4. All proton radiographs reside in the caustic regime, exhibiting multiple asymmetric caustics and significant flux concentrations.
A lightweight three-layer convolutional neural network (CNN) was designed for the reconstruction task. The network consists of an input layer, three convolutional modules (the first two following a ”convolution–batch normalization–max pooling” cascaded structure, and the third is simplified to a single convolutional layer), a flattening layer, a dropout layer, and an output layer. Bayesian optimization was applied to determine the optimal hyperparameters. The model was trained on 40000 samples, with 5000 samples for validation and 5000 for testing.
On the test set, the CNN achieves a mean absolute percentage error (MAPE) of 8.5% in predicting the 80 magnetic parameters, below the 12.9% random-guessing threshold. Prediction errors for most parameters follow near-zero-mean Gaussian distributions, with relative standard deviations under 6%. The reconstructed fields show high spatial agreement with the reference fields, and corresponding proton images match the originals with a cosine similarity of 0.89.
This study demonstrates that our CNN-based proton radiography reconstruction method can effectively reconstruct complex 3D magnetic fields without symmetry assumptions or manual parameter tuning, offering a novel tool for diagnosing electromagnetic fields in high-intensity laser-plasma interactions. Future work may incorporate multi-angle proton radiography and transfer learning from experimental data to enhance the method’s practicality and robustness.
, , Received Date: 2025-07-22
Abstract +
AlCoCrFeNi high-entropy alloys have consistently attracted attention due to their outstanding strength-to-ductility ratio. However, the substantial content of expensive cobalt in these alloys has somewhat limited their engineering applications. Consequently, there is an urgent need to design and develop high-performance, low-cost cobalt-free high-entropy alloys. AlCrFeNi alloys exhibit microstructures and properties similar to AlCoCrFeNi alloys. Simultaneously, the absence of Co significantly reduces costs and markedly improves casting performance. These alloys represent a potential structural material for harsh environments, demonstrating promising engineering applications. In order to explore the phase modulation mechanism of Nb element on AlCrFeNi alloy, this study combines experiments with first principles calculations to systematically investigate the effects of Nb on the microstructure, mechanical properties and wear resistance of AlCrFeNi alloy. The research results show that the AlCrFeNiNb0.4 high-entropy alloy has the best mechanical properties and wear resistance.The doping of Nb changes the wear mechanism of the AlCrFeNi alloy and improves the wear resistance of the alloy. This is attributed to the phase modulation effect of Nb on AlCrFeNi alloy. On the one hand, it induces the precipitation of Laves phase, which has high hardness, and on the other hand, it solidly dissolves in the BCC and B2 phases of the alloy, significantly improving the mechanical properties of the two phases. In addition, Nb doping refines the microstructure of the AlCrFeNi alloy, which leads to an increase in the phase interface density, thus enhancing the hardness, yield strength and wear resistance of the alloy. First principles calculations show that the Nb atoms change the electronic structures of the BCC and B2 phases in the AlCrFeNi alloy, thereby enhancing the stability of the two phases and confirming the solid solution strengthening effect of Nb on the two phases. The Nb atoms form strong antibonds with most of the atoms in the two phases, which further explains the nature of the generation of a large number of Laves phases in the microstructure of the alloy after Nb doping.
, , Received Date: 2025-06-24
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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.
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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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Quantum Fisher Information plays a central role in the fields of quantum metrology and quantum precision measurement. However, quantum systems are susceptible to the influence of noisy environments, which reduces the precision of parameter estimation (as measured by quantum Fisher information). Therefore, overcoming the impact of environmental noise on quantum systems to enhance the quantum Fisher information of parameters has become an important scientific issue in quantum precision measurement. In this paper, we investigate the enhancement of phase estimation precision for a two-level atom subjected to a zero-temperature bosonic environment, based on a continuous null-result measurement scheme. First, an analytical expression for the final state of the atomic system after n null-result measurements is derived. To highlight the crucial role of continuous measurement in the dynamics of the two-level atom, the core amplitude coefficient in the final state is reformulated into a specific form, yielding a concise mathematical expression. Interestingly, we find that the dynamics of the two-level atom under continuous measurements are closely related to a scaling parameter—the product of the environmental spectral width and the measurement time interval. In certain special cases, this formulation reduces to known results such as the quantum Zeno effect and Markovian approximations. Furthermore, we demonstrate that, under both Markovian and non-Markovian conditions, the quantum Fisher information for the atomic phase estimation can be significantly enhanced by tuning this scaling parameter. Using an exactly solvable model, we also provide an explanation for the quantum Zeno effect without explicit use of the projection postulate, and find that in certain limits, a concise formula for $\tilde{h}(t) = h^n(\tau)$ accurately captures the numerical results across a broad range of parameters. In summary, the proposed scheme of frequent null-result measurements with post-selection on the environment effectively mitigates the detrimental effects of decoherence on the quantum Fisher information, offering a novel theoretical approach for achieving high-precision measurements in open quantum systems.
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The time-dependent response of transient current to incident laser pulses in molecular junctions is an important method to obtain information about molecular structures and excited-state dynamics. In this work, a theoretical study is carried out on the transient charge transport through a model polyacetylene molecular junction driven by Gaussian-type femtosecond laser pulses. The molecule is described by the extended Su-Schrieffer-Heeger model, which explicitly includes electron-phonon interactions and captures both electronic and lattice degrees of freedom. The transient transport dynamics are calculated by combining the non-equilibrium Green’s function formalism with the hierarchical equations of motion, allowing a fully non-adiabatic description of the coupled electron-lattice evolution.
Results show that the central frequency of the incident laser pulse is one of the key factors that determines the transient current response. When electrons resonate with the optical field, the current amplitude is significantly enhanced, and the temporal profile becomes asynchronous with the laser field, indicating strong non-linear response. The corresponding current spectra exhibit broadened main peaks accompanied by multiple sidebands, suggesting the coexistence of various frequency components due to dynamic coupling between electrons and lattice vibrations.
Further analysis of the evolution of instantaneous energy levels demonstrates that, under resonant excitation, electrons are efficiently excited from HOMO to LUMO. The excited electrons induce lattice relaxation through electron-phonon coupling, resulting in local structural distortion and the formation of self-trapped excitonic states. These excitonic effects lead to additional energy transfer channels, thus amplifying the current response and broadening the frequency spectrum.
In contrast, when the lattice motion is artificially frozen, both the current amplitude and frequency broadening are greatly suppressed, and only a single sharp spectral peak corresponding to the laser frequency is observed. This comparison clearly demonstrates that electron-phonon coupling is a key factor governing the transient transport behavior in molecular junctions under optical excitation.
The present study reveals the microscopic mechanism of light-induced transient transport in organic molecular junctions and highlights the essential role of lattice dynamics in modulating non-equilibrium charge transfer. These findings provide theoretical guidance for the design of novel optoelectronic molecular devices and contribute to the fundamental understanding of non-adiabatic transport processes in low-dimensional quantum systems.
Results show that the central frequency of the incident laser pulse is one of the key factors that determines the transient current response. When electrons resonate with the optical field, the current amplitude is significantly enhanced, and the temporal profile becomes asynchronous with the laser field, indicating strong non-linear response. The corresponding current spectra exhibit broadened main peaks accompanied by multiple sidebands, suggesting the coexistence of various frequency components due to dynamic coupling between electrons and lattice vibrations.
Further analysis of the evolution of instantaneous energy levels demonstrates that, under resonant excitation, electrons are efficiently excited from HOMO to LUMO. The excited electrons induce lattice relaxation through electron-phonon coupling, resulting in local structural distortion and the formation of self-trapped excitonic states. These excitonic effects lead to additional energy transfer channels, thus amplifying the current response and broadening the frequency spectrum.
In contrast, when the lattice motion is artificially frozen, both the current amplitude and frequency broadening are greatly suppressed, and only a single sharp spectral peak corresponding to the laser frequency is observed. This comparison clearly demonstrates that electron-phonon coupling is a key factor governing the transient transport behavior in molecular junctions under optical excitation.
The present study reveals the microscopic mechanism of light-induced transient transport in organic molecular junctions and highlights the essential role of lattice dynamics in modulating non-equilibrium charge transfer. These findings provide theoretical guidance for the design of novel optoelectronic molecular devices and contribute to the fundamental understanding of non-adiabatic transport processes in low-dimensional quantum systems.
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The quantum statistical properties of optical fields are core parameters that characterize the intrinsic physical properties of light sources, among which the second-order degree of coherence g(2)(0) serves as a key criterion for distinguishing between different types of light such as thermal light and coherent light, and thus holds significant theoretical and practical value. The quantum correlation characteristics inherent in these properties provide crucial physical support for advanced fields including quantum spectroscopy and quantum imaging. Particularly in correlation imaging, this technique exhibits irreplaceable potential for complex scene detection, owing to its strong resistance to scattering interference and exceptional capability for high-resolution imaging under weak-light conditions. However, existing technologies are still constrained by several critical limitations, including the limited stability of sources with a high degree of coherence, insufficient manipulation speed and control over light intensity, a lack of synergy between coherent control and mode customization, poor adaptability to low-light conditions, and lagging capabilities in the analysis of high-order coherence control.
In response to the aforementioned issues, this study employs a Single-Photon Detection Array (SPDA) as the core detection device and proposes two schemes for enhancing the second-order coherence of a light field: an innovative approach based on random dynamic mask modulation and a comparative scheme using a Hadamard mask. By spatially modulating a coherent light field with an initial second-order coherence of 1, a light beam exhibiting both strong correlations and power-law statistical properties is successfully generated. Throughout the investigation, the photon statistical distribution and second-order coherence characteristics of the modulated light were systematically examined, with emphasis placed on analyzing the influence of key parameters such as exposure time and mask modulation frequency, while the enhancement effect of this modulation technique on single-photon correlation imaging performance was also experimentally validated.
Experimental results demonstrate that the proposed scheme achieves significant effectiveness in both light field manipulation and imaging optimization. In terms of photon statistical property control, the proposed method enables efficient manipulation of light fields with average photon numbers ranging from 10-2 to 102. The photon number statistics of the modulated light field strictly adhere to a discrete power-law distribution, and its distribution curve exhibits a distinct linear relationship within a specific interval in double logarithmic coordinates. This finding provides critical support for the quantitative analysis of quantum statistical properties in highly coherent light fields. Regarding the enhancement of second-order coherence and imaging performance optimization, under short exposure conditions (5 μs), the random dynamic mask can elevate the second-order coherence of the initial coherent light field to 98.6667, with an average photon number per pixel of only 0.0076, while the Hadamard mask can increase it to 47.2899, corresponding to an average photon number per pixel of 0.0137. Further experimental validation confirms that the g(2) correlation imaging scheme based on the second-order coherence significantly outperforms the traditional frame stacking approach in all performance metrics. With the proposed scheme, only 20 frames are required to achieve substantial improvement in imaging quality. Specifically, compared to the traditional frame stacking method, loading the random dynamic mask results in the following performance enhancements: the peak signal-to-noise ratio (PSNR) increases by 20.98 dB, the structural similarity (SSIM) improves by 0.84, the contrast (CTRS) enhances by 73.97, and the sharpness (ACU) rises by 34.01 compared to the initial value.
In summary, the modulation and imaging scheme proposed in this study can effectively optimize the performance of single-photon detection array under conditions of low photon flux and short exposure, providing a feasible approach for high-quality imaging in low-light scenarios. Meanwhile, experimental results fully demonstrate the core role of high-coherence light fields in promoting the performance of single-photon correlation imaging, which holds significant reference value for the practical application of quantum imaging technology.
In response to the aforementioned issues, this study employs a Single-Photon Detection Array (SPDA) as the core detection device and proposes two schemes for enhancing the second-order coherence of a light field: an innovative approach based on random dynamic mask modulation and a comparative scheme using a Hadamard mask. By spatially modulating a coherent light field with an initial second-order coherence of 1, a light beam exhibiting both strong correlations and power-law statistical properties is successfully generated. Throughout the investigation, the photon statistical distribution and second-order coherence characteristics of the modulated light were systematically examined, with emphasis placed on analyzing the influence of key parameters such as exposure time and mask modulation frequency, while the enhancement effect of this modulation technique on single-photon correlation imaging performance was also experimentally validated.
Experimental results demonstrate that the proposed scheme achieves significant effectiveness in both light field manipulation and imaging optimization. In terms of photon statistical property control, the proposed method enables efficient manipulation of light fields with average photon numbers ranging from 10-2 to 102. The photon number statistics of the modulated light field strictly adhere to a discrete power-law distribution, and its distribution curve exhibits a distinct linear relationship within a specific interval in double logarithmic coordinates. This finding provides critical support for the quantitative analysis of quantum statistical properties in highly coherent light fields. Regarding the enhancement of second-order coherence and imaging performance optimization, under short exposure conditions (5 μs), the random dynamic mask can elevate the second-order coherence of the initial coherent light field to 98.6667, with an average photon number per pixel of only 0.0076, while the Hadamard mask can increase it to 47.2899, corresponding to an average photon number per pixel of 0.0137. Further experimental validation confirms that the g(2) correlation imaging scheme based on the second-order coherence significantly outperforms the traditional frame stacking approach in all performance metrics. With the proposed scheme, only 20 frames are required to achieve substantial improvement in imaging quality. Specifically, compared to the traditional frame stacking method, loading the random dynamic mask results in the following performance enhancements: the peak signal-to-noise ratio (PSNR) increases by 20.98 dB, the structural similarity (SSIM) improves by 0.84, the contrast (CTRS) enhances by 73.97, and the sharpness (ACU) rises by 34.01 compared to the initial value.
In summary, the modulation and imaging scheme proposed in this study can effectively optimize the performance of single-photon detection array under conditions of low photon flux and short exposure, providing a feasible approach for high-quality imaging in low-light scenarios. Meanwhile, experimental results fully demonstrate the core role of high-coherence light fields in promoting the performance of single-photon correlation imaging, which holds significant reference value for the practical application of quantum imaging technology.
, , Received Date: 2025-08-26
Abstract +
The capillary discharge plasma ignition device features a simple and reliable structure with a high ignition efficiency, and has become a research focus in both industrial applications and academic studies. The transient radiative heat flux characteristics of the plasma jet is a critical indicator for characterizing its ignition capability. In this work, a transient radiative heat flux measurement system based on a thin-film heatflux gauge is established. Design and optimization methods are proposed to address the measurement range, response time, and sensitivity of the thin-film probe. The results indicate that reducing the thickness of the film can enhance measurement sensitivity effectively, whereas changing the film material yields relatively limited improvement. Additionally, the effects of energy storage capacitor voltage and capillary diameter on the output radiative heat flux characteristics are investigated using polyethylene and polytetrafluoroethylene as capillary propellant. The results indicate that the radiative heat flux of capillary discharge exhibits a temporal delay compared with the main discharge current. Increasing the voltage of the energy storage capacitor enhances the energy deposition efficiency of the main discharge and the plasma temperature, thereby improving both the output radiative heat flux and the duration of the heat flux. Moreover, the growth rate of the heat flux exceeds that of the stored energy. Enlarging the capillary diameter reduces the discharge time constant, thereby shortening the heat flux duration. At the same time, the ablation of the propellant becomes more sufficient, resulting in fewer jet deposits and a weaker absorption of the heat flux. When the capillary diameter increases from 1.5 mm to 3 mm, the jet expansion velocity and the energy deposition efficiency are significantly enhanced, leading to a remarkable increase in the radiative heat flux density. However, when the diameter further increases from 3 mm to 6 mm, the jet expansion velocity changes marginally, while the decrease of energy deposition efficiencycan result in a reduction in radiative heat flux. The capillary discharge with polyethylene propellant exhibits a higher peak radiative heat flux, an earlier peak time, and a shorter duration than that with the polytetrafluoroethylene propellant.
, , Received Date: 2025-08-29
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Understanding the dynamics of ions in the magnetron sputtering process of transparent conductive oxide (TCO) films is essential for clarifying the mechanisms of sputtering-induced damage and developing effective suppression strategies. In this work, indium tin oxide (ITO) is used as a cathode target in an RF magnetron sputtering system operating under pure argon atmosphere, and a positively biased auxiliary anode is introduced to modulate the plasma potential and investigate its effect on the ion energy distribution functions (IEDFs) at the substrate position. The ion energy spectra are measured using a commercial energy–mass spectrometer (EQP 1000, Hiden), and the plasma parameters such as potential and electron density are characterized using a radio-frequency compensated Langmuir probe. The results show that the incident positive ions consist mainly of O+, Ar+, In+, Sn+, as well as multiple metal oxide molecular and doubly charged ions. Their energies are determined by the combined effects of the initial ejection or backscattering energy of sputtered particles and the plasma potential. Increasing the auxiliary anode bias leads to an elevation of the plasma potential, thereby enhancing both the kinetic energy and flux of positive ions. In contrast, negative ions such as O– and O2– originate predominantly from cathode sputtering, exhibiting broad, multi-peaked energy distributions that are strongly influenced by RF oscillations of the cathode voltage and plasma potential, as well as relaxation effects during ion transport. Heavier metal oxide negative ions (InO–, InO2–, SnO–, SnO2–) respond more slowly to RF sheath modulation, with their high-energy peaks converging toward the cathode bias potential. Applying a positive auxiliary anode bias effectively reduces the cathode bias voltage, thereby suppressing the high-energy tail of negative ions without significantly affecting their total energy-integrated intensity. This demonstrates that auxiliary anode biasing provides an effective means for adjusting the ion energy distributions in magnetron sputtering discharges. The proposed approach provides a potential pathway for mitigating sputtering-induced damage and improving the structural and electronic quality of ITO films. Future work will focus on correlating the measured ion energy modulation with comprehensive film characterizations—including optical, electrical, and interfacial analyses—to further verify the physical mechanisms and evaluate the practical effectiveness of damage suppression during TCO deposition.
Abstract +
This study investigates gap solitons and their stability in Bose-Einstein condensates confined in Moiré optical lattices with distinct twisted angles. The results demonstrate that the twisted angle significantly modulates the Moiré periodicity and the flatness of low bands. For sufficiently large angular differences, smaller twisted angles generally lead to larger Moiré periods and flatter low bands, though this trend becomes less consistent at minimal angular differences. Moreover, smaller twisted angles yield more complex potential structures, which modify gap positions and widths, consequently affecting the properties of gap solitons. Using the Newton-conjugate gradient method, we identify various types of solitons in Moiré lattice with four different twisted angles, observing that solitons can exist over a broader range of potential depths at smaller twisted angles. The density distributions of solitons exhibit markedly different behaviors in different gaps: in the semi-infinite gap dominated by attractive interactions, deeper potentials lead to reduced soliton density, whereas in the first gap governed by repulsive interactions, deeper potentials enhance soliton density distributions. Linear stability analysis and nonlinear dynamical evolution results indicate that solitons found in the first gap(including both single-humped and multi-humped structures) demonstrate robust dynamical stability, whereas in the semi-infinite gap, single-humped solitons maintain good stability, while closely separated multi-humped in-phase solitons tend to be unstable, with enhanced stability observed for solitons located closer to the band edges. This work provides a theoretical foundation for manipulating nonlinear solitons in Moiré superlattices.
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This study applies machine learning, specifically transfer learning with neural networks, to improve predictions of fission barrier heights and ground state binding energies of superheavy nuclei, which are crucial for calculating survival probabilities in fusion reactions. Transfer learning for neural networks proceeds in two stages: pre-training and fine-tuning, each driven by a distinct pre-training data set and target data set. In this work we split the pre-training data into 60 % for training and 40 % for validation, while the target data are partitioned into 20 % test, with the remaining 80 % further divided into 60 % training and 40 % validation. To construct the neural-network model we adopt the proton number Z and mass number A as the input layer, employ two hidden layers each containing 128 neurons with ReLU (Rectified Linear Unit) activation, and set the learning rate to 0.001. For the fission-barrier-height model, the pre-training dataset is either the FRLDM or the WS4 model data, and the experimental measurements serve as the target set. For the ground-state binding-energy model, we first form the residuals between WS4 predictions and the AME2020 evaluation, then separate these residuals into a light-nucleus subset and a heavy-nucleus subset according to proton number. The light-nucleus subset is used for pre-training and the heavy-nucleus subset for fine-tuning. After optimization, the root-mean-square error (RMSE) of the FRLDM barrier model falls from 1.03 MeV to 0.60 MeV, and that of the WS4 barrier model drops from 0.97 MeV to 0.61 MeV. For the binding-energy model, the RMSE decreases from 0.33 MeV to 0.17 MeV on the test set and from 0.29 MeV to 0.26 MeV on the full data set. We also present the performance of the fission-barrier model before and after refinement, together with the predicted barrier heights along the isotopic chains of the new elements Z = 119 and Z = 120, and analyzed the reasons for the differences in the results obtained by different models. We hope that these results are intended to provide a useful reference for future theoretical studies. The datasets in this paper are openly available at https://www.doi.org/10.57760/sciencedb.28388(Please use private access link https://www.scidb.cn/s/6fmeIz to access the dataset during the peer review process).
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Octafluorocyclobutane (C4F8)-based fluorocarbon plasmas have emerged as the cornerstone of nanometre-scale etching and deposition in advanced semiconductor manufacturing, owing to their tunable fluorine-to-carbon (F/C) ratio, elevated 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 offering a viable pathway for the simultaneous optimisation of 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, 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 analysed 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 holds promising potential for application in real-time OES monitoring during actual etching processes.
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