Accepted
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Abstract +
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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Abstract +
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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Abstract +
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
Abstract +
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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Covalent Organic Frameworks (COFs) are regarded as aclassofpromising Surface-Enhanced Raman Scattering (SERS) substrates, owing to their highly ordered porous structure, excellent molecular adsorption capacity, and structural stability have attracted widely attention. However, traditional COF materials lack plasmonic properties, making it difficult to achieve a high-intensity Raman enhancement effect, which limits their applicationin high-sensitivity detection. To address this issue, a novel ruthenium-based covalent was choosen. Organic framework composite material (Ru-COF) was designed and fabricated in this study for constructing high-performance SERS-active substrates. By directly incorporating ruthenium complexes into the COF skeleton, astable Ru–N/Ocovalent coordination structure was formed, which effectively improved the loading capacity and dispersibility of ruthenium, while significantly enhancing the electromagnetic field coupling strength and electron transfer capability ofthesubstrate.Compared with pure COFs, the Ru-COF substrate exhibited excellentSERS response performance in the detection of MethyleneBlue (MB) molecules. Specifically,it achieved a low limit ofdetection (LOD) down to10 12 mol·L 1,alinearcorrelation coefficient (R2) ofno less than 0.99, and a high enhancement factor (EF) of up to 1.83×101. Additionally, the substrate showed good signal reproducibility(relative standard deviation, RSD < 5%) and retained over 90% o its initial signal intensity even after exposure to air for four months, demonstrating outstanding stability and durability. Further application studies indicated that the Ru-COF substrate could still realize stable detection of trace MB molecules in complex water samples, with the LOD remaining at the1012 mol·L1 level, along with excellent anti-ioninterference ability and signal consistency. This suggests that the substrate notonlyexhibits exceptional sensitivity and reproducibility under standard conditions but also holds potential for high-sensitivity quantitative detection in real environmental samples.The designstrategyofthismaterialprovidesanewresearchdirectionformetal-organic synergistically enhanced SERS systems and lays a crucial foundation for their practical applications in fields such as environmental pollutant detection,foodsafety analysis, and clinical diagnosis.
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High-pressure science has emerged as one of the core frontiers in exploring novel states of matter and phenomena under extreme conditions. In high-pressure environments, the in situ detection of physical quantities such as magnetic fields and pressure is crucial for understanding material behavior under extreme conditions. However, conventional high-pressure magnetic sensing techniques often face challenges such as low spatial resolution, poor sensitivity, and difficulties in achieving in situ magnetic detection.
In recent years, quantum sensors based on solid-state color centers—such as nitrogen-vacancy centers in diamond, silicon-vacancy/double-vacancy centers in silicon carbide, and color centers in hexagonal boron nitride—have enabled high-pressure quantum metrology with micrometer-scale spatial resolution, high sensitivity, and superior in situ detection capabilities, offering innovative solutions for high-pressure research.
This review systematically summarizes the effects of extreme high-pressure conditions on the optical and spin properties of these solid-state defects. Furthermore, taking high-pressure magnetic phase transition studies in magnetic materials and Meissner effect measurements in superconductors as examples, we highlight recent advances in in situ magnetic sensing using solid-state color centers under high pressure. This overview aims to provide technical guidance for the future development of high-pressure quantum precision measurement techniques based on solid-state defects.
In recent years, quantum sensors based on solid-state color centers—such as nitrogen-vacancy centers in diamond, silicon-vacancy/double-vacancy centers in silicon carbide, and color centers in hexagonal boron nitride—have enabled high-pressure quantum metrology with micrometer-scale spatial resolution, high sensitivity, and superior in situ detection capabilities, offering innovative solutions for high-pressure research.
This review systematically summarizes the effects of extreme high-pressure conditions on the optical and spin properties of these solid-state defects. Furthermore, taking high-pressure magnetic phase transition studies in magnetic materials and Meissner effect measurements in superconductors as examples, we highlight recent advances in in situ magnetic sensing using solid-state color centers under high pressure. This overview aims to provide technical guidance for the future development of high-pressure quantum precision measurement techniques based on solid-state defects.
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Photodetectors play an essential role in optical communications, environmental monitoring, and medical imaging, and their performance strongly depends on the properties of the optoelectronic materials. Therefore, the exploration of high-performance optoelectronic materials has long been a research focus in the field of materials science. Viologen-based organic materials, owing to their unique redox and chromic characteristics, have been extensively utilized in electrochromic devices, biosensors, and flow batteries. In this work, a viologen complex containing the transition metal element Co, {[Co(BPYBDC) (H2O)5]·(BDC)·H2O} (denoted as 1-Co) was designed and successfully synthesized. A series of in-situ high-pressure characterization techniques were employed to systematically investigate its structural and optoelectronic behaviors. The results reveal that 1-Co crystallizes in the Pc space group and remains structurally stable up to 11.6 GPa without any phase transition. UV-visible absorption spectroscopy shows a red-shift of the absorption edge upon compression, accompanied by a color change from colorless and transparent to yellow, indicating a pressure-induced narrowing of the optical bandgap. Consistent with the bandgap narrowing, impedance measurements demonstrate a significant reduction in the total resistance under compression, which remains about two orders of magnitude lower than the initial value after decompression. Furthermore, the photocurrent response is markedly suppressed under compression and barely recovers upon pressure release. This behavior can be attributed to the enhanced recombination of electrons with viologen groups under compression, leading to the formation of stable viologen radical states. These localized radicals cannot effectively participate in the separation and transport of photogenerated carriers, thereby contributing little to the photocurrent. These findings suggest that high pressure effectively modulates the optical and electrical behaviors of 1-Co by tuning intermolecular interactions and the electronic band structure, providing valuable insights into the pressure-dependent behavior of viologen-based materials.
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The uncertainty principle, a cornerstone of quantum mechanics, has evolved from a fundamental limitation into a manageable resource in quantum information science. Precise control over quantum uncertainty is crucial for ensuring the security of quantum cryptography and the advantage of quantum computation. This work investigates the control of the quantum-memory-assisted entropic uncertainty relation in a noisy two-particle qutrit system, using quantum feedback control as a suppression strategy. In our model, Bob prepares a system AB composed of two V-type three-level atoms and sends atom A to Alice. Atom A interacts with a bimodal dissipative cavity. To suppress decoherence, a photodetector is used to monitor the dissipative cavity. Once a photon is detected, a local quantum feedback control is applied to atom A. Meanwhile, Bob’s atom B is assumed to be isolated from the noisy environment. To quantify the uncertainty, we select two incompatible observables, Sx and Sz, corresponding to the spin-1 components. We analyze the evolution of the entropic uncertainty and its lower bound, with the system initialized in two distinct states: an excited state and a maximally entangled state. Our findings demonstrate that applying appropriate quantum feedback control to the system can significantly suppress decoherence, leading to a marked reduction in both the entropic uncertainty and its lower bound. Through numerical simulations, we identify the optimal feedback strength for minimizing the entropic uncertainty and its lower bound to be p=2 for both initial states. Furthermore, examination of the system’s steady-state behavior after prolonged evolution reveals a key insight: under optimal feedback, the initial maximally entangled state evolves into a state with maximal classical correlation. Although no quantum correlation exists in this steady state, the strong classical correlation provides Bob with partial information about atom A, thereby enhancing his prediction accuracy for the measurement outcomes and leading to the observed reduction in the entropic uncertainty. Additionally, we explore the dynamics of the system’s purity. The results show a clear negative correlation, indicating that the reduction in entropic uncertainty is directly attributable to the purification of the system effected by the feedback control. In conclusion, this study establishes quantum feedback control as an effective theoretical protocol for suppressing the entropic uncertainty in realistic noisy environments. It provides a viable pathway for manipulating quantum uncertainty to enhance the robustness and performance of quantum information processing tasks.
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The neutron total cross section is fundamental nuclear data crucial for the design of nuclear energy systems and research in nuclear physics. For graphite, an important reactor moderator, significant discrepancies exist among the major evaluated nuclear data libraries concerning its high-energy neutron total cross section, particularly in the resonance structures and the regions above 20 MeV. These uncertainties constrain the precise design of advanced nuclear systems. To resolve these controversies and provide benchmark experimental data, this study performed a high-accuracy measurement of the neutron total cross section of natural carbon from 0.3 eV to 50 MeV using the transmission method combined with the time-of-flight (TOF) technique. The experiment was conducted at the back-streaming white neutrons (Back-n) at the China Spallation Neutron Source (CSNS), utilizing the NTOX spectrometer equipped with a multi-cell fission chamber. The neutron emission time (t0) was precisely calibrated using the prompt γ-flash from the spallation reaction. The 77-meter flight path was accurately calibrated using the known standard fission resonance peaks of 235U at 8.774 eV, 12.386 eV, and 19.288 eV. For the energy region above 100 keV, a Bayesian iterative algorithm was applied to unfold the double-bunch problem, effectively resolving the overlap of TOF spectra from neutrons produced in different beam bunches. The experimental results show excellent agreement with the ENDF/B-Ⅷ.1 evaluation and existing experimental data in the EXFOR database within the 0.3 eV-100 keV region. Owing to the high statistical accuracy, approximately 97.6% of the data points have statistical uncertainties of less than 1%, with the vast majority of total uncertainties better than 2%, significantly reducing the uncertainty level in this energy region. In the 100 keV– 50 MeV energy range, the data align with the overall trends observed in mainstream evaluation databases. No significant resonance effect was detected at 4.93 MeV, providing high-quality reference data for clarifying the resonance structure at this energy point. Systematically evaluated data above 20 MeV are currently only available in JENDL-5. The measurement results of this work provide indispensable high-quality experimental data to fill the high-energy data gap and to drive updates of the relevant evaluated libraries. This study not only provides critical benchmark data for the international nuclear data re-evaluation, especially for the CENDL-3.2 library which currently lacks complete data for natural carbon, but also systematically validates the methodological reliability of obtaining wide-energy-range, high-precision neutron total cross section data at the CSNS Back-n beamline.
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Accurate prediction of the state of health (SOH) of lithium-ion batteries in electric vehicles is crucial for ensuring the safety of drivers and passengers, optimizing battery management systems (BMS), and extending battery life. Reliable SOH prediction underpins essential BMS functions, including charge–discharge control, remaining useful life (RUL) prediction, and fault diagnosis. However, existing data-driven methods still face two long-standing challenges. First, most models rely excessively on a large number of handcrafted health features derived from voltage, current, and capacity data, resulting in feature redundancy and low computational efficiency. Second, SOH-related time series exhibit strong nonlinearity and non-stationarity, causing traditional neural networks to suffer from prediction drift, instability, and performance degradation under varying conditions. To address the first challenge, we propose a lightweight health feature selection mechanism that combines incremental capacity analysis (ICA) with correlation analysis to automatically identify compact and physically meaningful degradation features. Only four key health features that are highly correlated with capacity fading are selected, which effectively reduces model complexity and computational cost while maintaining high SOH prediction accuracy. To overcome the second challenge, we develop a hybrid neural network model (KanFormer) integrating the Kolmogorov–Arnold (KAN) representation theory with a Transformer-based temporal modeling framework for accurate and robust SOH prediction. Specifically, the proposed KanFormer framework consists of three hierarchical modules: (1) the local feature extraction module, which leverages the smooth interpolation capability of KAN to capture fine-grained degradation characteristics from voltage–capacity and incremental capacity (IC) curves, modeling local nonlinear behaviors in the degradation process; (2) the global feature extraction module, which employs a multi-head Transformer encoder to learn long-range dependencies and cross-scale temporal relationships, enabling the joint modeling of short-term dynamics and long-term aging evolution; and (3) the prediction output module, which uses nonlinear KAN layers to adaptively fuse local and global representations, producing numerically stable and highly accurate SOH prediction results. By combining the mathematical expressiveness of KAN with the temporal reasoning capability of the Transformer, KanFormer effectively mitigates prediction drift and oscillations induced by data nonlinearity and non-stationarity. Compared with conventional deep-learning models, the proposed method improves training efficiency by 15.32%. Experimental validation on three publicly available battery-aging datasets—Michigan Formation, HNEI, and NASA—demonstrates its superior performance, achieving MSE = 0.0045, MAE = 0.041, R2 = 0.978 on the Michigan dataset, MSE = 0.00055, MAE = 0.0175, R2 = 0.996 on the HNEI dataset, and MSE = 0.0056, MAE = 0.017, R2 = 0.984 on the NASA dataset. These results substantially outperform mainstream baselines, confirming the high accuracy and robustness of KanFormer. In summary, KanFormer unifies lightweight feature selection, nonlinear functional representation, and cross-scale temporal modeling, providing a scalable and interpretable solution for high-accuracy and high-efficiency SOH prediction.
Abstract +
Based on the generalized reduced R-matrix theory, this work performs a comprehensive analysis of all available experimental data for the 6He system using the RAC (R-matrix Analysis Code). A complete set of evaluated nuclear data has been obtained for major reaction channels induced by triton beams in the energy range of 10-2 ~ 20 MeV. The evaluated integral cross sections include T(t,2n)4He, T(t,n)5He, and T(t,d)4H reactions; the differential cross sections include T(t,2n)4He, T(t,n)5He, T(t,d)4H, and T(t,t)T. The evaluation results show good agreement with experimental data and the evaluated data of ENDF/B-VIII.1. In particular, for the T(t,2n)4He reaction, the evaluated cross sections are consistent with existing experiments over the full energy range, and a resonance dominated by the 2+ level is observed near 2.9 MeV. At 1.9 MeV, where experimental measurements of both integral cross sections and angular distributions are available, the evaluation reproduces both observables well. The combined constraint of integral and differential data significantly improves the stability of R-matrix parameters and the reliability of the evaluation. Based on the global analysis of the 6He system, this work also provides supplementary cross section data for the T(t,n)5He and T(t,d)4H reactions. The results contribute to the nuclear data foundation for fusion-related reactions and lay the groundwork for future joint evaluation with the mirror 6Be system.
The datasets presented in this paper, including the ScienceDB, are openly available at https://www.doi.org/10.57760/sciencedb.j00213.00202 (Please use the private access link https://www.scidb.cn/s/7jMryq to access the dataset during the peer review process)
The datasets presented in this paper, including the ScienceDB, are openly available at https://www.doi.org/10.57760/sciencedb.j00213.00202 (Please use the private access link https://www.scidb.cn/s/7jMryq to access the dataset during the peer review process)
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