Accepted
, , Received Date: 2025-09-01
Abstract +
Due to their unique nonlinear characteristics and memory effects, memristor-based chaotic systems have become a significant focus of research. However, studies on unstable periodic orbits in memristive chaotic systems are still relatively scarce. In this work, a novel four-dimensional memristive chaotic system is constructed by introducing a trigonometric-function-based memristor to enhance a three-dimensional chaotic system. The dynamical behaviors of the system are analyzed using Lyapunov exponents, Poincaré sections, phase portraits, and time-domain plots. The proposed memristive chaotic system exhibits rich dynamical characteristics, including transient behavior, intermittent chaos, and diverse attractor dynamics under parameter variations. To overcome the limitations of the variational method in finding reliable initial guesses for unstable periodic orbits, an innovative optimization strategy that utilizes the physical properties of trigonometric functions is proposed. Integrated with symbolic dynamics, this strategy can quickly obtain robust initial guesses for unstable periodic orbits within specific intervals. Furthermore, it enables these guesses to migrate into other regions of the attractor, ultimately achieving full coverage of the attractor's unstable periodic orbits. After a systematic analysis of the unstable periodic orbits in the new system, the adaptive backstepping method is employed to control the stability of the known unstable periodic orbits, namely 320 and 0*1*3. The pseudorandom sequences generated by the novel memristive chaotic system successfully passes the NIST suite, with all test items yielding P-values greater than 0.01, which confirms their excellent pseudo-random properties. Using this system for image encryption results in a key space of 10120, significantly enhancing the key space and key sensitivity of the algorithm. The encryption process begins with cross-plane scrambling operations among the RGB color channels for initial pixel processing, followed by intra-plane scrambling to further disrupt the pixel arrangement. XOR operations are then employed for pixel value diffusion. The algorithm exhibits outstanding resistance to differential attacks, with average NPCR and UACI values reaching 99.6041% and 33.4933%, respectively. Comprehensive security analyses, including histogram analysis, correlation analysis, resistance to cropping attacks, and runtime evaluation, verify that the proposed encryption scheme not only possesses strong security capabilities but also maintains high computational efficiency, making it highly suitable for practical image encryption applications. Finally, the realizability of the system is verified by utilizing a DSP circuit.
, , Received Date: 2025-09-08
Abstract +
Dielectric strength (Er) is a critical factor in screening and evaluating SF6 replacement gas. The traditional experimental methods of measuring Er are not only extremely time-consuming but also very costly. In this work, an Er prediction model for SF6 replacement gases is constructed using machine learning methods. First, an exhaustive literature survey is performed to collect 88 high-quality experimental Er values. Second, a total of 32 insightful microscopic descriptors are accurately calculated for each compound using density functional theory, including both global parameters and molecular electrostatic potential parameters. Furthermore, five state-of-the-art machine learning algorithms, which have been carefully modified through five-fold cross-validation and hyperparameter optimization, are used to train and test the 88 experimental Er data and their relevant microscopic descriptors. Finally, the result shows that the Ada Boost regression model exhibits superior predictive performance, with a coefficient of determination of 0.90, a mean absolute error of 0.17, and a root mean square error of 0.18. Moreover, Shapley Additive exPlanations analysis is used to reveal the correlation between the microscopic descriptors and Er. The results indicate that polarizability is the predominant factor significantly affecting Er, which accounts for as high as 17.3%, followed by the molecular weight (14.1%). Specifically, molecules with high α are more prone to deformation under the action of an electric field, and their electron clouds are more likely to be polarized, which has a positive correlation with Er. There is an approximately positive correlation between the molecular weight and the Er of gases. To verify the reliability of the Ada Boost regression model for Er prediction, the Er of SF6 and six known environmentally friendly replacement gases are tested within an absolute error of 0.02–0.33. This study provides a feasible method for accelerating the search for SF6 replacement gases.
, , Received Date: 2025-09-08
Abstract +
In this paper, a novel scheme is proposed and experimentally demonstrated. It is based on a directly modulated laser (DML) and all-optical mode-locking for generating tunable microwave frequency combs (MFCs). Theoretical analysis reveals that harmonic or rational harmonic mode-locking can be achieved by adjusting the parameters of the fiber ring cavity, which enables the generation of MFCs with adjustable comb spacing. Based on this, experimental verification shows that the DML can be driven to exhibit various typical dynamical states under sinusoidal modulation with different frequencies and amplitudes. These states serve as seeding signals that subsequently undergo all-optical mode-locking within the ring laser cavity, resulting in the generation of MFCs. The bandwidths of the MFCs are 13, 15, 19.5, 19.8, and 22 GHz, respectively, all of which satisfy the ±5 dB flatness criterion. A continuously tunable comb-spacing range of 200 MHz to 3 GHz is attained through the effective combination of the DML and all-optical mode-locking. The single-sideband (SSB) phase noise of the first comb line remains below –100 dBc/Hz at a 10 kHz offset. Theoretical analysis and experimental results demonstrate that the modulated signals of the proposed scheme support flexible parameter tuning over a wide range. Furthermore, the generated MFCs have remarkable advantages in flatness, bandwidth, and tunability.
, , Received Date: 2025-09-16
Abstract +
A multi-unit thermoradiative device (TRD) is used for automotive exhaust waste heat recovery in this study. A coupled model integrating radiative heat transfer, current-voltage characteristics, and fluid heat exchange is established. Based on Fourier’s law of heat conduction and thermal radiative transfer theory, the energy constraint equations, total power output, and conversion efficiency of the system are derived. The variations of exhaust gas temperature, TRD operating temperature, and ambient temperature with unit number are obtained through numerical simulations, thereby revealing the regulation mechanisms of voltage and semiconductor bandgap on energy conversion performance. Results show that the temperatures of the exhaust gas and the hot side of the TRD decrease with the increase of unit number and also decreases with the increase of current at the same unit position. In contrast, the cold side of the TRD and the ambient temperature rise due to heat accumulation and cascading heating effects, and further increase with current rising, reflecting the coupling between electrical output and thermal processes. Increasing the voltage suppresses radiative recombination, leading to reduced current, while the electrical power reaches a maximum at a specific operating point. The total heat flux is reduced as voltage increases. Because of the nonlinear relationship between electrical power and heat flux, efficiency attains an optimum value at a specific voltage, achieving a balance between electrical output and heat dissipation. This study demonstrates that the locally optimal power reaches a global maximum value of 170.45 W at a bandgap of 0.06 eV, whereas the locally optimal efficiency increases monotonically with the increase of bandgap before saturating gradually. To address the inherent trade-off between power and efficiency, a target function Z defined as the product of locally optimal power and efficiency is introduced. Numerical analysis reveals that Z attains its maximum value of 49.74 W at a bandgap of 0.105 eV, effectively balancing the competing objectives of power output and energy conversion efficiency. This study provides a new method for optimizing the performance of thermoelectric systems.
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Abstract +
The Perovskite Li3xLa2/3-xTiO3 (LLTO) has been investigated as a Li-ion solid electrolyte material and has attracted significant attention due to its wide operating voltage range. Polycrystalline and grain boundaries (GBs) are a common structural motif found in ceramic oxides. So, GBs can have a significant impact on the material properties. Here, we presented a molecular dynamics (MD) study that quantifies the effect of LLTO GBs on Li-ion transport. We examined six types of LLTO GBs, including P- Σ5(210), P-Σ5(310), P-Σ13(510) in the Li-poor phase and R-Σ5(210), R- Σ5(310), R-Σ13(510) in the Li-rich phase. We also consider LLTO bulk for comparison. The results show that the grain boundary formation energies of the six GBs are all below 1.30 J/m2, indicating the presence of a high concentration of GBs in polycrystalline LLTO. It is likely to find a highest concentration of Σ5(210) GB due to its lowest formation energy (1.00 J/m2 for P-Σ5(210) and 0.89 J/m2 for R-Σ5(210)). Compared with the bulk LLTO, Li+ in the six GBs exhibits a lower mean squared displacement (MSD), a smaller migration energy barrier and a lower ionic conductivity. These results confirm that LLTO GBs hinder Li+ transport. For bulk LLTO, the Li+ migration barrier is determined to be 0.30 eV (Li-poor phase) and 0.26 eV (Li-rich phase). In comparison, the migration barrier of LLTO GBs exhibits a slight decrease, ranging from 0.32 to 0.37 eV (Li-poor phase) and 0.27 to 0.31 eV (Li-rich phase). The computed Li-ion conductivities of the six GBs are 1 to 2 orders of magnitude lower than those of the corresponding bulk counterparts. Among the six GBs, P-Σ13(510) exhibits the highest Li+ conductivity of 4.76 × 10-5 S/cm in the Li-poor phase, whereas R-Σ5(310) shows the maximum Li+ conductivity of 1.31 × 10-3 S/cm in the Li-rich phase. Furthermore, the peak Li+ conductivity in the Li- rich phase is substantially higher than that in the Li-poor phase. In addition, Li+ transport perpendicular to the GB (i.e., from grain to grain) is more hindered relative to transport along the GB. Nevertheless, the Li+ diffusion can be improved by increasing the Li content within the GB region. The Li+ diffusion maps can be visualized by analyzing the Li+ trajectories of the MD simulations. We found that Li+ transport is restricted to the GB region first, then gradually turns to the bulk region, and finally forms a two-dimensional diffusion path similar to that of the LLTO bulk.Furthermore, the Li+ diffusion strongly depends on the distribution of O ions in LLTO GBs. For example, in the Li-poor-phase P-Σ5(310) GB, the number of O ions in the GB region is greater than that in the bulk region, which indicates a stronger Li-O attractive interaction in the GB region and so hinders Li+ transport towards the bulk region. Collectively, these atomic- scale insights deepen our understanding of LLTO GBs and their influence on Li+ transport.
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Abstract +
The advancement of spintronics technology hinges on the efficient generation and control of spin-polarized currents, yet conventional approaches relying on magnetic materials are prone to external magnetic interference, limiting their practical applications. Andreev reflection spectroscopy has emerged as a powerful tool for probing material-specific properties such as spin polarization (P) and superconducting gaps (∆), but its theoretical foundations often rest on simplified models that assume isotropic interface scattering. This assumption neglects the ubiquitous spin-dependent anisotropic scattering observed in real-world interfaces, which can lead to significant misinterpretations of intrinsic material characteristics. To address this gap, our study aims to develop a comprehensive theoretical framework that incorporates anisotropic spin scattering effects, enabling a systematic investigation of how interface anisotropy modulates Andreev reflection spectra. This work seeks to resolve precision issues in the characterization of spin-polarized materials, particularly for emerging quantum systems like topological insulators, where accurate measurement of spin polarization is crucial but challenging.
Methodologically, we build upon the foundational Blonder-Tinkham-Klapwijk (BTK) model and its extension by Chen-Tesanovic-Chien (CTC) by introducing spin-dependent scattering parameters Z↑ and Z↓ to describe the distinct interface scattering strengths for spin-up and spin-down electrons. This allows us to construct a unified theoretical model applicable to a wide range of interface systems, from normal metals (P= 0) to half-metals (P= ±1). We employ detailed numerical calculations and three-dimensional image analysis to simulate the differential conductance spectra under varying conditions of spin polarization and interface anisotropy. Specifically, the model accounts for the probability amplitudes of Andreev reflection and normal reflection by solving the Bogoliubov-de Gennes equations with generalized boundary conditions, and the current formulae are derived by integrating over energy-dependent transmission probabilities, incorporating a judgment function to handle dominant spin channels realistically.
Our key results reveal several important physical insights. For non-magnetic metals (P=0), interface anisotropic scattering (e.g., Z↑ ≠ Z↓) can induce highly spin-polarized currents through a transmission spin polarization mechanism, as demonstrated by the suppression of Andreev reflection and the reduction in normalized differential conductance within the superconducting gap (e.g., decreasing from 2 to 0 as Z↑ increases while Z↓ is fixed). This effect is sensitive even to small values of Z (around 0.25–0.5), highlighting the importance of precise interface engineering. For magnetic materials with positive spin polarization (P＞0), such as those with P=0.25, anisotropy at the interface non-linearly modulates the current polarization; for instance, when Z↓ is fixed at 0.5, increasing initially enhances Andreev reflection due to balanced spin transmission but suppresses it beyond a critical point, illustrating the tunability of polarization rates. Conversely, for negatively polarized materials (P＜0), the spectra exhibit distinct features—such as the absence of peaks under certain conditions—enabling a novel method to determine the sign of P by comparing differential conductance behaviors. Experimental validation using pure Co films shows close agreement with our model, confirming its accuracy and the minor anisotropy in typical magnetic interfaces.
In conclusion, this theoretical framework not only refines the understanding of Andreev reflection spectroscopy by accounting for anisotropic scattering but also provides practical tools for characterizing quantum materials and designing spintronic devices. It offers new pathways for developing interference-resistant spin sources based on non-magnetic materials and optimizes interface engineering in magnetoresistance devices. Future work will focus on experimental extensions to low-dimensional systems and algorithmic improvements for parameter analysis, further bridging theory and application in quantum information science.
Methodologically, we build upon the foundational Blonder-Tinkham-Klapwijk (BTK) model and its extension by Chen-Tesanovic-Chien (CTC) by introducing spin-dependent scattering parameters Z↑ and Z↓ to describe the distinct interface scattering strengths for spin-up and spin-down electrons. This allows us to construct a unified theoretical model applicable to a wide range of interface systems, from normal metals (P= 0) to half-metals (P= ±1). We employ detailed numerical calculations and three-dimensional image analysis to simulate the differential conductance spectra under varying conditions of spin polarization and interface anisotropy. Specifically, the model accounts for the probability amplitudes of Andreev reflection and normal reflection by solving the Bogoliubov-de Gennes equations with generalized boundary conditions, and the current formulae are derived by integrating over energy-dependent transmission probabilities, incorporating a judgment function to handle dominant spin channels realistically.
Our key results reveal several important physical insights. For non-magnetic metals (P=0), interface anisotropic scattering (e.g., Z↑ ≠ Z↓) can induce highly spin-polarized currents through a transmission spin polarization mechanism, as demonstrated by the suppression of Andreev reflection and the reduction in normalized differential conductance within the superconducting gap (e.g., decreasing from 2 to 0 as Z↑ increases while Z↓ is fixed). This effect is sensitive even to small values of Z (around 0.25–0.5), highlighting the importance of precise interface engineering. For magnetic materials with positive spin polarization (P＞0), such as those with P=0.25, anisotropy at the interface non-linearly modulates the current polarization; for instance, when Z↓ is fixed at 0.5, increasing initially enhances Andreev reflection due to balanced spin transmission but suppresses it beyond a critical point, illustrating the tunability of polarization rates. Conversely, for negatively polarized materials (P＜0), the spectra exhibit distinct features—such as the absence of peaks under certain conditions—enabling a novel method to determine the sign of P by comparing differential conductance behaviors. Experimental validation using pure Co films shows close agreement with our model, confirming its accuracy and the minor anisotropy in typical magnetic interfaces.
In conclusion, this theoretical framework not only refines the understanding of Andreev reflection spectroscopy by accounting for anisotropic scattering but also provides practical tools for characterizing quantum materials and designing spintronic devices. It offers new pathways for developing interference-resistant spin sources based on non-magnetic materials and optimizes interface engineering in magnetoresistance devices. Future work will focus on experimental extensions to low-dimensional systems and algorithmic improvements for parameter analysis, further bridging theory and application in quantum information science.
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Abstract +
The precision determination of Rydberg states transition frequency is important for quantum sensing and computation. In this study, we prepare 133Cs Rydberg states of nD5/2, nD3/2, and nS1/2 by using a cascaded twophoton excitation scheme with counter-propagating 852 nm probe light and 509 nm coupling light in a cesium vapor cell. Furthermore, by introducing a microwave field to couple adjacent Rydberg states, we obtained the transition spectra between the Rydberg states based on electromagnetically induced transparency (EIT) and Autler-Townes (AT) splitting effects. Frequency calibration of the sampled data points collected by the oscilloscope was achieved using either the fine-structure splitting interval between nD5/2 and nD3/2 Rydberg states for n2D5/2→(n+1) 2P3/2 and n2D3/2 → (n+1) 2P1/2 transitions, or using a second EIT signal generated by an acousto-optic modulator frequency-shifted 852 nm laser for n2S1/2→n2P1/2 transitions. To reduce systematic errors, we employed a microwave frequency detuning method, calibrating the AT splitting intervals at different frequencies, and measured the resonant frequencies of three typical cesium Rydberg state transitions: n2D5/2→(n+1) 2P3/2 (n=39-53), n2D3/2→(n+1) 2P1/2 (n=39-47), and n2S1/2→n2P1/2 (n=59-62). Characterized by experimental simplicity, high precision, and broad applicability, this method is suitable for high-precision measurements of alkali metal Rydberg transition frequencies. Deviations between the experimentally measured transition frequencies and the theoretical values from the open-source Python library ARC (Alkali Rydberg Calculator) were all less than 850 kHz, with an average deviation of 449 kHz. Through the analysis of the influences of various physical effects such as Zeeman effect on the measurement of Rydberg state transition frequencies, the obtained measurement uncertainty is 410 kHz. This small deviation demonstrates the exceptional capability and reliability of the EIT-AT splitting method in overcoming environmental interference and achieving MHz-level precision measurements of Rydberg state transition frequencies. The results provide important data for Rydberg atom precision spectroscopy.
Abstract +
In edge computing scenarios, response speed, compactness, and power efficiency have become critical challenges for visual systems. Conventional vision architectures that separate sensing and computation suffer from high latency, excessive power consumption, and potential privacy leakage caused by data transmission. To address these issues, vision chips inspired by the human visual system have emerged as a promising solution. By integrating image acquisition and information processing within a single hardware platform, such chips enable a sensing–computation co-processing paradigm, supporting efficient visual perception and computation directly at the edge. Developing high-speed vision chips is an inherently interdisciplinary task that bridges physics, electronics, and information science. It addresses critical issues across device fabrication, circuit design, and intelligent algorithm integration. This paper systematically reviews recent advances in the core components of high-speed vision chips.
For high-speed sensor devices, the paper analyzes the physical mechanisms, structural innovations, and performance limitations of CMOS image sensors (CIS), dynamic vision sensors (DVS), and single-photon image sensors. High-speed CIS devices enhance temporal response by optimizing two fundamental aspects: charge transfer velocity and transfer path length. Gradient doping is employed to induce high-speed drift motion during charge transfer, while structural optimization based on physical device modeling shortens the transfer path, thereby enabling fast response. In contrast, DVS perform event-triggered readout when light intensity changes exceed a predefined threshold. This event-driven mechanism effectively removes static redundant information, producing only spike-based data that reflect brightness changes, achieving low latency and high temporal resolution. For single-photon detection, quantum image sensors based on CIS investigate noise origins and physical mechanisms, achieving ultra-low noise and extremely high conversion gain. Image sensors employing single-photon avalanche diodes (SPADs) leverage the avalanche effect to directly convert incident photons into pulse outputs, realizing high-speed and high-sensitivity single-photon detection. Furthermore, electric-field modulation enhances photogenerated charge collection and reduces temporal jitter, thereby improving timing precision in SPADs.
In terms of readout circuits, this paper reviews the architectures and optimization strategies for high-speed analog-to-digital converters (ADCs), address-event encoding, and time-correlated single-photon counting. To enhance conversion efficiency while minimizing chip area and power consumption, various ADC architectures have been developed. The successive approximation register (SAR) ADC has become a foundational solution owing to its high integration and low power characteristics. Hybrid architectures such as SAR/single-slope (SS) and pipeline–SAR combine the strengths of different schemes, effectively overcoming the area–resolution trade-offs inherent in conventional SAR ADCs. For DVS sensors, the address-event representation (AER) readout mechanism performs real-time detection of brightness variations and outputs them as asynchronous events, greatly enhancing image processing throughput while reducing storage and transmission demands. In SPAD-based sensors, on-chip integration of counting and histogram computation effectively alleviates the data throughput bottleneck associated with large-scale single-photon detection. These readout strategies, each tailored to the characteristics of their corresponding sensing mechanisms, collectively improve data conversion and transmission efficiency in high-speed imaging scenarios.
For intelligent processing, the primary objective is to efficiently extract information from sensor data and enable algorithmic intelligence. This process generally involves two stages: the reconstruction stage focuses on recovering high-quality image sequences from sparse spike streams, while the intelligent processing stage achieves high-speed semantic understanding through real-valued or spike-based computational architectures. By deeply integrating reconstruction and cognition at both algorithmic and hardware levels, end-to-end intelligent vision systems can simultaneously achieve high speed, low power consumption, and high accuracy. With ongoing technological convergence, multimodal vision chips integrating CIS, DVS, and SPAD architectures combine the advantages of different sensor modalities, offering more comprehensive perceptual capabilities for next-generation machine vision systems. Looking ahead, the continuous advancement of semiconductor fabrication technologies and novel materials, combined with the deep integration of multimodal sensing and heterogeneous computing paradigms, is expected to drive the evolution of high-performance, low-power, and intelligent vision chips.
For high-speed sensor devices, the paper analyzes the physical mechanisms, structural innovations, and performance limitations of CMOS image sensors (CIS), dynamic vision sensors (DVS), and single-photon image sensors. High-speed CIS devices enhance temporal response by optimizing two fundamental aspects: charge transfer velocity and transfer path length. Gradient doping is employed to induce high-speed drift motion during charge transfer, while structural optimization based on physical device modeling shortens the transfer path, thereby enabling fast response. In contrast, DVS perform event-triggered readout when light intensity changes exceed a predefined threshold. This event-driven mechanism effectively removes static redundant information, producing only spike-based data that reflect brightness changes, achieving low latency and high temporal resolution. For single-photon detection, quantum image sensors based on CIS investigate noise origins and physical mechanisms, achieving ultra-low noise and extremely high conversion gain. Image sensors employing single-photon avalanche diodes (SPADs) leverage the avalanche effect to directly convert incident photons into pulse outputs, realizing high-speed and high-sensitivity single-photon detection. Furthermore, electric-field modulation enhances photogenerated charge collection and reduces temporal jitter, thereby improving timing precision in SPADs.
In terms of readout circuits, this paper reviews the architectures and optimization strategies for high-speed analog-to-digital converters (ADCs), address-event encoding, and time-correlated single-photon counting. To enhance conversion efficiency while minimizing chip area and power consumption, various ADC architectures have been developed. The successive approximation register (SAR) ADC has become a foundational solution owing to its high integration and low power characteristics. Hybrid architectures such as SAR/single-slope (SS) and pipeline–SAR combine the strengths of different schemes, effectively overcoming the area–resolution trade-offs inherent in conventional SAR ADCs. For DVS sensors, the address-event representation (AER) readout mechanism performs real-time detection of brightness variations and outputs them as asynchronous events, greatly enhancing image processing throughput while reducing storage and transmission demands. In SPAD-based sensors, on-chip integration of counting and histogram computation effectively alleviates the data throughput bottleneck associated with large-scale single-photon detection. These readout strategies, each tailored to the characteristics of their corresponding sensing mechanisms, collectively improve data conversion and transmission efficiency in high-speed imaging scenarios.
For intelligent processing, the primary objective is to efficiently extract information from sensor data and enable algorithmic intelligence. This process generally involves two stages: the reconstruction stage focuses on recovering high-quality image sequences from sparse spike streams, while the intelligent processing stage achieves high-speed semantic understanding through real-valued or spike-based computational architectures. By deeply integrating reconstruction and cognition at both algorithmic and hardware levels, end-to-end intelligent vision systems can simultaneously achieve high speed, low power consumption, and high accuracy. With ongoing technological convergence, multimodal vision chips integrating CIS, DVS, and SPAD architectures combine the advantages of different sensor modalities, offering more comprehensive perceptual capabilities for next-generation machine vision systems. Looking ahead, the continuous advancement of semiconductor fabrication technologies and novel materials, combined with the deep integration of multimodal sensing and heterogeneous computing paradigms, is expected to drive the evolution of high-performance, low-power, and intelligent vision chips.
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Abstract +
With mature fabrication technologies and tunable spin relaxation, IIIV semiconductor two-dimensional quantum structures serve as a preferred material system for developing spintronic devices. This paper reviews the progress in manipulating spin-orbit coupling and spin relaxation in two-dimensional electron gas and two-dimensional hole gas systems via structural design, electric fields, and strain. By combining time-resolved magneto-optical spectroscopy with magnetotransport measurements, we analyze the synergistic modulation of Rashba and Dresselhaus effects to optimize the spin lifetime and highlight the distinct physical pathways for constructing long-lived SU(2) spin states in zinc-blende GaAs and wurtzite GaN heterostructures. For zinc-blende GaAs quantum wells, we discuss the realization of the persistent spin helix state by balancing the Rashba and Dresselhaus effects through structural design and electric field control. In contrast, for wurtzite GaN systems, we reveal that the Rashba and Dresselhaus effects inherently share the same symmetry form, allowing for the direct cancellation of effective magnetic fields to achieve a robust SU(2) electronic state. Ultimately, this comprehensive physical picture provides a scientific basis for material selection and architecture design in future high-performance spintronic devices.
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Abstract +
The development of rare-earth permanent magnets that combine high maximum energy product with high Curie temperature has emerged as a central challenge in the field of applied magnets. Sm-Fe-N magnets exhibit a theoretical maximum energy product comparable to Nd-Fe-B (~59 MGOe), along with a higher Curie temperature and greater magnetocrystalline anisotropy. Furthermore, Sm-Fe-N magnets do not rely on scarce heavy rare-earth elements and are immune to price fluctuations of neodymium. These advantages position them as a highly promising rareearth permanent magnet material, offering significant potential for achieving both high stability and coercivity. In this work, using complementary neutron diffraction, 57Fe Mössbauer spectroscopy, highfield magnetic measurements, and X-ray magnetic circular dichroism (XMCD), we systematically investigate nitrogen content and site occupancy, magnetic structure and hyperfine fields, as well as the Sm/Fe spin-orbit coupling in Sm-Fe-N. The specialized sample preparation and absorption correction methods enable the acquisition of high-quality neutron diffraction patterns for Sm2Fe17 and its nitrides. The result reveals that N atoms preferentially occupy the 9e interstitial sites, forming the fully nitrided Sm2Fe17N3. Combined with 57Fe Mössbauer spectroscopy analysis, it is found that the nitridation reaction significantly enhances both the Curie temperature and the ground-state Fe magnetic moment, thereby improving the room-temperature magnetic properties. Furthermore, high-field magnetic measurements reveal that the anisotropy field of Sm2Fe17N3 reaches 22.6 T at room temperature and exceeds 50 T at 2 K. This confirmsthe ultra-strong magnetocrystalline anisotropy of Sm2Fe17N3, demonstrating its significant potential for achieving high coercivity. XMCD measurements demonstrate that the magnetism of Sm is dominated by its orbital magnetic moment, establishing its strong spin-orbit coupling as the physical origin of the ultra-strong magnetocrystalline anisotropy. In contrast, the orbital magnetic moment of Fe is largely quenched, resulting in a magnetic moment that is primarily spin-derived. This work clarifies the intrinsic relationship between the content and site occupancy of interstitial nitrogen atoms and the magnetocrystalline anisotropy, and reveals the spinorbit coupling mechanism involving rare-earth Sm and Fe. These findings provide an important theoretical basis for the design of high-performance permanent magnet materials.
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Abstract +
Excited-state intramolecular proton transfer (ESIPT) is an important photophysical process which have wide applications in fluorescent probes, molecular switches, and organic light-emitting materials. The molecule with ESIPT is highly sensitive to its surroundings, such as solvents, and exhibits fruitful fluorescence properties. Theoretical study on the microscopic mechanism of proton transfer in regulating the fluorescence properties of organic molecules is very important. Recently, Yang et al. [Yang G, Li Y, He L, et al. 2024 Microchem. J. 198 110044] designed a fluorescent probe (FZ) based on ESIPT. They observed bimodal emission, strong long-wavelength emission and weak short-wavelength emission in low-polar, highly polar non-protic and highly polar protic solvents, respectively. To reveal the microscopic mechanism of these fluorescence properties, in this work, we theoretically investigate the proton transfer process of FZ molecule in various solvents including toluene, dichloromethane, ethanol, and dimethyl sulfoxide (DMSO) by using density functional theory and time-dependent density functional theory. Based on polarizable continuum model with the integral equation formalism variant (IEFPCM), the optimized structures are obtained and potential energy curves for proton transfer are scanned employing the CAM-B3LYP functional with Grimme’s D3 dispersion and 6-31+g(d,p)/6- 311+g(d,p) basis. Importantly, the excited-state dynamics behaviors of four intermolecular hydrogen-bonding systems in ethanol solvent are explored by using super-molecular model. The structures, hydrogen-bonding energies, and interaction region indicator (IRI) analysis show that the strength of the intramolecular hydrogen bond significantly enhances upon photo excitation. The potential energy curves indicate that FZ molecules tend to undergo the ESIPT process in all the solvents. The barriers of proton transfer decrease as the solvent polarity increases. As a result, a dual emission and a strong keto (K*) emission were observed in dichloromethane (low-polar) and DMSO (highly polar non-protic), respectively. In ethanol (highly polar protic), the excited-state behaviors of the four super-molecular systems (FZ-OH1, FZ-OH2, FZ-OH3, FZ-OH4) are quite different. In FZ-OH1, ESIPT cannot occur because enol (E*) is more stable than K*. As a result, FZ-OH1 can produce the E* emission. In contrast, ESIPT can take place almost barrierlessly in FZ-OH2, resulting in the K* emission. Interestingly, FZ-OH3 could undergo stepwise excitedstate double protons transfer (ESDPT) between FZ and ethanol molecules, resulting in a dark state of K*. Hole-electron analysis demonstrates that it is the twisted intramolecular charge transfer (TICT) that quenches the fluorescence of K*. Therefore, the observed weak short-wavelength emission in ethanol could ascribe to the E* emission of FZ-OH3. Our work is of great significance in understanding and predicting the photophysical properties of organic molecules in solvents and provides a useful theoretical basis for designing and developing ESIPT-based functional materials.
, , Received Date: 2025-09-23
Abstract +
, , Received Date: 2025-09-07
Abstract +
In order to develop a rapid and cost-effective new method to produce periodic microstructures on solid surfaces, and help to understand the physical mechanism of the enhancement of laser-induced breakdown spectroscopy (LIBS) signals induced by periodic surface microstructures, in this work, spherical copper powder with about 74 μm in diameter is used to imprint semispherical periodic surface microstructures on polyvinyl chloride (PVC) sheets under a pressure of 15 T. A platinum conducting layer about 100 nm in thickness is coated on the PVC surface by using a vacuum sputter coater and then nickel plates with the replicated microstructures on one surface are prepared using electroplating method. The signal enhancement effect induced by micro-structured surface in LIBS is experimentally observed and compared with that achieved by using flat surface nickel plate, the temperature and electron density of the induced plasma are measured according to Boltzmann plot method and the Stark broadening of Hα line of hydrogen. By systematically analyzing these results, it is concluded that the main physical mechanism of the signal enhancement in LIBS caused by the hemispherical periodic surface microstructure is due to the increased surface area of the sample that can be irradiated by the laser beam, leading to an increase in the mass of the ablated sample material when compared with that of a flat surface irradiated by the same laser beam. Comparative analysis is also conducted with experimental phenomena and signal enhancement mechanisms of using cylindrical periodic surface microstructures with a certain depth (20 μm diameter, 15 μm depth and 40 μm period). It is found that the depth of the microstructure helps to achieve better signal enhancement effects. This provides useful references for subsequent microstructure parameter design in the future. Finally, lead in aqueous solution samples is detected with surface-enhanced LIBS (SENLIBS) technique, while Pb I 405.78 nm line is selected as the analytical line. In comparison with flat nickel substrates, 23-fold detection sensitivity and slightly improved signal reproducibility can be achieved using nickel substrates with hemispherical periodic surface microstructures. The results indicate that nickel plates with hemispherical periodic surface microstructure show better analytical performance than flat nickel plates in elemental analysis of aqueous solution samples by SENLIBS.
, , Received Date: 2025-09-03
Abstract +
To reveal the correlation between the anisotropy of electromagnetic absorbing metastructure and the radar cross section (RCS) of its curved components, the typical anisotropic hexagonal honeycomb (HH) metastructure and isotropic sheet gyroid (SG) metastructure are systematically studied. Both conformal mapping and non-conformal mapping methods are employed for designing the conformal curved components. These designs are compared using simulation and microwave anechoic chamber testing to evaluate their RCSs. The results indicate that the RCS of isotropic sheet gyroid curved components is insensitive to design methods, exhibiting strong design method and absorbing robustness; however, the RCS of anisotropic hexagonal honeycomb curved components exhibits strong dependence on design methods. Compared with anisotropic structures, metastructures with electromagnetic isotropy have significant advantages in achieving wide-angle and robust low-scattering characteristics of curved components, with lower dependence on design and processing. This study provides important design guidance for developing high-performance radar low-scattering components.
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