Accepted
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Abstract +
Motivation : With the rapid development of the low-altitude economy, increasing attention has been paid to the radiation environment safety of low-altitude aircraft such as drones and electric vertical takeoff and landing (eVTOL) aircraft. Traditional views hold that the dense lower atmosphere is an effective barrier against cosmic radiation, but the shrinking feature sizes of modern integrated circuits (ICs) have significantly increased their susceptibility to single-event effects (SEEs). Most conventional studies have focused on the effects of particles such as neutrons and protons, while systematic evaluations of the risks induced by muons —the most abundant charged particles at sea level—remain scarce, particularly during extreme solar events. Therefore, this study quantitatively evaluates the muon-induced SEE risks of lowaltitude aircraft in different regions of China under both static cosmic ray backgrounds and Ground Level Enhancements (GLEs), aiming to provide critical insights for the operational safety of next-generation low-altitude aviation platforms.
Methods : This study employs city-specific atmospheric models and simulates atmospheric shower processes over different cities within the CORSIKA framework, yielding reliable energy spectra of lowenergy muons (10–100 MeV) across diverse regions. Drawing on electrical simulation data from other research groups, this study estimates muon-induced SEE cross sections in transistors with different process nodes, covering Bulk, FD-SOI, and FinFET processes. Subsequently, by integrating solar energetic particle (SEP) energy spectra associated with Ground Level Enhancement (GLE) events, we evaluate muoninduced SEE risks for systems of varying sizes under both static conditions (only cosmic-ray injection) and GLE event scenarios.
Results : Our results indicate that under static conditions, flight control systems (with 1 MB of memory) incorporating advanced process-node (≤ 45nm) Bulk transistors are exposed to non-negligible muon-induced SEE risks across all cities in China. In contrast, systems utilizing FD-SOI transistors can effectively alleviate such risks. For systems with large memory capacities (1 GB), irrespective of the process technology employed, redundancy and other radiation-hardening measures must be adopted. Regarding GLE events, this study innovatively introduces the concept of muon hazard levels to evaluate regional variations in risk. Specifically, during GLEs, the aggravation of muon-induced SEE risks in mid-to-low latitude regions is negligible, whereas high-latitude regions experience a significant rise in such risk.
Methods : This study employs city-specific atmospheric models and simulates atmospheric shower processes over different cities within the CORSIKA framework, yielding reliable energy spectra of lowenergy muons (10–100 MeV) across diverse regions. Drawing on electrical simulation data from other research groups, this study estimates muon-induced SEE cross sections in transistors with different process nodes, covering Bulk, FD-SOI, and FinFET processes. Subsequently, by integrating solar energetic particle (SEP) energy spectra associated with Ground Level Enhancement (GLE) events, we evaluate muoninduced SEE risks for systems of varying sizes under both static conditions (only cosmic-ray injection) and GLE event scenarios.
Results : Our results indicate that under static conditions, flight control systems (with 1 MB of memory) incorporating advanced process-node (≤ 45nm) Bulk transistors are exposed to non-negligible muon-induced SEE risks across all cities in China. In contrast, systems utilizing FD-SOI transistors can effectively alleviate such risks. For systems with large memory capacities (1 GB), irrespective of the process technology employed, redundancy and other radiation-hardening measures must be adopted. Regarding GLE events, this study innovatively introduces the concept of muon hazard levels to evaluate regional variations in risk. Specifically, during GLEs, the aggravation of muon-induced SEE risks in mid-to-low latitude regions is negligible, whereas high-latitude regions experience a significant rise in such risk.
Abstract +
Non-Hermitian physics has emerged as a rapidly advancing field of research, revealing a range of novel phenomena and potential applications. Traditional non-Hermitian Hamiltonians are typically simulated by constructing asymmetric couplings or by introducing dissipation and gain to realize non-Hermitian systems. The quadratic bosonic system (QBS) with squeezing interaction is intrinsically Hermitian; however, its dynamical evolution matrix in both real and momentum spaces is non-Hermitian. Based on this, applying a field-operator transformation $\{\hat{x},\hat{p}\}$ to the dynamical evolution matrix yields quadrature nonreciprocal transmission between the $\hat{x}$ and $\hat{p}$ operators. This nonreciprocal characteristic can be utilized in signal amplifiers. On the other hand, within the Bogoliubov–de Gennes framework in momentum space, one can observe non-Hermitian topological phenomena such as point-gap topology and the non-Hermitian skin effect, both induced by spectra with nonzero winding numbers. Additionally, QBS can be employed to realize non-Hermitian Aharonov–Bohm cages and to extend non-Bloch band theory. Previous studies in non-Hermitian physics have largely concentrated on classical systems. The influence of non-Hermitian properties on quantum effects remains a key issue awaiting exploration and has evolved into a research direction at the interface of non-Hermitian and quantum physics. In QBS, squeezing interactions without dissipation cause the dynamical evolution of the system to display effective non-Hermitian characteristics and induce quantum correlation effects, such as quantum entanglement. Recent studies have shown that the non-Hermitian exceptional points in QBS can alter squeezing dynamics and entanglement dynamics. Therefore, such systems not only offer a natural platform for realizing quantum non-Hermitian dynamics but also constitute an important basis for investigating the relationship between non-Hermitian dynamics and quantum effects, as well as for achieving quantum control based on non-Hermitian properties. Future research may further focus on elucidating the connections between non-Hermitian dynamics and quantum effects in QBS, which is expected to serve as a bridge linking non-Hermitian dynamics and quantum effects.
Abstract +
Bistable switching materials that enable reversible transitions between distinct stable states have emerged as a transformative platform for next-generation information technologies, optoelectronics, and quantum control. The application of high pressure serves as a powerful and precisely tunable stimulus for manipulating crystal structures, electronic configurations, and crystal fields, thereby enabling deterministic switching of diverse physical properties. This review systematically examines recent advances in pressure-induced bistable transitions, encompassing nonlinear optical switching via symmetry breaking, luminescence and color transitions mediated by bandgap engineering, insulator-metal transitions driven by electronic correlation effects, semiconductor carrier-type inversion, and spin crossover phenomena. Through comprehensive analysis integrating in situ high-pressure characterization techniques including synchrotron X-ray diffraction, vibrational spectroscopy, spatially resolved photoluminescence mapping, nonlinear optical microscopy, and transport measurements, we establish quantitative correlations between structural evolution, local coordination changes, and macroscopic switching responses. These multimodal investigations reveal fundamental mechanisms governing bistable transitions, particularly highlighting the critical roles of pressure-controlled symmetry breaking, coordination reconstruction, lone-pair stereochemical activity, and electronic correlation tuning. Notably, certain material systems exhibit extended multistate switching characteristics on complex energy landscapes, offering promising avenues for advanced applications in high-density data storage beyond conventional bistability. However, practical implementation faces significant challenges including the relatively high switching pressures required, limited reversibility in some systems, and difficulties in device integration. To solve current challenges, we proposed potential solutions including the development of diamond anvil cell-integrated micro/nanoelectrodes, fiber-optic coupled on-chip high-pressure cells, and strategies to reduce switching pressures to practical ranges. This work provides fundamental insights into the mechanisms of pressure-driven state switching while simultaneously outlining practical pathways toward realizing devices and reconfigurable optoelectronic systems. The integration of advanced in situ characterization techniques with theoretical understanding offers a robust framework for both fundamental research and technological applications of bistable switching materials under pressure.
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Abstract +
Abstract Nuclear masses are fundamental observables that reflect nuclear structure and stability, playing a key role in nuclear physics and astrophysical processes. Most existing neural network studies focus on predicting either binding energies or neutron/proton separation energies individually, with limited attention to the physical correlations between these observables. Based on the relativistic point-coupling model PCF-PK1, a physics-informed artificial neural network (ANN) was developed to systematically predict nuclear binding energies along with single- and double-neutron/proton separation energies, while preserving the physical self-consistency of the predictions. To assess the impact of incorporating separation-energy constraints, networks were trained with varying loss function weight combinations, enabling a comparison between networks without separation-energy constraints (e.g., ANN1) and those including such constraints (e.g., ANN3).
The neural network significantly improves the overall prediction accuracy of binding energies compared with the PCF-PK1 model. Without separation-energy constraints, ANN1 already achieves high precision for binding energies (RMSE ≈ 0.147 MeV) and separation energies (RMSE ≈ 0.158– 0.185 MeV). Incorporating separation-energy constraints in ANN3 results in a slight improvement in overall prediction accuracy. The binding energy predictions improve by approximately 4.6%, while the separation energy predictions increase by 8.9–12.0%. The improvement is particularly noticeable for nuclei where the deviations of ANN1 predictions from experimental values exceed 0.2 MeV. Supporting datasets are publicly accessible at the Science Data Bank (https://doi.org/10.57760/sciencedb.j00213.00239). To facilitate the review process, a private access link is provided for reviewers during the review period (https://www.scidb.cn/s/bqyemq).
The neural network significantly improves the overall prediction accuracy of binding energies compared with the PCF-PK1 model. Without separation-energy constraints, ANN1 already achieves high precision for binding energies (RMSE ≈ 0.147 MeV) and separation energies (RMSE ≈ 0.158– 0.185 MeV). Incorporating separation-energy constraints in ANN3 results in a slight improvement in overall prediction accuracy. The binding energy predictions improve by approximately 4.6%, while the separation energy predictions increase by 8.9–12.0%. The improvement is particularly noticeable for nuclei where the deviations of ANN1 predictions from experimental values exceed 0.2 MeV. Supporting datasets are publicly accessible at the Science Data Bank (https://doi.org/10.57760/sciencedb.j00213.00239). To facilitate the review process, a private access link is provided for reviewers during the review period (https://www.scidb.cn/s/bqyemq).
Abstract +
Mechanoluminescence (ML) is a phenomenon in which photon emission is produced directly under mechanical stimulation. Owing to its high spatial selectivity, rapid response, and multimodal emission capabilities, ML exhibits great potential for applications in structural health monitoring, intelligent sensing, and optical anti-counterfeiting. However, due to the complexity of ML modes, categories, and underlying kinetic processes, the field still faces several challenges, including the lack of a well-established mechanism, the limited availability of high-performance ML materials, and the absence of standardized testing standards. Existing studies have demonstrated that crystal field strength, band structure, and lattice configuration play crucial roles in governing the ML properties. High-pressure, with its unique ability to tune these physical quantities, undoubtedly provides new pathways for advancing ML research. Recent breakthroughs in rapid loading techniques have further enabled the exploration of ML behaviors under high-pressure conditions. In the GPa pressure range, modulation of interatomic distances, electronic orbitals, and crystal structures has not only allowed effective control over emission intensity and color, but has also enabled the capture of ML kinetic processes over microsecond–second timescales, thereby supplying essential experimental data for revealing the microscopic mechanisms of ML. In this review, we first provide a brief overview of the historical development, classification, and mechanistic understanding of ML, together with commonly employed ML characterization methods under ambient and high-pressure conditions. We then summarize recent progress in the application of high-pressure techniques for optimizing ML performance and elucidating ML mechanisms, highlighting advances in enhancing emission intensity, modulating spectral characteristics, and uncovering dynamic processes. Finally, the future directions and challenges for high-pressure ML research are discussed.
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Abstract +
As the field of artificial intelligence continues to evolve, it generates an escalating need for intensive computational resources and novel computing architectures. As a new generation of non-volatile memory, memristors can simulate biological synapses. This makes them ideal for neuromorphic computing, enabling brain-like learning and reasoning to significantly enhance computational capabilities. Current research on memristor dielectric materials primarily focuses on transition of metal oxides, perovskites, and organic polymers. Among these, the transition metal oxide TiO2 is widely used for the switching layer due to its high dielectric constant and excellent thermal stability. However, TiO2-based memristors face challenges including poor stability and inadequate analog performance, which limit their application in neuromorphic computing. This study developed a high-performance analog memristor using an aMoS2/a-TiO2 (amorphous MoS2/ amorphous TiO2) heterostructure, achieving over 200 stable cycles and a long data retention time exceeding 104 seconds. This device demonstrates a lower threshold voltage, higher endurance, and superior data retention, as compared to previously reported TiO2-based heterostructure memristors. Furthermore, various voltage sweep schemes were designed to successfully implement multi-level conductance modulation in the W/a-MoS2/a-TiO2/Pt device. The resistive switching mechanism of the W/a-MoS2/a-TiO2/Pt device was elucidated by combining conductive mechanism fitting with a physical model that attributes the switching to the localized formation and rupture of conductive filaments. Finally, synaptic functions like LTP and LTD were implemented in the device using square-wave pulses. A convolutional neural network leveraging these functions achieved a 95.8% accuracy in handwritten digit recognition. This study developed a W/a-MoS2/a-TiO2/Pt heterostructure that significantly enhances analog memristive performance, providing an effective strategy for improving transition metal oxide-based memristors.
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Abstract +
Silver is a very common material in archaeology, and its neutron total cross-section is crucial for Neutron Resonance Transmission Analysis (NRTA) in archaeometry. In this work, the neutron total cross sections of natural silver (natAg) in the resonance region from 10 to 100 eV were measured at the China Spallation Neutron Source (CSNS) back-streaming neutron facility (Back-n). The neutron transmission rate of a 0.3 mm natAg sample was measured with a fission chamber equipped with 235U neutron converters. The neutron total cross-sections around resonance peaks at 16.25 eV, 30.35 eV, 40.15 eV, 41.35 eV, 44.6 eV, 51.25 eV, 55.45 eV, 70.75 eV, 87.3 eV were obtained. The cross-sections measured in this work are generally higher than previous measurements by G. Kim et al. and F. G. P. Seidl et al. These significant discrepancies are probably due to the resolution function of the facility, which is very sensitive to the resonance peaks.
The resonance parameters—the peak position of the resonances (Eres) and the neutron width (Γn)—of 107Ag and 109Ag were extracted by fitting the transmission rate based on R-matrix theory. The extracted parameters Eres and Γn are generally in good agreement with ENDF/B-VIII.0 and CENDL-3.2 evaluations, except that the neutron width at 16.33 eV is significantly lower than that in the evaluations. The resolution function of the facility is considered to be the main reason of the inaccuracy. More accurate resonance parameters could be extracted in the future when a better control of the resolution function is achieved.
This work provides new cross-section data that supports the research and development of NRTA technique at CSNS Back-n facility and contributes to the experimental dataset for the neutron total cross-section of natural silver. The dataset of this paper is available at https://www.scidb.cn/s/mu2Mjq.
The resonance parameters—the peak position of the resonances (Eres) and the neutron width (Γn)—of 107Ag and 109Ag were extracted by fitting the transmission rate based on R-matrix theory. The extracted parameters Eres and Γn are generally in good agreement with ENDF/B-VIII.0 and CENDL-3.2 evaluations, except that the neutron width at 16.33 eV is significantly lower than that in the evaluations. The resolution function of the facility is considered to be the main reason of the inaccuracy. More accurate resonance parameters could be extracted in the future when a better control of the resolution function is achieved.
This work provides new cross-section data that supports the research and development of NRTA technique at CSNS Back-n facility and contributes to the experimental dataset for the neutron total cross-section of natural silver. The dataset of this paper is available at https://www.scidb.cn/s/mu2Mjq.
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Abstract +
, , Received Date: 2025-08-01
Abstract +
Utilizing fractional vortex beams (FVBs) as information carriers can significantly enhance the capacity of communication systems. However, the small gap difference between adjacent fractional orbital angular momentum (FOAM) modes makes FVBs highly sensitive to atmospheric turbulence. Therefore, precise measurement of distorted FOAM modes is crucial for practical FVBs-based communication systems. To fully utilize the beam intensity information and the triangular diffraction pattern information, we propose a dual-channel deep learning model with a hybrid architecture combining convolutional neural network (CNN) and vision transformer (ViT). The beam intensity information is extracted using the CNN, while the diffraction pattern information is extracted using the ViT. Then, by combining the complementary feature information from the intensity distribution of FVBs and their triangular diffraction patterns, this model can effectively identify the FOAM modes. The results show that the proposed model only requires a relatively small number of samples to reach convergence, namely 100 sets of data under weak turbulence and 400 sets of data under strong turbulence. Moreover, within a transmission distance of 1000 m, the proposed model can identify 101 FOAM modes with a mode spacing of 0.1 with an accuracy of 100% under weak and moderate turbulences, and maintains 98.12% accuracy under strong turbulence. Furthermore, the model can expand the detection range of turbulence intensity with only a minimal loss in accuracy, exhibiting strong generalization ability under unknown atmospheric turbulence strengths, thus providing a novel approach for accurately identifying FOAM modes.
, , Received Date: 2025-07-24
Abstract +
In recent years, the application of machine learning in materials science has significantly accelerated the discovery of new materials. In particular, when combined with traditional methods such as first-principles calculations, machine learning models have proven effective in screening potential high-performance materials from existing databases. However, these methods are largely limited by the known chemical spaces, making it difficult to achieve the active design of novel material structures. To overcome this limitation, generative models have become a promising tool for inverse material design, providing new avenues for exploring unknown structures and property spaces. Although existing generative models have achieved initial progress in crystal structure generation, achieving property-guided material generation remains a significant challenge. In this review paper, we first introduce the representative generative models recently applied to materials generation, including CDVAE, MatGAN, and MatterGen, and analyzes their basic abilities and limitations in structural generation. We then focus on strategies for incorporating target properties into generative models to generate the property-guided structure. Specifically, we discuss four representative methods: Con-CDVAE based on target property vectors, SCIGEN with integrated structural constraints and guidance mechanisms, a fine-tuned version of MatterGen leveraging adapter-based property control, and a CDVAE latent space optimization strategy guided by property objectives. Finally, we summarize the key challenges faced by property-guided generative models and provide an outlook on future research directions. This review aims to offer researchers a systematic reference and inspiration for advancing property-driven generative approaches in material design and provides researchers with a systematic reference and insight into the advancement of property-driven generative methods for materials design.
, , Received Date: 2025-08-06
Abstract +
The lower friction coefficient and superior mechanical properties of palladium (Pd) alloys make them potentially advantageous for use in high-precision instruments and devices that require long-term stable performance. However, the high cost of raw materials and experimental expenses result in a lack of fundamental data, which hinders the design of high-performance Pd alloys. Therefore, in this study, first-principles calculations are used to determine the lattice constant and elastic modulus of Pd. A model of dilute solid solutions formed by Pd with 33 alloying elements including Al, Si, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and others, is established. The mixing enthalpy, elastic constant, and elastic modulus are calculated. The results show that, all other alloying elements except for Mn, Fe, Co, Ni, Ru, Rh, Os, and Ir can form solid solutions with Pd. Alloying elements from both sides of the periodic table enhance the ductility of Pd solid solutions, with La, Ag, and Zn having the most significant effects, while Cu and Hf reduce the ductility of Pd. Differential charge density analysis indicates that the electron cloud formed after doping with Ag is spherically distributed, thereby improving ductility. After doping with Hf, the degree of delocalization around the atoms is maximized, indicating a strong ionic bond between Hf and Pd, which results in a higher hardness of Pd31Hf. The datasets presented in this paper are openly available at https://www.doi.org/10.57760/sciencedb.j00213.00186 .
, , Received Date: 2025-10-09
Abstract +
Hydrogen energy, as one of the most promising clean and renewable energy sources, has received much attention due to its green production technology. Electrolytic water splitting is regarded as a critical pathway for large-scale green hydrogen production due to its environmentally friendly reaction process, high product purity, and operational simplicity, However, electrocatalysts for water electrolysis commonly face challenges such as high costs and complex synthesis processes, thereby severely hindering the industrial application. Low-temperature plasma (LTP) technology, with its advantages of mild processing conditions, high reactivity, and unique electromagnetic field effects, has demonstrated remarkable potential in the surface modification of materials. This review systematically summarizes the applications of LTP in the preparation and modification of electrocatalytic materials for water splitting, focusing on the mechanism of plasma-induced enhancement in electrocatalytic efficiency. First, the physical characteristics and fundamental principle of typical non-equilibrium low-temperature plasma are elucidated. Subsequently, recent advances in plasma-assisted modification strategies for catalytic materials are categorized and critically discussed, including surface microstructure modulation, surface property regulation and interface optimization. Finally, based on the current limitations in mechanistic understanding and practical applications, future research directions for LTP technology in catalyst design are proposed.
Abstract +
Photoionization time delay in atoms and molecules is a fundamental phenomenon in attosecond physics, encoding essential information about electronic structure and dynamics. Compared with atoms, molecules exhibit anisotropic potentials and additional nuclear degrees of freedom, which make the interpretation of molecular photoionization time delays more intricate but also more informative. In this work, we investigate the dependence of the photoionization time delay on the internuclear distance in the $ 5\sigma \to k\sigma$ ionization channel of carbon monoxide (CO) molecules. The molecular ground state is obtained using the Hartree-Fock method, and the photoionization process is treated within quantum scattering theory based on the iterative Schwinger variational principle of the Lippmann–Schwinger equation. Numerical calculations are performed with the ePolyScat program to obtain molecular-frame differential photoionization cross sections and time delays at various internuclear distances. Our results show that the extrema of the photoionization time delay occur near the peaks and dips of the differential cross section and shift toward lower energies as the internuclear distance R increases. At low energies, the time delay along the oxygen end increases with R, while that along the carbon end decreases, which is attributed to the asymmetric charge distribution and the resulting short-range potential difference between the two atomic sites. Around the shape-resonance energy region, both cross section and time delay display pronounced peaks associated with an $ l=3$ quasi-bound state. As R increases, the effective potential barrier broadens, the quasi-bound state energy moves to lower values, and its lifetime becomes longer, leading to enhanced resonance amplitude and increased time delay. In the high-energy region, opposite-sign peaks of time delay are found along the O and C directions, corresponding to minima in the cross section. These features are well explained by a two-center interference model, where increasing R shifts the interference minima and the associated time-delay peaks toward lower energies. This study provides deeper insights into the photoionization dynamics of CO molecules, accounting for the role of nuclear motion, and offers valuable references for studying the photoelectron dynamics of more complex molecular systems.
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Abstract +
Magnetic proximity effects (MPE) are crucial for topological quantum devices because they enable control of boundary states between a ferromagnetic insulator and a topological insulator. The InAs/GaInSb double quantum well system—especially when combined with a superconductor and influenced by MPE—shows promise for producing topological qubits. Nonetheless, researchers still debate the exact strength of the MPE between europium sulfide (EuS) and InAs.
To directly probe the MPE, this work focuses on a EuS/InAs/GaInSb heterostructure. The heterostructure was fabricated by depositing EuS onto the passivated surface of a Hall bar formed from an InAs/GaInSb double quantum well, utilizing an electron beam evaporation system. Structural analysis using Reflection High-Energy Electron Diffraction and magnetic measurements revealed that, although the resulting EuS thin films were polycrystalline, they nonetheless displayed desired magnetic properties, making them suitable for further study of MPE phenomena.
Low-temperature magnetoresistance measurements on the fabricated Hall bar revealed several key phenomena that collectively provide evidence for the MPE. Application of a positive gate voltage caused the electron wavefunction within the InAs layer to shift toward the EuS interface, thereby enhancing the MPE. Under a perpendicular magnetic field, the magnetoresistance exhibited an increasing slope for the odd-parity component. Additionally, a transition from positive to negative magnetoresistance near zero field was observed. When an in-plane magnetic field was applied, a gate-enhanced negative magnetoresistance emerged. Hysteretic magnetoresistance, corresponding to the reversal of EuS magnetization, was also detected during these measurements.
The resistance-temperature curve for the heterostructure displayed a pronounced upturn at low temperatures. This behavior was well described by the Kondo model, indicating the presence of exchange coupling between InAs electrons and the localized magnetic moments of EuS near the interface. Such coupling is a strong indicator of the magnetic proximity effect at work in the system.
These findings collectively demonstrate the existence of a gate-tunable MPE in the EuS/InAs/GaInSb heterostructure. The ability to control the MPE through gate voltage establishes this heterostructure as a compelling platform for the exploration of proximity-induced magnetism. Furthermore, these results underscore the potential applications of such systems in the development of spin-based electronic devices and highlight their significance for future research in topological quantum computing.
To directly probe the MPE, this work focuses on a EuS/InAs/GaInSb heterostructure. The heterostructure was fabricated by depositing EuS onto the passivated surface of a Hall bar formed from an InAs/GaInSb double quantum well, utilizing an electron beam evaporation system. Structural analysis using Reflection High-Energy Electron Diffraction and magnetic measurements revealed that, although the resulting EuS thin films were polycrystalline, they nonetheless displayed desired magnetic properties, making them suitable for further study of MPE phenomena.
Low-temperature magnetoresistance measurements on the fabricated Hall bar revealed several key phenomena that collectively provide evidence for the MPE. Application of a positive gate voltage caused the electron wavefunction within the InAs layer to shift toward the EuS interface, thereby enhancing the MPE. Under a perpendicular magnetic field, the magnetoresistance exhibited an increasing slope for the odd-parity component. Additionally, a transition from positive to negative magnetoresistance near zero field was observed. When an in-plane magnetic field was applied, a gate-enhanced negative magnetoresistance emerged. Hysteretic magnetoresistance, corresponding to the reversal of EuS magnetization, was also detected during these measurements.
The resistance-temperature curve for the heterostructure displayed a pronounced upturn at low temperatures. This behavior was well described by the Kondo model, indicating the presence of exchange coupling between InAs electrons and the localized magnetic moments of EuS near the interface. Such coupling is a strong indicator of the magnetic proximity effect at work in the system.
These findings collectively demonstrate the existence of a gate-tunable MPE in the EuS/InAs/GaInSb heterostructure. The ability to control the MPE through gate voltage establishes this heterostructure as a compelling platform for the exploration of proximity-induced magnetism. Furthermore, these results underscore the potential applications of such systems in the development of spin-based electronic devices and highlight their significance for future research in topological quantum computing.
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Abstract +
The spin-reorientation transition (SRT) in rare-earth orthoferrites offers an important platform for exploring the coupling and manipulation of spin dynamics, which is crucial for developing high-frequency spintronic and terahertz (THz) magneto-optical devices. In this work, we systematically investigate the temperature- and magnetic-field-induced SRT behavior and the associated electron paramagnetic resonance (EPR) transitions of Yb3+ ions in a-cut YbFeO3 single crystals using time-domain terahertz spectroscopy (THz-TDS). The temperature-dependent measurements from 1.6 to 300 K reveal a distinct SRT near 7 K, marked by a sudden shift of the magnetic resonance mode frequency. This indicates a transition of the Fe3+ spin configuration from the low-temperature Γ2 phase to the high-temperature Γ4 phase, driven primarily by the temperature evolution of the anisotropic Fe3+-Yb3+ exchange interaction.
Under an external magnetic field applied along the a-axis at 20 K, the system exhibits an incomplete field-induced SRT from the Γ4 phase toward the Γ2 phase. In the intermediate Γ24 phase, both the quasi-AntiFerroMagnetic (q-AFM) and quasi-FerroMagnetic (q-FM) modes are simultaneously excited, as observed in the THz absorption spectra. Notably, even at the maximum field of 7 T, the transition remains incomplete, indicating the stabilization of the intermediate phase over a wide field range. In the low-frequency region (<0.8 THz), two absorption peaks exhibiting clear blue shifts with increasing magnetic field are identified as EPR transitions between Zeeman sublevels of the crystal-field-split Kramers doublets of Yb3+ ions.
All experimental observations, including the temperature- and magnetic-field-dependent frequency responses of the q-AFM and q-FM modes as well as the evolution of the electron paramagnetic resonance signals with magnetic field, have been quantitatively described by coupling a spin dynamics model with crystal field theory. The model successfully reproduces the continuous rotation of the macroscopic Fe3+ magnetization vector within the ac plane under an applied magnetic field, revealing the microscopic mechanism of the field-induced SRT. The analysis demonstrates that the SRT process results from the competition and synergy between the external magnetic field and the anisotropic Fe3+-Yb3+ exchange interaction, which collectively modulate the internal effective field and determine the stability of the intermediate Γ24 phase.
This study confirms the effective control of spin configurations in YbFeO3 via both temperature and magnetic field, provides a deeper understanding of the Fe3+-Yb3+ exchange interaction mechanism, and offers important experimental insights for the design of terahertz functional devices based on rare-earth orthoferrites.
Under an external magnetic field applied along the a-axis at 20 K, the system exhibits an incomplete field-induced SRT from the Γ4 phase toward the Γ2 phase. In the intermediate Γ24 phase, both the quasi-AntiFerroMagnetic (q-AFM) and quasi-FerroMagnetic (q-FM) modes are simultaneously excited, as observed in the THz absorption spectra. Notably, even at the maximum field of 7 T, the transition remains incomplete, indicating the stabilization of the intermediate phase over a wide field range. In the low-frequency region (<0.8 THz), two absorption peaks exhibiting clear blue shifts with increasing magnetic field are identified as EPR transitions between Zeeman sublevels of the crystal-field-split Kramers doublets of Yb3+ ions.
All experimental observations, including the temperature- and magnetic-field-dependent frequency responses of the q-AFM and q-FM modes as well as the evolution of the electron paramagnetic resonance signals with magnetic field, have been quantitatively described by coupling a spin dynamics model with crystal field theory. The model successfully reproduces the continuous rotation of the macroscopic Fe3+ magnetization vector within the ac plane under an applied magnetic field, revealing the microscopic mechanism of the field-induced SRT. The analysis demonstrates that the SRT process results from the competition and synergy between the external magnetic field and the anisotropic Fe3+-Yb3+ exchange interaction, which collectively modulate the internal effective field and determine the stability of the intermediate Γ24 phase.
This study confirms the effective control of spin configurations in YbFeO3 via both temperature and magnetic field, provides a deeper understanding of the Fe3+-Yb3+ exchange interaction mechanism, and offers important experimental insights for the design of terahertz functional devices based on rare-earth orthoferrites.
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