Most Cited
2024, 73 (6): 069301.
doi: 10.7498/aps.73.20231618
Abstract +
In silico protein calculation has been an important research subject for a long time, while its recent combination with machine learning promotes the development greatly in related areas. This review focuses on four major fields of the in silico protein research that combines with machine learning, which are molecular dynamics, structure prediction, property prediction and molecule design. Molecular dynamics depend on the parameters of force field, which is necessary for obtaining accurate results. Machine learning can help researchers to obtain more accurate force field parameters. In molecular dynamics simulation, machine learning can also help to perform the free energy calculation in relatively low cost. Structure prediction is generally used to predict the structure given a protein sequence. Structure prediction is of high complexity and data volume, which is exactly what machine learning is good at. By the help of machine learning, scientists have gained great achievements in three-dimensional structure prediction of proteins. On the other hand, the predicting of protein properties based on its known information is also important to study protein. More challenging, however, is molecule design. Though marching learning has made breakthroughs in drug-like small molecule design and protein design in recent years, there is still plenty of room for exploration. This review focuses on summarizing the above four fields andlooks forward to the application of marching learning to the in silico protein research.
2024, 73 (8): 086104.
doi: 10.7498/aps.73.20231755
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This research focuses on enhancing Co-based high-temperature alloys by using γ' precipitate phases to address the structural metastability of γ'-Co3(Al, W). By adding Ti and Ta, the γ'-Co3(V, Ti) and γ'-Co3(V, Ta) of Co-V alloys are stabilized, surpassing the performance of traditional Co-Al-W alloys. Utilizing a 2×2×2 supercell model and density functional theory (DFT), we investigate these alloys' phase stabilities and mechanical, thermodynamic, and electronic properties. Our findings show that γ'-Co3(V, Ti) phase and γ'-Co3(V, Ta) phases are stable at 0 K, evidenced by negative formation enthalpies and stable phonon spectra. Mechanical analysis confirms their stabilities through elastic constants and detailed evaluations of properties such as bulk modulus, shear modulus, and Young’s modulus, revealing excellent resistance to deformation and ductility. The electronic structure analysis further distinguishes γ'-Co3(V, Ta) for superior electronic stability, which is attributed to its lower state density and deviation from “pseudogap” peaks. Thermodynamically, the quasi-harmonic Debye model highlights the γ'-Co3(V, Ti) phase’s temperature-sensitive thermal expansion coefficient, while γ'-Co3(V, Ta) maintains higher stability at elevated temperatures. As temperature rises, both phases show decreased resistance to deformation, though they maintain comparable heat resistance due to low-temperature dependency. These results suggest that Co-V-Ti alloy and Co-V-Ta alloy can maintain their γ' phase stability at higher temperatures, enhancing Co-based high-temperature alloys’ performances and phase stabilities. This progress is crucial for developing new Co-based superalloys, and is of great significance for their applications and performance optimization.
2024, 73 (1): 010301.
doi: 10.7498/aps.73.20231795
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In the early decades of the 20th century, the inception of quantum mechanics catalyzed the first quantum revolution, resulting in groundbreaking technological advances, such as nuclear energy, semiconductors, lasers, nuclear magnetic resonance, superconductivity, and global satellite positioning systems. These innovations have promoted significant progress in material civilization, fundamentally changed the way of life and societal landscape of humanity. Since the 1990s, quantum control technology has made significant strides forward, ushering in a rapid evolution of quantum technologies, notably exemplified by quantum information science. This encompasses domains such as quantum communication, quantum computing, and quantum precision measurement, offering paradigm-shifting solutions for enhancing information transmission security, accelerating computational speed, and elevating measurement precision. These advances hold the potential to provide crucial underpinning for national security and the high-quality development of the national economy. The swift progression of quantum information technology heralds the advent of the second quantum revolution. Following nearly three decades of concerted efforts, China’s quantum information technology field as a whole has achieved a leap. Specifically, China presently assumes a prominent international role in both the research and practical application of quantum communication, leading the global domain in quantum computing, and achieving international preeminence or advanced standing across various facets of quantum precision measurement. Presently, it is imperative to conduct a comprehensive assessment of the developmental priorities in the realm of quantum information in China for the forthcoming 5 to 10 years, in alignment with national strategic priorities and the evolving landscape of international competition. This will enable the proactive establishment of next-generation information technology systems that are secure, efficient, autonomous, and controllable.
2024, 73 (4): 048901.
doi: 10.7498/aps.73.20231416
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Identifying influential nodes in spreading process in the network is an important step to control the speed and range of spreading, which can be used to accelerate the spread of beneficial information such as healthy behaviors, innovations and suppress the spread of epidemics, rumors and fake news. Existing researches on identification of influential spreaders are mostly based on low-order complex networks with pairwise interactions. However, interactions between individuals occur not only between pairwise nodes but also in groups of three or more nodes, which introduces complex mechanism of reinforcement and indirect influence. The higher-order networks such as simplicial complexes and hypergraphs, can describe features of interactions that go beyond the limitation of pairwise interactions. Currently, there are relatively few researches of identifying influential spreaders in higher-order networks. Some centralities of nodes such as higher-order degree centrality and eigenvector centrality are proposed, but they mostly consider only the network structure. As for identification of influential spreaders, the spreading influence of a node is closely related to the spreading process. In this paper, we work on identification of influential spreaders on simplicial complexes by taking both network structure and dynamical process into consideration. Firstly, we quantitatively describe the dynamics of disease spreading on simplicial complexes by using the Susceptible-Infected-Recovered microscopic Markov equations. Next, we use the microscopic Markov equations to calculate the probability that a node is infected in the spreading process, which is defined as the spreading centrality (SC) of nodes. This spreading centrality involves both the structure of simplicial complex and the dynamical process on it, and is then used to rank the spreading influence of nodes. Simulation results on two types of synthetic simplicial complexes and four real simplicial complexes show that compared with the existing centralities on higher-order networks and the optimal centralities of collective influence and nonbacktracking centrality in complex networks, the proposed spreading centrality can more accurately identify the most influential spreaders in simplicial complexes. In addition, we find that the probability of nodes infected is highly positively correlated with its influence, which is because disease preferentially reaches nodes with many contacts, who can in turn infect their many neighbors and become influential spreaders.
2024, 73 (2): 027702.
doi: 10.7498/aps.73.20230614
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2024, 73 (1): 010501.
doi: 10.7498/aps.73.20231211
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A physical memristor has an asymmetric tight hysteresis loop. In order to simulate the asymmetric tight hysteresis curve of the physical memristor more conveniently, a fractional-order diode bridge memristor model with a bias voltage source is proposed in this paper, which can continuously regulate the hysteresis loop. Firstly, based on fractional calculus theory, a fractional order model of a diode bridge memristor with a bias voltage source is established, and its electrical characteristics are analyzed. Secondly, by integrating it with the Jerk chaotic circuit, a non-homogeneous fractional order memristor chaotic circuit model with a bias voltage source is established, and the influence of bias voltage on its system dynamic behavior is studied. Once again, a fractional-order equivalent circuit model is built in PSpice and validated through circuit simulation. The experimental results are basically consistent with the numerical simulation results. Finally, the experiments on the circuit are completed in LabVIEW to validate the correctness and feasibility of the theoretical analysis. The results indicate that the fractional order memristor with bias voltage source can continuously obtain asymmetric tight hysteresis loop by adjusting the voltage of the bias voltage source. As the bias power supply voltage changes, the non-homogeneous fractional order memristor chaotic system exhibits that the period doubling bifurcation turns into chaos due to the symmetry breaking.
2024, 73 (6): 063101.
doi: 10.7498/aps.73.20231631
Abstract +
Perovskite solar cell material becomes one of the most attractive light absorbing materials in the photovolatic field due toits unique photoelectric characteristics, especially the rapid improvement of photoelectric conversion efficiency in the initial short period of time. However, in recent years, the growth of conversion efficiency has entered a slow stage, posing a challenge for subsequent development. In addition, the long-time stability of material has become a key barrier to widespread commerical applications. The emergence of these problems is closely related to the inevitable defects in the material in preparation process, because defect is usually regarded as one of the key factors hindering the improvement of photovolatic performance and materical stability. Therefore, a comprehensive understanding of the inherent defects of material is essential to improve cell efficiency and maintain long-time structural stability. In this paper, the effects of defects in perovskite material on photovolatic performance and stability are discussed in many aspects, including the traditional rigid defects, unconventional defects, complex defects, and ion migration. Second, this work also delves into how defects affect carrier lifetime and highlights their role in determining the overall cell performance. Such insights are very important in designing effective strategies to mitigate the adverse effects of defects on material performance and stability. Finally, we discuss the complex relationship between defects and structural stability, and recognize that the defects are a key factor affecting the long-term robustness of perovskite solar cells. The understanding of the mechanism behind the focus problems will help researchers achieve new ideas to improve the efficiency and duraibility of perovskite solar cell technology. Overall, this review not only provides the current state of knowledge on defects in perovskite materials, but also illustrates further research directions. By revealing the complex interplay between defects, photovoltaic performance and structural stability, researchers can find a way to break through the current limitations and realize the potential value of perovskite solar cell technology in the commercial applications. Thiswork aims to spark an in-depth discussion of this issue and further explore and innovate in this promising field.
2024, 73 (3): 038901.
doi: 10.7498/aps.73.20231096
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Quantifying structural similarity between complex networks presents a fundamental and formidable challenge in network science, which plays a crucial role in various fields, such as bioinformatics, social science, and economics, and serves as an effective method for network classification, temporal network evolution, network generated model evaluation, etc. Traditional network comparison methods often rely on simplistic structural properties such as node degree and network distance. However, these methods only consider the local or global aspect of a network, leading to inaccuracies in network similarity assessments. In this study, we introduce a network similarity comparison method based on the high-order structure. This innovative approach takes into account the global and the local structure of a network, resulting in a more comprehensive and accurate quantification of the network difference. Specifically, we construct distributions of higher-order clustering coefficient and distance between nodes in a network. The Jensen-Shannon divergence, based on these two distributions, is used to quantitatively measure the similarity between two networks, offering a more refined and robust measure of network similarity. To validate the effectiveness of our proposed method, we conduct a series of comprehensive experiments on the artificial and the real-world network, spanning various domains and applications. By meticulously fine-tuning the parameters related to three different artificial network generation models, we systematically compare the performances of our method under various parameter settings in the same network. In addition, we generate four different network models with varying levels of randomization, creating a diverse set of test cases to evaluate the robustness and adaptability of the method. In artificial networks, we rigorously compare our proposed method with other baseline techniques, consistently demonstrating its superior accuracy and stability through experimental results; in real networks, we select datasets from diverse domains and confirm the reliability of our method by conducting extensive similarity assessments between real networks and their perturbed reconstructed counterparts. Furthermore, in real networks, the rigorous comparison between our method and null models underscores its robustness and stability across a broad spectrum of scenarios and applications. Finally, a meticulous sensitivity analysis of the parameters reveals that our method exhibits remarkable performance consistency across networks of different types, scales, and complexities.
2024, 73 (1): 017501.
doi: 10.7498/aps.73.20231589
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Hall effect is an ancient but highly potential subfield in condensed matter physics, and its origin can be traced back hundreds of years. In 1879, Hall made a momentous discovery that when a current-carrying conductor is placed in a magnetic field, the Lorentz force pushes its electrons to one side of the conductor. This intriguing phenomenon was dubbed Hall effect. Since then, a series of novel Hall effects have been discovered, including anomalous Hall effect, quantum Hall effect, spin Hall effect, topological Hall effect, and planar Hall effec. Notably, Hall effects play an important role in realizing the information transport, since it can realize the mutual conversion of current in different directions. In bosonic systems such as magnons, a series of magnon Hall effects have been found, jointly driving the development of the magnon-based spintronics. In this perspective, we review the researches of the Hall effect in magnonic system in recent years, and briefly introduce its modern semi-classical theories, including virtual electromagnetic field theory and scattering theory. Furthermore, we introduce the different magnon Hall effects and clarify the physics behind them. Finally, the prospect of magnon Hall effect is discussed.
2024, 73 (2): 027301.
doi: 10.7498/aps.73.20230556
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With the development of science and technology, polymer dielectric capacitors are widely used in energy, electronics, transportation, aerospace, and many other areas. For polymer dielectric energy storage capacitors to remain effective in practical applications, excellent charge and discharge performance is essential. However, the performance of the common polymer dielectric capacitors will deteriorate rapidly at high temperature, which makes them fail to work efficiently under worse working conditions. Dielectric trap energy levels and trap densities increase when nanoparticles are incorporated into the dielectric. The change in trap parameters will affect carrier transport. Therefore, the high temperature energy storage performance of polymer nanocomposite dielectric can be improved by changing the trap parameters to regulate the carrier transport process. However, the quantitative relationship between trap energy level and trap density and the energy storage properties of nanocomposite dielectric need further studying. In this paper, the energy storage and release model for exponentially distributed trapped charge jump transport in linear polymer nanocomposite dielectrics is constructed and simulated. The volume resistivity and electric displacement-electric field loops of pure polyetherimide are simulated at 150 ℃, and the simulation results match the experimental results, which demonstrates the validity of the model. Following that, under different temperatures and electric fields, the current density, electric displacement-electric field loops, discharge energy density and charge-discharge efficiency of polyetherimide nanocomposite dielectric are simulated by using different trap parameters. The results show that increasing the maximum trap energy level and the total trap density can effectively reduce the carrier mobility, current density and conductivity loss, and enhance the discharge energy density and the charge-discharge efficiency of the nanocomposite dielectric. On condition that temperature is 150 ℃ and applied electric field is 550 kV/mm, the polyetherimide nanocomposite dielectric with a maximum trap energy level of 1.0 eV and a total trap density of 1×1027 m–3, has 4.26 J·cm–3 of discharge energy density and 98.93% of energy efficiency. Compared with pure polyetherimide, the rate of improvement is 91.09% and 227.58%, respectively. The energy storage performance under high temperature and high electric field is obviously improved. It provides theoretical and model support for the research and development of capacitors with high temperature resistance and energy storage performance.
2024, 73 (2): 024401.
doi: 10.7498/aps.73.20231142
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This work is devoted to investigating the difference in flow and heat transfer characteristics between vertical upward flow and horizontal flow of supercritical carbon dioxide ($\rm sCO_2$ ) based on the pseudo-boiling theory and the experimental parameters: mass flux G = 496–1100 kg/m2s, heat flux qw = 54.4–300.2 kW/m2, and pressure P = 7.531–20.513 MPa. The differences in flow and heat transfer characteristics between horizontal upward tube and vertical upward tube are compared at different mass fluxes, heat fluxes and pressures fully. Finally, unlike the classical treatment of flow and heat transfer for supercritical fluid, single-phase fluid assumption is abandoned, instead, the pseudo-boiling theory is introduced to deal with the flow transfer and heat transfer of $\rm sCO_2 $ in the two tubes. Supercritical fluid is regarded as a multiphase structure in this work, including a vapor-like layer near the wall and a liquid-like fluid in tube core. The results are indicated below. 1) In terms of heat transfer, the inner-wall temperature of the vertical upward tube and the bottom generatrix of horizontal tube are basically the same under normal heat transfer mode. When the heat transfer deterioration occurs in the vertical upward tube, larger supercritical boiling number (SBO) will cause the wall temperature peak of the vertical upward tube to be much higher than the wall temperature at top generatrix of the horizontal tube at the corresponding enthalpy. The SBO (SBO = 5.126×10–4) distinguishes between normal heat transfer deterioration and heat transfer deterioration in the vertical upward tube. In the horizontal tubes, SBO dominates the maximum wall temperature difference between the top generatrix and the bottom generatrix. Comparing with vertical upward tubes, higher qw/G is required for the heat transfer deterioration of supercritical fluid in the horizontal tubes under the same pressure. 2) In terms of flow, the increase in slope of pressure drop in the vertical upward tube is due to the orifice contraction effect. The mechanism that dominates the variation of pressure drop in the horizontal tube is the flow stratification effect, and we show that Froude number Frave can be the similarity criterion number to connect the temperature difference between the top and bottom generatrix of horizontal tube and the pressure drop. The analysis suggests that mechanisms governing horizontal flow and vertical flow of $\rm sCO_2 $ are different in heat transfer deterioration mode. For the vertical flow, the SBO plays a leading role, while for the horizontal flow, the Fr plays an indispensable role.
2024, 73 (2): 025201.
doi: 10.7498/aps.73.20231056
Abstract +
Laser-sustained plasma (LSP), which can be utilized for a novel radiation light source, has advantages such as high irradiance, broad spectral range, and stable emission, demonstrating significant applications in wafer inspection in the field of the semiconductor industry. This paper revisits the historical development of LSP research and introduces fundamental physical processes in LSP. The mathematical description equations for LSP and methods of calculating plasma parameters are provided, thereby a time-dependent two-dimensional fluid model is established by taking into consideration a laser-thermal-hydrodynamic coupling effect. The propagation of the laser in plasma is investigated based on the established model, and the fundamental processes in LSP, including the initial evolution process, laser energy deposition, steady-state characteristics, and instability, are explored. The effectiveness of the simulation model is confirmed through comparing with the experimental results of high-pressure Xe LSP. The findings indicate that the mode, power, F-number of incident lasers, as well as parameters including components, pressure, and flow velocity of gas, can all affect the steady-state properties of LSPs. Under the identical power and F-number conditions, Gaussian mode laser and annular mode laser both produce LSPs with different shapes and positions. Notably, under the conditions of high-power annular laser incidence, large laser F-number, and high flow velocity, the simulation results reveal temporal and spatial instability in LSP. These simulation results contribute significantly to a more in-depth understanding of the underlying physical mechanisms of the LSP. Furthermore, they provide a theoretical basis for designing the light source system and optimizing the multiple parameters. The influence of laser parameters on LSP properties elucidated in this study not only advances the fundamental understanding of LSP but also offers crucial insights for designing and optimizing the light source systems in various applications, particularly in the field of optical detection for semiconductor wafer inspection.
2024, 73 (2): 026102.
doi: 10.7498/aps.73.20231357
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Due to their excellent electrical and optical properties, carbon nanotubes have broad application prospects in the field of optoelectronics. In this work the vacuum filtration method is used to obtain an isotropic single-walled carbon nanotube film by the dispersion of single-walled carbon nanotube powder through vacuum filtration; on the basis of extracting the dielectric parameters of the thin film in a range from 0.4 to 2.0 THz, a novel terahertz metasurface narrowband absorber based on single-walled carbon nanotube films is designed and prepared. This metasurface absorber is composed of square and I-shaped narrow slot resonators. The experimental and simulation results show that the proposed terahertz metasurface absorber exhibits four distinct resonance absorption peaks at 0.65, 0.85, 1.16, and 1.31 THz, respectively, achieving a perfect absorption of up to 90%. The absorption mechanism of this novel multi band terahertz metasurface is elucidated by using the theory of multiple reflection interference. By covering dielectric layers with different refractive indices on the surface of metasurface device, the sensing performance of metasurface acting as refractive index sensor is studied in depth. The research results indicate that this new type of metasurface absorber has high sensitivity for refractive index sensing, providing new ideas and solutions for further developing carbon-based new terahertz metasurface absorbers.
2024, 73 (3): 034302.
doi: 10.7498/aps.73.20231578
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Ultrasonic identification has an important application value for national defense, military affairs, aerospace, nuclear facilities and other high-tech fields. Ultrasonic waves can be used to identify any metal material. At present, the researches focus on algorithms for identifying the difference in ultrasonic signal among materials, but the study on the corresponding identification theory is lacking. In this work, 10 primary models of the microstructure of 2A12 aluminum alloy are established as analogies to the complex microstructures of polycrystalline metallic materials. The grains of these models are different from each other in size, separation distance, shape, arrangement directions and orders. The time-domain ultrasonic echo signals of different microstructures are calculated by making use of the finite element method. The grass-like signals between two echoes are ultrasonic backscattering signals, which are sensitive to any change of microstructure. The backscattering signals between the primary echo and the secondary echo in the ultrasonic echo time domain signals are extracted as ultrasonic fingerprints. The feature difference Q is defined to quantify the difference in ultrasonic fingerprint of each sample. The results show that the slight variation in microstructure will lead to difference in ultrasonic signal, and the difference caused by the variation in grain size is more distinct. And then, an ultrasonic identification algorithm is proposed, and the identification experiments are conducted on four 2A12 aluminum alloy samples with the same shape. The identification results show that the target sample can be accurately identified by using ultrasonic fingerprints and the ultrasonic fingerprints of the target sample are distinctly different from those of the other samples. The microstructure morphologies of the samples are examined by using scanning electron microscopy (SEM). The SEM results show that there are significant differences in grain size, separation distance and densification between samples although they are the same material. The features of the microstructure in the proposed ultrasonic scattering model in this work are confirmed by the actual y micromorphologies observed in the SEM images. The identification experiments and SEM results demonstrate that the established ultrasonic scattering model is effective. This work can provide a reference for theoretically studying ultrasonic identification and present an idea for developing some new identification algorithms in future.
2024, 73 (2): 024201.
doi: 10.7498/aps.73.20231365
Abstract +
The design of thin frequency selective surface (FSS) absorber based on resistive film that meets the requirements of broadband, polarization independence, incident angle stability, and strong absorption is a challenging task. Fabrication tolerance of resistive film can result in fluctuations in sheet resistance, which negatively affects the absorber performance. To tackle these problems, this work firstly investigates how sheet resistance fluctuations affect the absorbing performance of resistive film FSS absorber. The analysis of simulated surface current density distribution and impedance reveals that the diversity of current paths provides an effective way to mitigate the influence of sheet resistance fluctuation. This is achieved by enabling flexible variation of surface current in response to sheet resistance fluctuations. Consequently, the variation of input impedance of the FSS absorber due to the fluctuation of sheet resistance is suppressed within a small range. Then, a method of extending bandwidth is proposed by employing the complementary variation of FSS impedance with frequency at different layers. By combining this approach with a miniaturization design, a thin and light FSS absorber is developed that exhibits ultra-wide bandwidth, polarization independence and angle stability while mitigating the effects of sheet resistance perturbation. The proposed FSS absorber achieves a 90% absorption bandwidth from 1.50 GHz to 20.50 GHz, covering Ku, X, C, S bands and part of the L and K bands, with a relative bandwidth reaching 173%. The absorber has a thickness of 0.093λL for both transverse electric (TE) polarization and transverse magnetic (TM) polarization, yielding a figure of merit (FoM, the ratio of the theoretical minimum thickness to the actual thickness) of 0.95, indicating that the thickness is close to the theoretical limit. The absorber maintains over 90% absorption rate for TM polarization at an incidence angle of up to 70°, and 80% absorption for TE polarization at 45°. Furthermore, the 90% absorbance bandwidth of the absorber remains at 167.0% when the sheet resistance of any FSS layer fluctuates within a range from 12 to 30 Ω/sq. A prototype of the proposed FSS absorber is fabricated and measured, and the experimental results are in good agreement with the simulation results, thus validating the effectiveness of the proposed method.
2024, 73 (2): 027703.
doi: 10.7498/aps.73.20230708
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Adding nanofillers into epoxy resin matrices is a common method to achieve their multi-function. Boron nitride nanotubes (BNNTs) with one-dimensional nanostructures have attracted much attention because of their ultra-high thermal conductivity, wide energy level band gap, high aspect ratio and mechanical strength. Yet, the strong π-π non-covalent bonding and lip-lip interactions make BNNTs prone to agglomeration in the epoxy resin matrix. Moreover, the different physicochemical properties of BNNTs and epoxy resins as well as the chemical inertness of BNNTs surface lead to the lack of effective interfacial interaction between BNNTs and epoxy resin matrix. Therefore, the performance of the epoxy composite dielectric is not enhanced by simple blending solely, but will even have the opposite effect. To address the problems of BNNTs, in this study, the surface structure of BNNTs is constructed from the perspective of interface modulation by using sol-gel method to coat mesoporous silica (mSiO2) on BNNTs’ surface and further introducing silane coupling agent (KH560). The results indicate that the surface structure of BNNTs can optimize the level of interfacial interaction between BNNTs and epoxy resin matrix, which leads to stronger interfacial connection and elimination of internal pore phenomenon. The dielectric constant and loss of the composite dielectric prepared in this way are further reduced, reaching 4.1 and 0.005 respectively at power frequency, which is significantly lower than that of pure epoxy resin. At the same time, the mechanical toughness (3.01 MJ/m3) and thermal conductivity (0.34 W/(m⋅K)) are greatly improved compared with the counterparts of pure epoxy resin. In addition, the unique nano-mesoporous structure of mSiO2 endows the composite dielectric with a large number of deep traps, which effectively hinders the migration of electrons, thereby improving the electrical strength of the composite dielectric, and the breakdown field strength reaches 95.42 kV/mm. Furthermore, the interfacial mechanism of BNNTs’ surface structure on dielectric relaxation and trap distribution of composite dielectrics is systematically studied by Tanaka multinuclear model. The above results indicate that the good interfacial interaction between BNNTs and epoxy resin matrix is crucial in establishing the micro-interface structure and improving the macroscopic properties of composite dielectrics. This study presents a novel idea for the multifunctionalities of epoxy resin, and also provides some experimental data support for revealing the correlation among surface properties of nano-fillers, microstructure and macroscopic properties of composite dielectric.
2024, 73 (1): 017503.
doi: 10.7498/aps.73.20231940
Abstract +
Diluted ferromagnetic semiconductors (DMSs) have attracted widespread attention in last decades, owing to their potential applications in spintronic devices. But classical group-III-IV, and -V elements based DMS materials, such as (Ga,Mn)As which depend on heterovalent (Ga3+, Mn2+) doping, cannot separately control carrier and spin doping, and have seriously limited chemical solubilities, which are disadvantages for further improving the Curie temperatures. To overcome these difficulties, a new-generation DMS with independent spin and charge doping have been designed and synthesized. Their representatives are I-II-V based Li(Zn,Mn)As and II-II-V based (Ba,K)(Zn,Mn)2As2. In these new materials, doping isovalent Zn2+ and Mn2+ introduces only spins, while doping heterovalent non-magnetic elements introduces only charge. As a result, (Ba,K)(Zn,Mn)2As2 achieves Curie temperature of 230 K, a new record among DMS where ferromagnetic orderings are mediated by itinerate carriers. Herein, we summarize the recent advances in the new-generation DMS materials. The discovery and synthesis of several typical new-generation DMS materials are introduced. Physical properties are studied by using muon spin relaxation, angle-resolved photoemission spectroscopy and pair distribution function. The physical and chemical pressure effects on the title materials are demonstrated. The Andreev reflection junction based on single crystal and the measurement of spin polarization are exhibited. In the end, we demonstrate the potential multiple-parameter heterojunctions with DMSs superconductors and antiferromagnetic materials.
2024, 73 (1): 014202.
doi: 10.7498/aps.73.20231030
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2024, 73 (4): 044204.
doi: 10.7498/aps.73.20231384
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2024, 73 (3): 037202.
doi: 10.7498/aps.73.20231393
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In this work, we numerically study the localization properties in a quasi-periodically modulated one-dimensional cross-stitch lattice with a flat band. When $\varDelta\neq0$ , it is found that there are two different quasi-periodic modulation frequencies in the system after the local transformation, and the competing modulation by two frequencies may lead to the reentrant localization transition in the system. By numerically solving the fractal dimension, the average inverse participation ratio, and the average normalized participation ratio, we confirm that the system can undergo twice localization transitions. It means that the system first becomes localized as the disorder increases, at some critical points, some of the localized states go back to the delocalized ones, and as the disorder further increases, the system again becomes fully localized. By the scalar analysis of the normalized participation ratio, we confirm that reentrant localization stably exists in the system. And the local phase diagram is also obtained. From the local phase diagram, we find that when $1.6<\varDelta<1.9$ , the system undergoes a cascade of delocalization-localization-delocalization-localization transition by increasing λ. When $\varDelta=0$ , there exists only one quasi-periodic modulation frequency in the system. And we analytically obtain the expressions of the mobility edges, which are in consistence with the numerical studies by calculating the fractal dimension. And the system exhibits one localization transition. This work could expand the understanding of the reentrant localization in a flat band system and offers a new perspective on the research of the reentrant localization transition.
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