Most Cited
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 (3): 037201.
doi: 10.7498/aps.73.20231151
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Metal is one of the most widely used engineering materials. In contrast to the extensive research dedicated to their mechanical properties, studies on the thermal conductivity of metals remain relatively rare. The understanding of thermal transport mechanisms in metals is mainly through the Wiedemann-Franz Law established more than a century ago. The thermal conductivity of metal is related to both the electron transport and the lattice vibration. An in-depth understanding of the thermal transport mechanism in metal is imperative for optimizing their practical applications. This review first discusses the history of the thermal transport theory in metals, including the Wiedemann-Franz law and models for calculating phonon thermal conductivity in metal. The recently developed first-principles based mode-level electron-phonon interaction method for determining the thermal transport properties of metals is briefly introduced. Then we summarize recent theoretical studies on the thermal conductivities of elemental metals, intermetallics, and metallic ceramics. The value of thermal conductivity, phonon contribution to total thermal conductivity, the influence of electron-phonon interaction on thermal transport, and the deviation of the Lorenz number are comprehensively discussed. Moreover, the thermal transport properties of metallic nanostructures are summarized. The size effect of thermal transport and the Lorenz number obtained from experiments and calculations are compared. Thermal transport properties including the phonon contribution to total thermal conductivity and the Lorenz number in two-dimensional metals are also mentioned. Finally, the influence of temperature, pressure, and magnetic field on thermal transport in metal are also discussed. The deviation of the Lorenz number at low temperatures is due to the different electron-phonon scattering mechanisms for thermal and electrical transport. The mechanism for the increase of thermal conductivity in metals induced by pressure varies in different kinds of metals and is related to the electron state at the Fermi level. The effect of magnetic field on thermal transport is related to the coupling between the electron and the magnetic field, therefore the electron distribution in the Brillouin zone is an important factor. In addition, this review also looks forward to the future research directions of metal thermal transport theory.
2024, 73 (6): 064201.
doi: 10.7498/aps.73.20231850
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The proposal and development of topological photonics have provided a new approach to fundamentally addressing the susceptibility of traditional photonic devices to defects or disorders, significantly enhancing the transmission efficiency and robustness of photonic devices. Among them, non-reciprocal topological photonics which break time-reversal symmetry and support chiral topological states are crucial branches of topological photonics. Their topological properties are characterized by non-zero Chern numbers in two dimensions or topological Chern vectors in three dimensions, exhibiting a rigorous and complete topological protection beyond that of reciprocal topological photonics. This review focuses on introducing the remarkable achievements of non-reciprocal topological photonics in exploring novel physical phenomena (chiral/antichiral edge/surface states, two-dimensional/three-dimensional photonic Chern insulators, magnetic Weyl photonics crystals, etc.) and constructing non-reciprocal robust topological photonic devices (unidirectional waveguides, broadband slow-light delay lines, arbitrarily shaped topological lasers, high-orbital-angular-momentum coherent light sources, etc.). Finally, the present status, potential challenges, and possible breakthroughs in the development of non-reciprocal topological photonics are discussed.
2024, 73 (6): 063101.
doi: 10.7498/aps.73.20231631
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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 (7): 078801.
doi: 10.7498/aps.73.20240201
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Cross-linked polyethylene (XLPE) has been widely used in the field of power cables due to its excellent mechanical properties and insulating properties. However, during the manufacturing of high voltage cables, XLPE will inevitably be affected by electrical aging, thermal aging and electro-thermal combined aging, which makes the resistance and life of the material decline. Therefore, it is necessary to enhance the aging resistance of XLPE without affecting its mechanical properties and insulating properties, so as to extend its service life. In this work, the structural characteristics and cross-linking mechanism of XLPE are introduced, the aging process and influencing mechanism are systematically analyzed, and the life decay problems of XLPE due to aging are explored by using methods such as the temperature Arrhenius equation and the inverse power law of voltage. The improvement strategies such as grafting, blending, and nanoparticle modification can be used to enhance the thermal stability, antioxidant properties, and thermal aging resistance of XLPE, thereby extending its service life. Finally, the strategies of adjusting and controlling the service life of XLPE cable insulation materials in the future are discussed, which provide theoretical guidance for further improving long-term stable operation of XLPE cable insulation materials.
2024, 73 (2): 027702.
doi: 10.7498/aps.73.20230614
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2024, 73 (4): 044204.
doi: 10.7498/aps.73.20231384
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2024, 73 (22): 226401.
doi: 10.7498/aps.73.20240937
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This paper deals with the problem of identifying, evaluating, and ranking key nodes in complex networks by introducing a novel multi-parameter control graph convolutional network (MPC-GCN) for assessing node importance. Drawing inspiration from the multidimensional and hierarchical interactions between nodes in physical systems, this method integrates the automatic feature learning capabilities of graph convolutional networks (GCNs) with a comprehensive analysis of intrinsic properties of nodes, their interactions with neighbors, and their roles in the broader network. The MPC-GCN model provides an innovative framework for identifying key node by using GCNs to iteratively aggregate node and neighbor features across layers. This process captures and combines local, global, and positional characteristics, enabling a more nuanced, multidimensional assessment of node importance. Moreover, the model also includes a flexible parameter adjustment mechanism that allows for adjusting the relative weights of different dimensions, thereby adapting the evaluation process to various network structures. To validate the effectiveness of the model, we first test the influence of model parameters on randomly generated small networks. We then conduct extensive simulations on eight large-scale networks by using the susceptible-infected-recovered (SIR) model. Evaluation metrics, including the M(R) score, Kendall’s tau correlation, the proportion of infected nodes, and the relative size of the largest connected component, are used to assess the model’s performance. The results demonstrate that MPC-GCN outperforms existing methods in terms of monotonicity, accuracy, applicability, and robustness, providing more precise differentiation of node importance. By addressing the limitations of current methods, such as their reliance on single-dimensional perspectives and lack of adaptability, the MPC-GCN provides a more comprehensive and flexible approach to node importance assessment. This method significantly improves the breadth and applicability of node ranking in complex networks.
2024, 73 (7): 076401.
doi: 10.7498/aps.73.20231939
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2024, 73 (3): 038102.
doi: 10.7498/aps.73.20230832
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Group III nitride semiconductors, such as GaN, AlN, and InN, are an important class of compound semiconductor material, and have attracted much attention, because of their unique physicochemical properties. These semiconductors possess excellent characteristics, such as wide direct bandgap, high breakdown field strength, high electron mobility, and good stability, and thus are called third-generation semiconductors. Their alloy materials can adjust their bandgaps by changing the type or proportion of group III elements, covering a wide wavelength range from near-ultraviolet to infrared, thereby achieving wavelength selectivity in optoelectronic devices. Atomic layer deposition (ALD) is a unique technique that produces high-quality group III nitride films at low temperatures. The ALD has become an important method of preparing group III nitrides and their alloys. The alloy composition can be easily controlled by adjusting the ALD cycle ratio. This review highlights recent work on the growth and application of group III nitride semiconductors and their alloys by using ALD. The work is summarized according to similarities so as to make it easier to understand the progress and focus of related research. Firstly, this review summarizes binary nitrides with a focus on their mechanism and application. In the section on mechanism investigation, the review categorizes and summarizes the effects of ALD precursor material, substrate, temperature, ALD type, and other conditions on film quality. This demonstrates the effects of different conditions on film growth behavior and quality. The section on application exploration primarily introduces the use of group III nitride films in various devices through ALD, analyzes the enhancing effects of group III nitrides on these devices, and explores the underlying mechanisms. Additionally, this section discusses the growth of group III nitride alloys through ALD, summarizing different deposition methods and conditions. Regarding the ALD growth of group III nitride semiconductors, there is more research on the ALD growth of AlN and GaN, and less research on InN and its alloys. Additionally, there is less research on the ALD growth of GaN for applications, as it is still in the exploratory stage, while there is more research on the ALD growth of AlN for applications. Finally, this review points out the prospects and challenges of ALD in preparation of group III nitride semiconductors and their alloys.
2024, 73 (19): 198901.
doi: 10.7498/aps.73.20240702
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Supply chain is a chain structure formed by the sequential processes of production and distribution, spanning from raw material suppliers to end customers. An efficient and reliable supply chain is of great significance in enhancing enterprise’s market competitiveness and promoting sustainable social and economic development. The supply chain includes the interconnected flows of materials, resources, capital, and information across various stages, including procurement, production, warehousing, distribution, customer service, information management, and financial management. By representing the various participants in the supply chain as nodes and their interactions—such as the logistics, capital flow, information flow, and other interactions—as edges, the supply chain can be described and characterized as a complex network. In recent years, using complex network theory and methods to model and analyze supply chains has attracted increasing attention from researchers. This paper systematically reviews the supply chain research based on complex network theory, providing an in-depth analysis of supply chain networks in terms of network construction, structural properties, and management characteristics. First, this paper reviews two kinds of approaches to constructing supply chain network: empirical data-based approach and network model-based approach. In the empirical data-based research, scholars use common supply chain databases or integrate multiple data sources to identify the supply chain participants and clarify their attributes, behaviors, and interactions. Alternatively, the research based on network models employs the Barabási–Albert (BA) model, incorporating factors such as node distance, fitness, and edge weights, or uses hypergraph models to construct supply chain networks. Next, this paper summarizes the research on the structural properties of supply chain networks, focusing on their topological structure, key node identification, community detection, and vulnerability analysis. Relevant studies explore the topological structure of supply chain networks, uncovering the connections between nodes, hierarchical structures, and information flow paths between nodes. By analyzing factors such as node centrality, connection strength, and flow paths, the key nodes within the supply chain network are identified. Community detection algorithms are used to investigate the relationships between different structural parts and to analyze the positional structure, cooperative relationships, and interaction modes. Furthermore, quantitative evaluation indicators and management strategies are proposed for the robustness and resilience of supply chain networks. Further research has explored the management characteristics of supply chain networks, including risk propagation and competition game. Relevant studies have employed three main methods—epidemic model, cascading failure model, and agent-based model—to construct risk propagation models, simulate the spread of disruption risks, and analyze the mechanisms, paths, and extent of risk propagation within supply chain networks. These studies provide valuable insights for developing risk prevention and mitigation strategies. In addition, the game theory has been used to investigate the cooperative competition, resource allocation, and strategy selection among enterprises within the supply chain network. This paper reviews the research contents and emerging trends in supply chain studies based on complex network methods. It demonstrates the effectiveness and applicability of complex network theory in supply chain network research, discusses key challenges, such as how to obtain accurate, comprehensive, and timely supply chain network data, proposes standardized data processing methods, and determines the attributes of supply chain network nodes and the strength of their relationships. Furthermore, research on the structure of supply chain network has not yet fully captured the unique characteristics of supply chain networks. Existing models and methods for vulnerability assessment often fail to consider the dynamic and nonlinear characteristics of supply chain networks. Research on risk propagation in supply chains has not sufficiently integrated empirical data, overlooking the diversity of risk sources and the complexity of propagation paths. The asymmetry and incompleteness of information in supply chain networks, as well as multiple sources of uncertainty, make the prediction and analysis of multi-party decision-making behavior more complex. This paper also outlines several key directions for future research. One direction involves using high-order network theory to model interactions among multiple nodes and to describe the dynamics of multi-agent interactions within supply chain networks. Furthermore, integrating long short-term memory (LSTM) methods to process long-term dependence in time-series data can enhance the analysis of network structure evolution and improve the prediction of future states. The application of reinforcement learning algorithms can also adaptively adjust network structures and strategies according to changing conditions and demands, thereby improving the adaptability and response speed of supply chain networks in emergency situations. This paper aims to provide valuable insights for supplying chain research and promoting the development and application of complex network methods in this field.
2024, 73 (7): 074204.
doi: 10.7498/aps.73.20231691
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The micro-Doppler effect is a physical phenomenon generated by the micro-motion of objects and their components, which have a significant influence on improving radar detection and resolution capability and also enhancing the radar imaging and target recognition performance. The extraction of micro-Doppler frequency, as a commonly used time-frequency analysis tool, is of great significance in extracting and reconstructing the signal with micro-motion targets. The micro-motion characteristics for moving targets can be verified by using simulation through combining the theory of micro-Doppler effect with the frequency domain model of electromagnetic waves. The simulation research on the micro-motion characteristics of a three-dimensional target is conducted by using the finite element method. The influences of environmental conditions such as relative humidity, visibility, and the presence or absence of turbulence on echo intensity and time-frequency relationship are investigated theoretically. The simulation results indicate that parameters such as relative humidity and visibility, which affect the atmospheric attenuation coefficient, can reduce echo intensity and the period of time-frequency curve. By triggering off beam drift in the transmission path, turbulence can lead to “frequency shift deformation” of the time-frequency curve, degrading the extraction of target motion attitude. A motion attitude classification method is proposed in order to study the micro-Doppler effect better. According to whether the frequency shift changes with time, the motion attitude can be divided into frequency shift time-invariant motion and time-variant motion. Frequency shift time-variant motion includes translation, rolling and vibration. Vibration and rolling are motions that periodically change with time, requiring the comparison of instantaneous frequency shifts at any three times within a cycle. Translation is a time-variant motion with irregular frequency shifts over time, which involves studying instantaneous frequency shifts at any three times. Transient frequency shifts should be analyzed and compared at different times for these motions. The frequency shift time-invariant motion is mainly rotation obtained experimental results indicate that the amplitude, plus-minus, and spectral width of frequency shift at different positions are aimed at inverting the target shape, attitude, direction and velocity. Demodulating one-dimensional data obtained from the FFTshift function can obtain the time-frequency-intensity relationship. This multi-parameter analysis method is a multi-dimensional processing method widely used in the fields of radar, sonar, and communication. The above research is conductive to the measurement of target macroscopic shape properties and the extraction of microscopic motion information, which lays the foundation for radar detection and recognition.
2024, 73 (9): 095201.
doi: 10.7498/aps.73.20231598
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Semiconductor chips are the cornerstone of the information age, which play a vital role in the rapid development of emerging technologies such as big data, machine learning, and artificial intelligence. Driven by the growing demand for computing power, the chip manufacturing industry has been committed to pursuing higher level of integration and smaller device volumes. As a critical step in the chip manufacturing processes, the etching process therefore faces great challenges. Dry etching (or plasma etching) process based on the low-temperature plasma science and technology is the preferred solution for etching the high-precision circuit pattern. In the low-temperature plasma, electrons obtain energy from the external electromagnetic field and transfer the energy to other particles through collision process. After a series of complex physical and chemical reactions, a large number of active particles such as electrons, ions, atoms and molecules in excited states, and radicals are finally generated, providing the material conditions for etching the substrate. Dry etching chamber is a nonlinear system with multiple space-time dimensions, multiple reaction levels and high complexity. Facing such a complex system, only by fully understanding the basic physical and chemical reaction of the etching process can we optimize the process parameters and improve the etching conditions, so as to achieve precision machining of the semiconductor and meet the growing demand of the chip industry for etching rate and yield. In the early days, the process conditions of dry etching were determined through the trial-and-error method, which is characterized by high cost and low yield. However, with the help of plasma simulation, nowadays people have been able to narrow the scope of experiment to a large extent, and find out efficiently the optimal process conditions in a large number of parameters. In this review, we first introduce the basic theory of the mostly used models for plasma simulation including kinetic, fluid dynamic, hybrid and global models, in which the electron collision cross sections are the key input parameters. Since the formation of the low-temperature plasma is driven by the electron-heavy particle collision processes, and the active species for plasma etching are generated in the reactions induced by electron impact, the accuracy and completeness of the cross-section data greatly affect the reliability of the simulation results. Then, the theoretical and experimental methods of obtaining the cross-section data of etching gases are summarized. Finally, the research status of the electron collision cross sections of etching atoms and molecules is summarized, and the future research prospect is discussed.
2024, 73 (6): 069301.
doi: 10.7498/aps.73.20231618
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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 (9): 094206.
doi: 10.7498/aps.73.20231772
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2024, 73 (7): 072802.
doi: 10.7498/aps.73.20231661
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Monte Carlo (MC) method is a powerful tool for solving particle transport problems. However, it is extremely time-consuming to obtain results that meet the specified statistical error requirements, especially for large-scale refined models. This paper focuses on improving the computational efficiency of neutron transport simulations. Specifically, this study presents a novel method of efficiently calculating neutron fixed source problems, which has many applications. This type of particle transport problem aims at obtaining a fixed target tally corresponding to different source distributions for fixed geometry and material. First, an efficient simulation is achieved by treating the source distribution as the input to a neural network, with the estimated target tally as the output. This neural network is trained with data from MC simulations of diverse source distributions, ensuring its reusability. Second, since the data acquisition is time consuming, the importance principle of MC method is utilized to efficiently generate training data. This method has been tested on several benchmark models. The relative errors resulting from neural networks are less than 5% and the times needed to obtain these results are negligible compared with those for original Monte Carlo simulations. In conclusion, in this work we propose a method to train neural networks, with MC simulation results containing importance data and we also use this network to accelerate the computation of neutron fixed source problems.
2024, 73 (5): 054701.
doi: 10.7498/aps.73.20231745
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In the present paper, a hybrid RANS/LES model with the wall-modelled LES capability (called WM-HRL model) is developed to perform the high-fidelity CFD simulation investigation for complex flow phenomena around a bluff body with coherent structure in subcritical Reynolds number region. The proposed method can achieve a fast and seamless transition from RANS to LES through a filter parameter rk which is only related to the space resolution capability of the local grid system for various turbulent scales. Furthermore, the boundary positions of the transition region from RANS to LES, as well as the resolving capabilities for the turbulent kinetic energy in the three regions, i.e. RANS, LES and transition region, can be preset by two guide index parameters nrk1-Q and nrk2-Q. Through a series of numerical simulations of the flow around a circular cylinder at Reynolds number Re = 3900, the combination conditions are obtained for such two guide index parameters nrk1-Q and nrk2-Q that have the capability of high-fidelity resolving and capturing temporally- and spatially-developing coherent structures for such complex three-dimensional flows around such a circular cylinder. The results demonstrate that the new WM-HRL model is capable of accurately resolving and capturing the fine spectral structures of the small-scale Kelvin-Helmholtz instability in the shear layer for flow around such a circular cylinder. Furthermore, under a consistent grid system, through different combinations of these two guide index parameters rk1 and rk2, the fine structuresof the recirculation zones with two different lengths and the U-shaped and V-shaped distribution of the average stream-wise velocity in the cylinder near the wake can also be obtained.
2024, 73 (5): 058701.
doi: 10.7498/aps.73.20231569
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To achieve rapid and accurate identification of genetically modified (GM) and non-GM rapeseed oils, a support vector machine (SVM) model based on an improved mayfly optimization algorithm and coupled with the terahertz time-domain spectroscopy, is proposed. Two types of GM rapeseed oils and two types of non-GM rapeseed oils are selected as research subjects. Their spectral information is acquired by using the terahertz time-domain spectroscopy. The observations show that GM rapeseed oils exhibit stronger terahertz absorption characteristics than non-GM rapeseed oils. However, their absorption spectra are highly similar, making direct differentiation difficult through visual inspection alone. Therefore, SVM is used for spectral recognition. Considering that the classification performance of SVM is significantly affected by its parameters, the mayfly optimization algorithm is combined to optimize these parameters. Furthermore, adaptive inertia weight and Lévy flight strategies are introduced to enhance the global search capability and robustness of the mayfly optimization algorithm, thus addressing the issue of easily becoming trapped in local optima in the optimization process. Moreover, principal component analysis is used to reduce the dimensionality of the absorbance data in a 0.3–1.8 THz range, aiming to extract critical features, thereby enhancing modeling efficiency and reducing redundancy in spectral data. Experimental results demonstrate that the improved mayfly optimization algorithm effectively identifies the optimal parameter combination for SVM, thereby enhancing the overall performance of the identification model. The proposed SVM model, in which the improved mayfly optimization algorithm is used, can achieve a recognition accuracy of 100% for the four types of rapeseed oils, surpassing the 98.15% accuracy achieved by the SVM model with the original mayfly optimization algorithm. Thus, this study presents a rapid and effective new approach for identifying GM rapeseed oils and offers a valuable reference for identifying other genetically modified substances.
2024, 73 (1): 017503.
doi: 10.7498/aps.73.20231940
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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): 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.
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