Most Cited
2022, 71 (12): 127504.
doi: 10.7498/aps.71.20220301
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Two-dimensional (2D) magnetic materials with magnetic anisotropy can form magnetic order at finite temperature and monolayer limit. Their macroscopic magnetism is closely related to the number of layers and stacking forms, and their magnetic exchange coupling can be regulated by a variety of external fields. These novel properties endow 2D magnetic materials with rich physical connotation and potential application value, thus having attracted extensive attention. In this paper, the recent advances in the experiments and theoretical calculations of 2D magnets are reviewed. Firstly, the common magnetic exchange mechanisms in several 2D magnetic materials are introduced. Then, the geometric and electronic structures of some 2D magnets and their magnetic coupling mechanisms are introduced in detail according to their components. Furthermore, we discuss how to regulate the electronic structure and magnetism of 2D magnets by external (field modulation and interfacial effect) and internal (stacking and defect) methods. Then we discuss the potential applications of these materials in spintronics devices and magnetic storage. Finally, the encountered difficulties and challenges of 2D magnetic materials and the possible research directions in the future are summarized and prospected.
2022, 71 (13): 133702.
doi: 10.7498/aps.71.20220071
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The precision dynamic measurement of absolute gravity based on the cold atom interferometer can provide a new method for marine gravimetry, so that it has attracted more attention. Based on the homemade shipborne cold atom interferometric absolute gravity measurement system, we carry out a series of measurement experiments in a certain area of the South China Sea. Under dynamic conditions, the suppression of measurement noise is essential for the improvement of the measurement performance. According to the physical model of the measurement system, in this paper a data processing method is proposed based on the extended Kalman filter algorithm for the absolute gravity dynamic measurement. The observed atomic interference fringe data are filtered in the time domain to estimate the absolute gravity value. Based on this processing method, the sensitivity of absolute gravity measurement under the condition of ship speed less than 2.1 km/h is improved from 300.2 mGal/Hz1/2 to 136.8 mGal/Hz1/2 (T = 4 ms). Comparing the processed data with the data calculated from the earth gravity model (XGM2019), it is found that both of the data are in good agreement. These results confirm the effectiveness of the data processing method proposed in this paper, and provide a new processing method of suppressing the measurement noise of shipborne cold atom interferometric absolute gravity measurement system.
2022, 71 (5): 058504.
doi: 10.7498/aps.71.20211883
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Sensors play an important role in Internet of Things (IoT) industry and account for a rapidly growing market share. Among them, the magnetic sensor based on tunneling magnetoresistance (TMR) effect possesses great potential applications in the fields of biomedical, navigation, positioning, current detection, and non-destructive testing due to its extremely high sensitivity, small device size and low power consumption. In this paper, we focus on the development of TMR sensor technology routes, covering a series of research advances from a sensor transducer to three-dimensional magnetic field detection, and then to the applications. Firstly, we recall the development history of TMR sensors, explain its working principle, and discuss the method to improve the output linearity of single magnetic tunnel junction. Next, we state the Wheatstone-bridge structure, which can inhibit temperature drift in detail and review several methods of fabricating the full bridge of TMR sensors. Furthermore, for the market demand of three-dimensional magnetic field detection, we summarize the methods of designing and fabricating three-dimensional sensing structure of the TMR sensor. At the same time, we list several optimization schemes of TMR sensor performance in terms of sensitivity and noise level. Finally, we discuss two types of emerging applications of TMR sensors in recent years. The TMR sensors can also be used in intelligence healthcare due to their ultra-high sensitivity. In addition, devices from the combination of spin materials and MEMS structure have attracted wide attention, especially, because of the large commercial market of microphones, spin-MEMS microphones utilized TMR techniques will be the next research hotspot in this interdisciplinary field.
2022, 71 (11): 110701.
doi: 10.7498/aps.71.20220099
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When capturing images under low-light lighting conditions, the resulting images often suffer low visibility. Such low-visibility images not only affect the visual effect but also cause many difficulties in practical application. Therefore, image enhancement technology under low-light conditions has always been a challenging problem in image algorithms. Considering that most of the existing image enhancement methods are based on the RGB color space enhancement technology, the correlation among the RGB three primary colors is ignored, which makes the color distortion phenomenon easy to occur when the image is enhanced. To solve the problems of poor image visibility and color deviation under low-light conditions, in this paper an advanced Retinex network enhancement method is proposed. In the method, firstly the low-light RGB image is transformed into HSV color space, the Retinex decomposition network is used to decompose and enhance the value component separately, and thus increasing the resolution of the value component through up-sampling operation; then, for the hue component and saturation component, the nearest neighbor interpolation is used to increase their resolutions, and the enhanced value component is combined to convert back to RGB color space to obtain the initial enhanced image; finally, the wavelet transform image fusion technology is used to fuse with the original low-light image to eliminate the over-enhanced part in the initial enhanced image. The analysis of experimental results shows that the method proposed in this paper has obvious advantages in brightness enhancement and color restoration of low-light images. Especially, comparing with the original Retinex network method, the NIQE value decreases by an average of 19.49%, and the image standard deviation increases by an average of 41.35%. The algorithm proposed in this paper is expected to be effectively used in the fields of security monitoring and biomedicine.
2022, 71 (23): 233601.
doi: 10.7498/aps.71.20221398
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In the era of highly thin, multi-functional and integrated electronic devices, it will inevitably lead to the heat accumulation inside the composite material, thereby seriously affecting the operation stability and service life of the equipment. How to realize the rapid and efficient heat conduction and heat dissipation of dielectric materials has become a bottleneck problem restricting the further development of electronic devices. The intrinsic thermal conductivity of traditional polyimide is low, which limits its application in electrical equipment, smart grid and other fields. The development of new high thermal conductivity polyimide dielectric film materials has become the focus of research. This paper introduces the thermal conduction mechanism of composite materials, summarizes the research progress and development status of thermally conductive polyimide films in recent years, and focuses on the effects of thermally conductive fillers, interface compatibility, and molding process of the thermal conductivity of materials. Finally, some key scientific and technical issues in the research are summarized and prospected in combination with the future development needs of thermally conductive polyimide composite dielectric materials.
2022, 71 (6): 068503.
doi: 10.7498/aps.71.20212076
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In the past 60 years, silicon-based semiconductor technology has triggered off the profound change of our information society, but it is also gradually approaching to the physical limit and engineering limit as well. Thus, the global semiconductor industry has entered into the post-Moore era. Carbon nanotube has many excellent electronic properties such as high mobility and ultra-thin body, so it has become a hopeful candidate for the new semiconductor material in the post-Moore era. After more than 20 years of development, carbon based electronic technology has made fundamental breakthroughs in many basic problems such as material preparation, Ohmic metal-semiconductor contact and gate engineering. In principle, there is no insurmountable obstacle in its industrialization process now. Therefore, in this paper the intrinsic advantages of carbon based electronic technology in the post-Moore era is introduced, the basic problems, progress and optimization direction of carbon based electronic technology are summarized, the application prospects in the fields of digital circuits, radio frequency electronics, sensing and detection, three-dimensional integration and chips for special applications are presented. Finally, the comprehensive challenges to the industrialization of carbon based electronic technology are analyzed, and its future development is also prospected.
2022, 71 (16): 160502.
doi: 10.7498/aps.71.20220593
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Using memristors to construct special chaotic systems is highly interesting and meaningful. A simple four-dimensional memristive chaotic system with an infinite number of coexisting attractors is proposed in this paper, which has a relatively simple form but demonstrates complex dynamical behavior. Here, we use digital simulations to further investigate the system and utilize the bifurcation diagrams to describe the evolution of the dynamical behavior of the system with the influence of parameters. We find that the system can generate an abundance of chaotic and periodic attractors under different parameters. The amplitudes of the oscillations of the state variables of the system are closely dependent on the initial values. In addition, the experimental results of the circuit are consistent with the digital simulations, proving the existence and feasibility of this memristive chaotic system.
2022, 71 (2): 023601.
doi: 10.7498/aps.71.20211876
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2022, 71 (3): 030501.
doi: 10.7498/aps.71.20211549
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2022, 71 (3): 038803.
doi: 10.7498/aps.71.20211401
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The current electrochemical models of lithium-ion power batteries have many problems, such as complex models, difficult modeling, low computational efficiency and poor aging evaluation effect. In this paper, a mechanism model (ADME) considering battery decay and aging is proposed. In this paper, the pseudo-two-dimensions (P2D) electrochemical model is first reduced by finite difference method to obtain a simplified P2D (SP2D). On the basis of SP2D model, a mechanism model considering battery decay and aging is proposed, which is based on the degradation and aging phenomenon caused by the side reactions between positive and negative electrodes. Secondly, the multivariate deviation compensation least square method is used to identify the model parameters. Finally, the terminal voltage output of SP2D model is compared with that of ADME model and the outputs from the two models are also analyzed through the cycle experiment on power battery aging performance, constant current and pulse condition. The results show that the ADME model is relatively simple, has high computational efficiency and estimation accuracy, and can effectively evaluate the aging decline of battery capacity, and obtain the ideal external characteristic curve of lithium ion power battery.
Three dimensional image encryption algorithm based on quantum random walk and multidimensional chaos
2022, 71 (17): 170303.
doi: 10.7498/aps.71.20220466
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With the development of computer network technology, people’s requirements for information security is increasing day by day. However, the classical encryption technology has the defects of small key space and easy crack. The problems of image encryption technology in protecting image information security and private content need solving urgently. As a new type of quantum key generator, quantum random walk has a large key space. Compared with the classical random walk, the computing speed and security are significantly improved. This paper presents a three-dimensional image encryption algorithm that is based on quantum random walk and involves Lorenz and Rossler multidimensional chaos. Firstly, Gaussian pyramid is used to segment the image. Secondly, the Hamming distances of several sub images are calculated by using the random sequence generated by quantum random walk and the random sequence generated by Lorenz chaotic system in multi-dimensional chaos, and then synthesized, and the Euclidean distances between the three RGB channels of the image are calculated. Finally, the sequence value obtained from the remainder of Hamming distance and Euclidean distance, as an initial value is input into the Rossler system in multi-dimensional chaos to generate a random sequence which is used as the key to XOR the RGB channel of the image so as to create an encrypted image. The corresponding decryption scheme is the inverse process of the encryption process. In addition, in terms of transmission security, this paper uses a blind watermark embedding algorithm based on DCT and SVD to embed the watermark information into the encrypted image, so that the receiver can extract the watermark and judge whether the image is damaged by the attack in the transmission process according to the integrity of the watermark information. If it is not attacked maliciously, the image will be decrypted. This operation further improves the protection of image information security.The experimental results show that the peak signal-to-noise ratio of the encrypted image is stable between 7 and 9 and the encryption effect is good, the GVD score is close to 1, the correlation of the encrypted image is uniformly distributed, and the correlation coefficient is close to 0, and the key space is 2128 in size and the encrypted histogram is evenly distributed, showing a high ability to resist statistical analysis attacks.
2022, 71 (2): 026701.
doi: 10.7498/aps.71.20211449
Abstract +
Dynamic gravity measurements can improve the survey efficiency of the gravity field, and can play an important role in implementing the basic geological surveys, resource exploration, and geophysical research. Based on cold atom gravimeter, inertial stabilization platform and the movable vehicle device, a system for dynamically measuring absolute gravity is built, and the dynamic measurement experiments are carried out. Firstly, the noise power spectra of the vertical vibration are measured at different moving velocities, and the influence of such a vibration on the measurement of absolute gravity is analyzed theoretically. Besides, the influence on the contrasts and offsets of the atomic interference fringes are evaluated from different moving velocities, then the effect of vibration compensation in the dynamic measurement environment is analyzed. When the maximum moving speed is 5.50 cm/s and the maximum vibration amplitude is 0.1 m/s2, the atomic interference fringes can still be rebuilt based on the technology of vibration compensation. On this basis, the atomic interference fringes are obtained at different values of T and different moving velocities, then the absolute gravity value in the dynamic measurement environment is evaluated. After the correction of the systematic system and subtraction by the initial value of absolute gravity, the final measured result is (–1.22 ± 2.42) mGal. Finally, the experiment on the static absolute gravity is conducted, and the two values are found to be not much different from each other through comparing the static measurement data with the dynamic measurement data. The experiment of dynamic absolute gravity measurement in this paper may provide the helpful reference data for the dynamic absolute gravity measurement with moving vehicles.
2022, 71 (11): 113701.
doi: 10.7498/aps.71.20220113
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Cold atom gravimeter is gradually developing towards miniaturization, dynamics and practicality. It is of great significance to apply it to deep and far sea absolute gravity measurement and underwater long navigation time and high-precision navigation. At present, most cold atom gravimeters are still in the state of laboratory static base or quasi-static base measurement, which is difficult to meet the gravity measurement needs in dynamic application scenarios. Therefore, the research on "static to dynamic" of cold atom interferometric gravity measurement is very urgent and key. In this paper, the basic principle of dynamic measurement is analyzed, the basic method of combined measurement of cold atom gravimeter and accelerometer is given, a set of absolute dynamic gravity measurement system based on cold atom gravimeter and inertial stabilization platform is built, and the ship-borne dynamic measurement experiment is carried out by using the combined measurement method of cold atom gravimeter and traditional accelerometer. Firstly, the continuous absolute gravity measurement for about 40 h is carried out in the laboratory static environment to preliminarily evaluate the performance of the cold atom gravimeter. The sensitivity is 447 µGal/$\sqrt {{\text{Hz}}} $ , and the long-term stability can reach 2.7 µgal. On this basis, the ship-borne experiment is conducted, the survey ship sails on the lake at a speed of about 4.6 kn, and the ship-borne absolute dynamic gravity measurement is carried out by means of repeated survey lines. After evaluation, the internal coincidence accuracy of the four repeated survey lines is 2.272 mGal, and the external coincidence accuracy values of the four voyages are 2.331, 1.837, 3.988 and 2.589 mGal respectively. Finally, according to the experimental results, the possible problems are further analyzed and summarized. This experimental study provides preliminary verification and technical scheme reference for marine absolute dynamic gravity measurement.
2022, 71 (17): 176401.
doi: 10.7498/aps.71.20220565
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How to use quantitative analysis methods to identify which nodes are the most important in complex network, or to evaluate the importance of a node relative to one or more other nodes, is one of the hot issues in network science research. Now, a variety of effective models have been proposed to identify important nodes in complex network. Among them, the gravity model regards the coreness of nodes as the mass of object, the shortest distance between nodes as the distance between objects, and comprehensively considers the local information of nodes and path information to identify influential nodes. However, only the coreness is used to represente the quality of the object, and the factors considered are relatively simple. At the same time, some studies have shown that the network can easily identify the core-like group nodes with locally and highly clustering characteristics as core nodes when performing k-core decomposition, which leads to the inaccuracy of the gravity algorithm. Based on the universal gravitation method, considering the node H index, the number of node cores and the location of node structural holes, this paper proposes an improved algorithm ISM and its extended algorithm ISM+. The SIR model is used to simulate the propagation process in several classical real networks and artificial networks, and the results show that the proposed algorithm can better identify important nodes in the network than other centrality indicators.
2022, 71 (7): 078702.
doi: 10.7498/aps.71.20211632
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In the era of The Internet of Things, how to develop a smart sensor system with sustainable power supply, easy deployment and flexible use has become an urgent problem to be solved. Triboelectric nanogenerator (TENG) driven by Maxwell’s Displacement Current can convert mechanical motion into electrical signals, thus it can be used as a self-powered sensor. Sensors based on TENGs have the advantages of simple structure and high instantaneous power density, which provide an important means to build intelligent sensor systems. Meanwhile, machine learning, as a technique with low cost, short development cycle, and strong data processing capabilities and predictive capabilities, is effective in processing the large amount of electrical signals generated by TENG. This article combines the latest research progress of TENG-based sensor systems for signal processing and intelligent recognition by employing machine learning techniques, and outlines the technical features and research status of this research direction from the perspectives of traffic safety, environmental monitor, information security, human-computer interaction and health motion detection. Finally, this article also in-depth discusses the current challenges and future development trends in this field, and analyzes how to improve in the future to open up a broader application space. It is suggested that the integration of machine learning technology and TENG-based sensors will promote the rapid development of intelligent sensor networks in the future.
2022, 71 (2): 024202.
doi: 10.7498/aps.71.20211122
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2022, 71 (9): 097301.
doi: 10.7498/aps.71.20212261
Abstract +
In this paper, the stability, electronic structure, optical properties, and work function of GaN/g-C3N4 heterojunction are studied by using the first-principles plane wave ultra-soft pseudopotential method based on density functional theory. The electric field effect is also considered. The results show that the total energy for each of the three stacking modes changes little for using the two different dispersion correction methods, i.e. Tkatchenko-Scheffler and Grimme, and the total energy of mode II is the lowest, indicating that the structure of mode II is the most stable. The lattice mismatch ratio and lattice mismatch energy of GaN/g-C3N4 van der Waals heterojunction are very low, indicating that the heterojunction has good stability. The heterojunction retains the basic electronic properties of GaN and g-C3N4 to a great extent and can be used as a direct bandgap semiconductor material. It can be known from the work function and differential charge diagram that the charge on the heterojunction interface is transferred from GaN to g-C3N4, and a built-in electric field orientating g-C3N4 from GaN is formed at the interface. The built-in electric field of the heterojunction can effectively separate the photogenerated electron-hole pairs, which is conducive to improving the photocatalytic capability of the system. Further analysis shows that the applied electric field reduces the bandgap of GaN/g-C3N4 heterostructure to varying degrees. It makes it easier for electrons to transit from valence band to conduction band, which is conducive to improving the photocatalytic activity of the system. In addition, when the applied electric field is –0.6 V/Å and 0.5 V/Å separately, the semiconductor metal phase transition occurs in the heterojunction. When the applied electric field is higher than 0.3 V/Å and lower than –0.4 V/Å, in the energy band arrangement of the heterojunction there occurs the transition from type I to type II. This can better realize the separation of photogenerated electron-hole pairs and further improve the photocatalytic capactivity of the system. Therefore, the construction of heterojunction and application of external electric field proposed in this work constitute an effective means to improve the photocatalytic activity of the system.
2022, 71 (7): 074701.
doi: 10.7498/aps.71.20211373
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Extraction and recognition of the features of flow field is an important research area of fluid mechanics. However, the wake flow field of object immersed in fluid is complicated in the case of medium- and high-Reynolds number, thus it is difficult to extract and recognize the key features by using traditional physical models and mathematical methods. The continuous development of deep learning theory provides us with a new method of recognizing the complex flow features. A new method of extracting the features of the flow time history is proposed based on deep learning in this work. The accuracy of four deep learning model for feature recognition is studied. The results show that the proposed model can identify different characteristics of the wake time history and object shapes accurately. Some conclusions can be obtained below (i) The model based on convolutional layers has higher accuracy and is suitable for analyzing the features of flow time history data. (ii) The residual convolutional network, with a deeper structure and more complex inter-layer structure, has highest accuracy for feature recognition. (iii) The proposed method can extract and recognize the flow features from the perspective of physical quantities time history, which is a high-accuracy method, and it is an important new way to study the features of flow physical quantities.
2022, 71 (24): 240502.
doi: 10.7498/aps.71.20221423
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2022, 71 (5): 050101.
doi: 10.7498/aps.71.20211504
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Aiming at the pilot design problem in channel estimation of large-scale multiple input multiple output (MIMO) systems, an adaptive autocorrelation matrix reduction parameter pilot optimization algorithm based on channel reconstruction error rate minimization is proposed under the framework of compression perception theory. Firstly, the system model and orthogonal matching pursuit (OMP) algorithm are introduced. Secondly, for minimizing the channel reconstruction error rate, the relation between the expected value of the correlation decision in each iteration of the OMP algorithm and the reconstruction error rate is analyzed. For the optimal expected value of the correlation decision, the relation between the channel reconstruction error rate and the correlation of the pilot matrix column under the OMP algorithm is derived, and the two criteria of optimizing the pilot matrix are obtained: the pilot matrix column correlation expectation and the variance minimization. Then the method of optimizing the pilot matrix is studied, and the corresponding adaptive autocorrelation matrix reduction parameter pilot matrix optimization algorithm is proposed. In each iteration, whether the average column correlation degree of the matrix to be optimized is reduced is used as a judgment condition. The autocorrelation matrix reduction parameter value is adjusted to make the parameters close to the theoretical optimization. The simulation results show that the proposed method has a better column correlation property and lower channel reconstruction error rate than the pilot matrix obtained, separately, by Gaussian matrix, Elad method and low power average column correlation method.
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