Most Cited
2015, 64 (3): 038805.
doi: 10.7498/aps.64.038805
Abstract +
The efficiency of solar cells based on organic-inorganic hybrid perovskite materials has a rapid growth from 3.8% in 2009 to 19.3%. The perovskite material (CH3NH3PbX3) exhibits advantages of high absorbing coefficient, low cost, and easily synthesised, which achieved extremely rapid development in recent years and gains great concern from the academic circle. As we know, perovskite materials not only serve as light absorption layer, but also can be used as either electron or hole transport layer. Consequently, various structures are designed based on the function of the perovskite, such as the solid-state mesoscopic heterojunction, meso-superstructured planar-heterojunction, HTM-free and the organic structured layers. Besides, it is also attractive for its versatility in fabrication techniques: one-step precursor solution deposition, two-step sequential deposition, dual-source vapor deposition, and vapor-assisted solution processing etc. This review mainly introduces the development and mechanism of the perovskite solar cells performance and the fabrication methods of peroskite films, briefly describes the specific function and improvement of each layer, and finally discusses the challenges we are facing and the development prospects, in order to have a further understanding of perovskite solar cells and lay a solid foundation for the preparation of new structures of the perovskite solar cells.
2017, 66 (7): 074207.
doi: 10.7498/aps.66.074207
Abstract +
Distributed fiber-optic sensing (DFOS) is one of the most important parts in the fiber-optic sensing field, due to the following advantages:1) there is no need to manufacture sensors on the fiber; 2) fibers are able to realize transmission and detection simultaneously; 3) long-distance/large-scale sensing and networking can be accomplished prospectively; 4) the spatial distribution and measurement information of physical parameters such as temperature, strain and vibration, can be obtained continuously along the fiber link, and the number of sensing points on a single fiber can be up to several tens of thousands. Due to the above tremendous superiority, DFOS has found wide application prospects, including perimeter security, oil/gas exploration, electrical facilities and structure monitoring, etc. This paper overviews recent progress in ultra-long distributed fiber-optic static (Brillouin optical time-domain analyzer) and dynamic (phase-sensitive optical time-domain reflectometer) sensing at Key Laboratory of Optical Fiber Sensing and Communications, UESTC. This paper summarizes our work on both basic and application studies.
2017, 66 (7): 070705.
doi: 10.7498/aps.66.070705
Abstract +
With the superiority of anti-electromagnetic interference, corrosion resistance, light quality, small size and so on, optical fiber sensing technology is widely used in aerospace industry, petrochemical engineering, power electronics, civil engineering and biological medicine. It can be divided as discrete and distributed. Discrete optical fiber sensing utilizes fiber sensitive element as sensors to detect the quantity to be measured. Optical spectrum, light intensity and polarization are usually used as the sensitivity parameter because they can be modulated by parameter such as rotation, acceleration, electromagnetic field, temperature, pressure, stress, stress, vibration, humidity, viscosity, refractive index and so on. Fiber works as the channel and links the fiber sensor and demodulating equipment. After a long period of research, the discrete optical fiber sensing technology stretch out many branches, we discussed the most representative ones as follows, the fiber grating sensing technique, the fiber fabry perot sensing technique, the fiber gyroscope sensing technique, the fiber intracavity sensing technique, the fiber surface plasma sensing technique, hollow-core fiber whispering gallery mode sensing technique, magnetic fluid fiber sensing technique and fiber-based optical coherence tomography sensing technique. Based on optical effect as rayleigh scattering, Raman scattering and Brillouin scattering, distributed fiber sensing system uses fiber itself as a sensor, when the vibration, stress, voice or temperature acts on the fiber changes, the optical signal transfers inside the fiber will change accordingly. The fiber distributes in a large range and a long distance, then the signal can be located at different positions and realize the multi-position measurement. We discussed the main distributed fiber sensing technologies as follows, the interferometric disturbance fiber sensing technology, the optical frequency domain reflectometry fiber sensing technology, the -optical time domain reflectometer fiber sensing technology, the optical fiber Brillouin sensing technology and the optical fiber Raman sensing technology. The development of technology is promoting the integration and network of optical fiber sensing, now it also becomes a research hotspot. Fiber optic smart sensor network is formed by various discrete and discrete optical fiber sensors in certain topological structure with the function of self-diagnosis and self-healing. Current research concentrates in the following areas, the increase of the multiplex sensor number, the topological structure with higher robustness and the intelligent control of sensing network. In this paper, we discuss the origination, development and research progress of discrete, distributed optical fiber sensing technologies and optical fiber sensing network technology, and the future research direction is also prospected.
2018, 67 (20): 207701.
doi: 10.7498/aps.67.20181091
Abstract +
Piezoelectric functional materials have been extensively studied and employed in numerous devices. With the rapid development of modern industries, such as power plants, aerospace, automotive, renewable energy and material processing industries, the high temperature piezoelectric materials that can work in extreme environments are in great demand.
Piezoelectric materials including piezoelectric single crystals, ceramics and films, are at the heart of electromechanical actuation and sensing devices. A variety of applications where piezoelectric actuators and sensors operate at elevated temperatures (T 200℃) would be extremely desired. The actuators need to work efficiently with high strokes, torques, and forces while operating under relatively harsh conditions. These include high-temperature fans and turbines, motors for valves or natural gas industries, kiln automation, and actuators for automotive engines such as fuel injectors and cooling system elements. Yet, the majority of industrial actuator applications are at or below the 250℃ temperature limit. In addition to the increase in operational temperatures of piezoelectric motors and actuators, a future area of interest is high-temperature MEMS research, which can be used for high-temperature valving. On the other hand, the piezoelectric sensors have been widely used for structural health monitoring applications. This is due to their wide bandwidth, versatility, simplicity, high rigidity, high stability, high reproducibility, fast response time, wide operating temperature range, insensitivity to electric and magnetic fields, the capacity for miniaturization and minimal dependence on moving parts and low power consumption, and wide piezoelectric materials and mechanisms selections, which will greatly benefit the sensing applications. In addition to the temperature usage range, the piezoelectric sensors must withstand the harsh environments encountered in space, engine, power plants, and also need to possess high sensitivity, resistivity, reliability, stability and robustness.
In order to use the piezoelectric materials for a specific high temperature application, many aspects need to be considered together with piezoelectric properties, such as phase transition, thermal aging, thermal expansion, chemical stability, electrical resistivity, and the stability of properties at elevated temperature. In this paper, ferroelectric materials with high Curie point, including perovskite-type ferroelectrics, bismuth layer structured ferroelectrics, tungsten-bronze structured ferroelectrics, together with non-ferroelectric piezoelectric single crystals, are surveyed. The crystal structure characteristics, high temperature piezoelectric properties, and recent research progress are discussed. A series of high temperature piezoelectric devices and their applications are reviewed, including high temperature piezoelectric detectors, sensors, transducers, actuators, etc. Finally, recent important research topics, the future development of high temperature piezoelectric materials and the potential new applications are summarized.
2018, 67 (1): 016101.
doi: 10.7498/aps.67.20171473
Abstract +
Amorphous alloy is a kind of metallic materials prepared by rapidly cooling the alloy melt through hindering crystallization in cooling process. Due to the unique structure of atomic random packing, Fe-based amorphous alloys exhibit not only structural and property isotropy, but also small structural correlation length, small magnetic anisotropic constant, and then small coercivity Hc. Like crystalline Fe-based alloys, Fe-based amorphous alloys also possess high saturation induction Bs. As a result, research on engineering applications of Fe-based amorphous alloys has been promoted by their excellent soft magnetic properties. Now Fe-based soft magnetic amorphous/nanocrystalline alloys have been produced and applied to various areas on a large scale. Here in this paper, the processes of discovery, development and application of Fe-based soft magnetic amorphous alloys are reviewed, and the effects of chemical composition, structure and preparation technology on the soft magnetic properties are introduced and discussed. The obtained theoretic results and the technological innovation show that the great contributions have been made to the development and application of Fe-based soft magnetic amorphous/crystalline alloys. Based on the progress of structure and soft magnetic property and our understanding, the development process of the fundamental research and the application progress of Fe-based soft magnetic amorphous alloys could be divided into three periods. In addition, the present challenge topics in their researches and applications are proposed.
2015, 64 (6): 067503.
doi: 10.7498/aps.64.067503
Abstract +
This article first gives a brief review of magnetic structures, magnetic domains and topological magnetic textures and their relations. On the one hand, the magnetic domains are determined by the magnetic structures, the intrinsic magnetic properties and the micro-structural factors of a material. On the other hand, the magnetic domains could control the magnetization and demagnetization processes and also the technical magnetic properties of a material. Topology is found to have a close relation with physical properties of material. Recent interest has focused on topological magnetic textures, such as vortex, bubble, meron, skyrmion, and it has been found that the topological behaviors of these topological textures are closely related with magnetic properties of a material. Then this article introduces recent advances in magnetic structures, magnetic domains and topological magnetic textures, from views of the size effect, defects and interfaces. Finally, this article reviews briefly some results of investigation on the relations between microstructures, magnetic domains and magnetic properties of rare-earth permanent magnetic thin films, the topological magnetic textures and their dynamic behaviors of exchange coupled nanodisks. It has been concluded from the reviews on the literature that the investigation on anisotropic exchange-coupled rare-earth permanent magnets with high performance benefits the high efficient utilization of rare-earth resources. One could achieve optimal magnetic properties through magnetic domain engineering by adjusting the microstructures of magnetic materials. The concepts of topology is applied to various research fields, while the contributions from topological behaviors to physical properties are discovered in different materials. The researches on magnetic domains, topological magnetic ground state and excitation states and their dynamic behaviors are very important for a better understanding of quantum topological phase transitions and other topological relevant phenomena. It can be quite helpful for understanding the correlation between different topological states and their relationship with magnetic properties of a material, and also it will definitely contribute to the applications in various fields of magnetic materials.
2017, 66 (3): 038902.
doi: 10.7498/aps.66.038902
Abstract +
Ranking node importance is of great significance for studying the robustness and vulnerability of complex network. Over the recent years, various centrality indices such as degree, semilocal, K-shell, betweenness and closeness centrality have been employed to measure node importance in the network. Among them, some well-known global measures such as betweenness centrality and closeness centrality can achieve generally higher accuracy in ranking nodes, while their computation complexity is relatively high, and also the global information is not readily available in a large-scaled network. In this paper, we propose a new local metric which only needs to obtain the neighborhood information within two hops of the node to rank node importance. Firstly, we calculate the similarity of node neighbors by quantifying the overlap of their topological structures with Jaccard index; secondly, the similarity between pairs of neighbor nodes is calculated synthetically, and the redundancy of the local link of nodes is obtained. Finally, by reducing the influence of densely local links on ranking node importance, a new local index named LLS that considers both neighborhood similarity and node degree is proposed. To check the effectiveness of the proposed method of ranking node importance, we carry out it on six real world networks and one artificial small-world network by static attacks and dynamic attacks. In the static attack mode, the ranking value of each node is the same as that in the original network. In the dynamic attack mode, once the nodes are removed, the centrality of each node needs recalculating. The relative size of the giant component and the network efficiency are used for network connectivity assessment during the attack. A faster decrease in the size of the giant component and a faster decay of network efficiency indicate a more effective attack strategy. By comparing the decline rates of these two indices to evaluate the connectedness of all networks, we find that the proposed method is more efficient than traditional local metrics such as degree centrality, semilocal centrality, K-shell decomposition method, no matter whether it is in the static or dynamic manner. And for a certain ranking method, the results of the dynamic attack are always better than those of the static attack. This work can shed some light on how the local densely connections affect the node centrality in maintaining network robustness.
2016, 65 (21): 217502.
doi: 10.7498/aps.65.217502
Abstract +
Magnetocaloric effect（MCE) is the intrinsic property of a magnetic material near transition temperature and the magnetic refrigeration based on MCE has been demonstrated as a promising alternative to the conventional gas compression or expansion refrigeration due to its high energy efficiency and environmental friendliness. The development of magnetic refrigeration technology depends on the research progress of magnetic refrigerant materials with large MCEs. Lots of researches of material exploration and material optimization have promoted the progress of magnetic refrigeration technology in recent decades. In this paper, we introduce the basic theory of MCE and the development of refrigeration technology, review the research progress of large MCE materials both in low temperature range and in room temperature range, and specifically focus on the latest progress of some MCE materials. Low temperature MCE materials mainly include those rare earth based materials with low transition temperatures, such as binary alloys（RGa, RNi, RZn, RSi, R3Co and R12Co7), ternary alloys（RTSi, RTAl, RT2Si2, RCo2B2 and RCo3B2), and quaternary alloys（RT2B2C), where R denotes the rare earth and T represents the transition metal. Those materials mainly possess the second-order phase transitions and show good thermal hysteresis, magnetic hysteresis, and thermal conductivities. Room temperature MCE materials are mainly Gd-Si-Ge intermetallic compounds, La-Fe-Si intermetallic compounds, MnAs-based compounds, Mn-based Heusler alloys, Mn-based antiperovskite compounds, Mn-Co-Ge intermetallic compounds, Fe-Rh compounds, and perovskite-type oxides. The above materials usually have the first-order phase transitions and most of these materials possess the large MCEs in room temperature range, therefore they have received much attention home and abroad. Among those room temperature MCE materials, the La-Fe-Si series is considered to be the most promising magnetic refrigerant materials universally and our country has independent intellectual property rights of them. The further development prospects of MCE materials are also discussed at the end of this paper.
2015, 64 (3): 038802.
doi: 10.7498/aps.64.038802
Abstract +
Ever since the first organic-inorganic hybrid halogen perovskite solar cell was first used as a photo-voltaic material in 2009, reports on this type of solar cell have grown exponentially over the years. Up till May 2014, the photo-energy conversion efficiency of the perovskite solar cell have already achieved an efficiency approaching 20%. Surpassing the efficiency achieved by organic and dye synthesized solar cell, the perovskite solar cell is in good hope of reaching the efficiency compatible with that of mono-crystalline silicon solar cell, thus it is going to be the star in photo-voltaic industry. In a perovskite solar cell, the film-formation and electron-mobility in the electron transfer layer can dramatically affect its efficiency and life-span. Especially in the up-right structured device, the mesoscopic structures of the electron-transfer layer will directly influence the growth of the perovskite layer. The present researches of electron transport materials mainly focus on three aspects: (1) How to improve the instability in mesoporous TiO2-mesosuperstructured solar cells, that arises from light-induced desorption of surface-adsorbed oxygen. (2) How to obtain TiO2 or other electron transport materials at low temperature (sub 150 ℃) in order to be applicatable in flexible devices. (3) How to substitute the mesoporous TiO2 or compact TiO2 transport layer by organic or composite materials. This article devides the materials that are used to make the electron-transfer layer into three distinct groups according to their chemical composition: i.e. metal oxides, organic small molecules, and composite materials, and introduces about the role they play and the recent development of them in constructing the perovskite solar cell.
2020, 69 (17): 178102.
doi: 10.7498/aps.69.20200987
Abstract +
As an emerging type of electronic devices, flexible pressure sensors have more advantages than rigid sensors in human-computer interaction, healthcare, and tactile sensing in robots. These advantages, however, require the materials to be thin and soft. For applications in human bodies, the sensor needs to be biocompatible and mechanically match the biotissue such that they can be conformable to the skin textures, or be implanted in the body. Sensitivity, response time, limitation of detection, and stability are basic properties to evaluate a pressure sensor. Recently, some other parameters of flexible pressure sensors including pressure response range, pressure resolution, space resolution, and stretchability have also been studied, enabling such devices to have a wider application prospect. This review introduces about the state of the arts of flexible pressure sensors in recent years, and is intended to discuss the sensing mechanisms, properties, and potential applications of flexible tactile sensors. At last, we talk about the future of flexible tactile sensors.
2015, 64 (5): 058902.
doi: 10.7498/aps.64.058902
Abstract +
Structural hole nodes in complex networks play important roles in the network information diffusion. Unfortunately, most of the existing methods of ranking key nodes do not integrate structural hole nodes and other key nodes. According to the relevant research on structural hole theory as well as the key node ranking methods, network constraint coefficient, betweenness centrality, hierarchy, efficiently, network size, PageRank and clustering coefficient, 7 metrics are selected to rank the key nodes. Based on the 7 metrics, a ranking learning method based on ListNet is introduced to solve ranking key nodes by multi metrics. Comprehensive experiments are conducted based on different artificial networks and real complex networks. Experimental results with manual annotation show that the ranking method can comprehensively consider the structural hole nodes and other nodes with different important features. The ranking results on different networks are highly consistent with the manual ranking results. The spreading experiment results using signed to interference ratio propagation model show that SIR model can reach a maximum propagating ratio in a shorter propagating time initiated by TOP-K key nodes selected by our method than TOP-K key nodes selected by other methods.
2015, 64 (2): 020101.
doi: 10.7498/aps.64.020101
Abstract +
The identifying of influential nodes in large-scale complex networks is an important issue in optimizing network structure and enhancing robustness of a system. To measure the role of nodes, classic methods can help identify influential nodes, but they have some limitations to social networks. Local metric is simple but it can only take into account the neighbor size, and the topological connections among the neighbors are neglected, so it can not reflect the interaction between the nodes. The global metrics is difficult to use in large social networks because of the high computational complexity. Meanwhile, in the classic methods, the unique community characteristics of the social networks are not considered. To make a trade off between affections and efficiency, a local structural centrality measure is proposed which is based on nodes' a nd their ‘neighbors’ structural holes. Both the node degree and “bridge” property are reflected in computing node constraint index. SIR (Susceptible-Infected-Recovered) model is used to evaluate the ability to spread nodes. Simulations of four real networks show that our method can rank the capability of spreading nodes more accurately than other metrics. This algorithm has strong robustness when the network is subjected to sybil attacks.
2016, 65 (1): 018102.
doi: 10.7498/aps.65.018102
Abstract +
Recently, two-dimensional (2D) layered molybdenum disulfide (MoS2) has attracted great attention because of its graphene-like structure and unique physical and chemical properties. In this paper, physical structure, band gap structure, and optical properties of MoS2 are summarized. MoS2 is semiconducting and composed of covalently bonded sheets held together by weak van der Waals force. In each MoS2 layer, a layer of molybdenum (Mo) atoms is sandwiched between two layers of sulfur (S) atoms. There are three types of MoS2 compounds, including 1T MoS2, 2H MoS2, and 3R MoS2. As the number of layers decreases, the bad gap becomes larger. The bad gap transforms from indirect to direct as MoS2 is thinned to a monolayer. Changes of band gap show a great potential in photoelectron. Preparation methods of 2D MoS2 are reviewed, including growth methods and exfoliation methods. Ammonium thiomolybdate (NH4)2MoS4, elemental molybdenum Mo and molybdenum trioxide MoO3 are used to synthesize 2D MoS2 by growth methods. (NH4)2MoS4 is dissolved in a solution and then coated on a substrate. (NH4)2MoS4 is decomposed into MoS2 after annealing at a high temperature. Mo is evaporated onto a substrate, and then sulfurized into MoS2. MoO3 is most used to synthesize MoS2 on different substrates by a chemical vapor deposition or plasma-enhanced chemical vapor deposition. Other precursors like Mo(CO)6, MoS2 and MoCl5 are also used for MoS2 growth. For the graphene-like structure, monolayer MoS2 can be exfoliated from bulk MoS2. Exfoliation methods include micromechanical exfoliation, liquid exfoliation, lithium-based intercalation and electrochemistry lithium-based intercalation. For micromechanical exfoliation, the efficiency is low and the sizes of MoS2 flakes are small. For liquid exfoliation, it is convenient for operation to obtain mass production, but the concentration of monolayer MoS2 is low. For lithium-based intercalation, the yield of monolayer MoS2 is high while it takes a long time and makes 2H MoS2 transform to 1T MoS2 in this process. For electrochemistry lithium-based intercalation, this method saves more time and achieves higher monolayer MoS2 yield, and annealing makes 1T MoS2 back to 2H MoS2. The applications of 2D MoS2 in field-effect transistors, sensors and memory are discussed. On-off ratio field effect transistor based on MoS2 has field-effect mobility of several hundred cm2V-1-1 and on/off ratio of 108 theoretically.
2016, 65 (18): 186801.
doi: 10.7498/aps.65.186801
Abstract +
Nature always supplies inspirations to scientists and engineers. Many newfangled materials have been fabricated by learning from and mimicking nature. In daily life and industrial processes these bioinspired novel materials have been widely used. The special wettability of natural organisms is significant to their life and attractive to researchers, which inspires us to fabricate the functional interfacial materials with high performances. In the last decade, the bioinspired multiscale interfacial materials exhibiting superwettability have emerged as a new type of functional material. Superwettable materials offer great chances to solve numerous issues ranging from fundamental research to practical exploration, and from bionic philosophy to fabricating technology. Inspired by nature's example, researchers developed a series of scientific strategies of new materials and fabricating methods, technologies, and applications. Based on the requirement of developing advanced materials in the fields of energy, environment, healthcare and resource, superwettable materials possessing binary cooperative nanostructure have been widely investigated to solve scientific and technical problems. In this review, we firstly present the development history of bioinspired multiscale interfacial materials with superwettability and the theoretical basis of the wettability of solid surfaces. Secondly, the principles of superwettable functional surfaces in nature is revealed and the bionic designs of bioinspired materials are discussed in detail. Meanwhile the typical applications of superwettable materials such as self-cleaning, oil-water separation and green printing are introduced. Finally, the perspectives of the future development of bioinspired superwettable materials are proposed for further studying the superwettable materials.
2015, 64 (5): 050501.
doi: 10.7498/aps.64.050501
Abstract +
In this paper, we first introduce a mutual influence function among network nodes based on characteristics of information spreading in online social network. Then we put forward an information spreading model based on relative weight, analyze the propagation path and process of the network, and discuss the influence on different paths. Finally, the simulation experiments of the traditional SIR model and the model in this paper are conducted with six different network topologies. Results show that the two models have no significant difference in homogeneous networks, but there are significant differences in inhomogeneous networks. This result shows that the information spreading is influenced by the status of spreading nodes, and also shows that the real networks like Twitter and Sina Microblog have certain similarity in topological structure.
2019, 68 (7): 078501.
doi: 10.7498/aps.68.20181845
Abstract +
Gallium oxide (Ga2O3), with a bandgap of about 4.9 eV, is a new type of ultra-wide bandgap semiconductor material. The Ga2O3 can crystallize into five different phases, i.e. α, β, γ, δ, and ε-phase. Among them, the monoclinic β-Ga2O3 (space group: C2/m) with the lattice parameters of a = 12.23 Å, b = 3.04 Å, c = 5.80 Å, and β = 103.7° has been recognized as the most stable phase. The β-Ga2O3 can be grown in bulk form from edge-defined film-fed growth with a low-cost method. With a high theoretical breakdown electrical field (8 MV/cm) and large Baliga’s figure of merit, the β-Ga2O3 is a potential candidate material for next-generation high-power electronics (including diode and field effect transistor) and extreme environment electronics [high temperature, high radiation, and high voltage (low power) switching]. Due to a high transmittance to the deep ultraviolet-visible light with a wavelength longer than 253 nm, the β-Ga2O3 is a natural material for solar-blind ultraviolet detection and deep-ultraviolet transparent conductive electrode. In this paper, the crystal structure, physical properties and device applications of Ga2O3 material are introduced. And the latest research progress of β-Ga2O3 in deep ultraviolet transparent conductive electrode and solar-blind ultraviolet photodetector are reviewed. Although Sn doped Ga2O3 thin film has a conductivity of up to 32.3 S/cm and a transmittance greater than 88%, there is still a long way to go for commercial transparent conductive electrode. At the same time, the development history of β-Ga2O3 solar-blind ultraviolet photodetectors based on material type (nanometer, single crystal and thin film) is described in chronological order. The photodetector based on quasi-two-dimensional β-Ga2O3 flakes shows the highest responsivity (1.8 × 105 A/W). The photodetector based on ZnO/Ga2O3 core/shell micron-wire has a best comprehensive performance, which exhibits a responsivity of 1.3 × 103 A/W and a response time ranging from 20 ${\text{μ}}{\rm{s}}$ to 254 nm light at –6 V. We look forward to applying the β-Ga2O3 based solar-blind ultraviolet photodetectors to military (such as: missile early warning and tracking, ultraviolet communication, harbor fog navigation, and so on) and civilian fields (such as ozone hole monitoring, disinfection and sterilization ultraviolet intensity monitoring, high voltage corona detection, forest fire ultraviolet monitoring, and so on).
2019, 68 (12): 120701.
doi: 10.7498/aps.68.20190281
Abstract +
Modern infrared detector technology has a history of nearly eighty years. Since the first PbS photodiode was put into use during the World War II, infrared detectors have achieved significant progress, even the third-generation infrared systems have been proposed. In the past decades, the traditional infrared detectors represented by HgCdTe, InSb and InGaAs have been widely applied in military, remote sensing, communication, bioscience, and space exploration. However, the increasing applications demand higher performance infrared detectors. Especially in recent years, the intelligent infrared detection technique was strongly demanded in many high-tech fields such as artificial intelligence, virtual reality systems and smart city. Therefore, the fabricating of infrared detection systems with smaller size, lighter weight, lower power, higher performance and lower price has become an urgent task. At present, the infrared photodetectors are in an age of rapid change, and many new type of advanced infrared photodetectors come to the fore quickly. For the purpose of summarizing these detectors, they are reviewed covering four parts: microstructure coupled infrared detector, infrared detector based on band engineering, new type of low-dimensional material infrared detector, and new directions for traditional infrared detectors. In the infrared detection systems, these photodetectors can be fully used for their prominent performance. The microstructure coupled infrared detector can improve chip integration with high quantum efficiency. Precise design of band structure will raise the operating temperature for mid and long wavelenth infrared photodetectors. Owing to the unique structures and physical properties, low-dimensional material infrared photodetectors have shown their potential application value in flexibility and room temperature detection systems. The ability of avalanche photodetector to detect the extremely weak signal makes it possible using in the frontier science such as quantum private communication and three-dimensional radar imaging systems. The device based on hot electron effect provides a new idea for far infrared detection. The barrier detectors will reduce the manufacturing cost of traditional materials and the design is also very illuminating for other new materials. In this review, firstly we present the history of infrared photodetectors in short. Then the mechanism and achievements of the advanced infrared photodetectors are introduced in detail. Finally, the opportunities and challenges of infrared detection are summarized and predicted.
2015, 64 (11): 110503.
doi: 10.7498/aps.64.110503
Abstract +
Information of internet public opinion is influenced by many netizens and net medias; characteristics of this information are non regular, stochastic, and may be expressed by a nonlinear complex evolution system. Corresponding model is difficult to establish and effectively predicted using the traditional methods based on statistical and machine learning. Characteristics of internet public opinion are chaotic, so the chaos theory can be introduced to research first, then the information of internet public opinion having chaotic characteristic is proved by the Lyapunov index. The model to predict the development trend of internet public opinion is next established by the phase space reconstruction theory. Finally, the hybrid algorithm EMPSO-RBF which is based on EM algorithm and the RBF neural network optimized by the improved PSO algorithm is proposed to solve the model. The hybrid algorithm fully takes the advantage of the EM clustering algorithm and the improved PSO, so the RBF neural network is improved by initializing the network structure in the early stage and optimizing the network parameters later. First, the EM clustering algorithm is used to obtain the center value and variance, and the radial basis function is improved with the combination of traditional Gauss model. Then the relevant network parameters are obtained by the improved PSO algorithm which is based on error optimizing the network parameters constantly. The model algorithm can be accurately simulated in the time series of chaotic information by experiments which are validated by different chaotic time series information; and it can better describe the development trend of different information of internet public opinion. The predicted results are made for government to monitor and guide the information of internet public opinion and benefit the social harmony and stability.
2016, 65 (1): 010502.
doi: 10.7498/aps.65.010502
Abstract +
Recently, the research on traffic flow system based on some classical models, such as cellular automata and car-following models, has attracted much attention. Some meaningful achievements have been obtained in the past few years by scholars from various fields. This paper starts with literature review on traffic flow theory studies. Car-following models, including the initial model proposed by Newell in 1961 (Newell G F 1961 Oper. Res. 9 209) and some later modified ones (e.g. full velocity difference model, or FVD model for short) have been deeply investigated. Based on Newell's car-following model, an extension of car-following model with consideration of vehicle-to-vehicle (V2V) communication is then developed. The vehicle-to-vehicle communication technology, which was proposed in the early 2000s, enable vehicles to collect traffic condition information from other vehicles (e.g. speed, headway, position, acceleration, etc.) and provide them for drivers in almost real time. Compared with those without V2V devices, drivers with information from V2V devices can react to traffic flow fluctuation timelier and more precisely. To represent the pre-reaction of drivers to traffic flow information provided by V2V devices, a parameter, , is newly introduced into Newell's car-following model. Then by second-order Taylor series expansion, a new car-following model with the influence of V2V (called V2V model) is proposed. Neutral stability condition of V2V model as well as phase diagram is derived theoretically with linear analysis method. The phase diagram of linear stability condition is divided into stable and unstable regions. By analyzing stability performance of the proposed model, it is evident that V2V communication technology can improve the stability of traffic flow system. Numerical simulation is demonstrated to study the influence of V2V devices on traffic flow on the one hand, and to acquire density waves as well as hysteresis loops under different values of parameter on the other hand. The sensitive analysis method are adopted as well.The numerical simulation results indicate that: 1) when compared with FVD model, V2V model can make vehicles react to traffic flow fluctuation earlier and reduce the speed changes under start-up, brake and incident conditions; this indicates that the consideration of V2V devices can improve the safety and ride comfort of traffic flow system; 2) the V2V model is sensitive to the value changes of parameter and T; the stability of traffic flow can be improved if the value of parameter increases, or parameter T decreases; this outcome precisely agrees with the above theoretical analysis; 3) the characteristics of traffic flow can influence the performance of V2V technology: compared with under low density condition, V2V communication technology can significantly increase the average speed of traffic flow under high density condition.
2020, 69 (10): 100701.
doi: 10.7498/aps.69.20191935
Abstract +
Photovoltaic power generation is affected by weather and geographical environment, showing fluctuations and random multi-interference, and its output power is easy to change with changes in external factors. Therefore, the prediction of output power is crucial to optimize the grid-connected operation of photovoltaic power generation and reduce the impact of uncertainty. This paper proposes a hybrid model of both convolutional neural network (CNN) and long short-term memory neural network (LSTM) based on genetic algorithm (GA) optimization (GA-CNN-LSTM). First, the CNN module is used to extract the spatial features of the data, and then the LSTM module is used to extract the temporal features and nearby hidden states. Optimizing the hyperparameter weights and bias values of the LSTM training network through GA. At the initial stage, the historical data is normalized, and all features were analyzed by grey relational degree. Important features are extracted to reduce the computational complexity of the data. Then, the GA-optimized CNN-LSTM hybrid neural network model (GA-CNN-LSTM) is applied for photovoltaic power prediction experiment. The GA-CNN-LSTM model was compared with the single neural network models such as CNN and LSTM, and the CNN-LSTM hybrid neural network model without GA optimization. Under the Mean Absolute Percentage Error index, the GA-CNN-LSTM algorithm proposed in this paper reduces the error by 1.537% compared with the ordinary single neural network model, and 0.873% compared with the unoptimized CNN-LSTM hybrid neural network algorithm model. From the perspective of training and test running time, the GA-CNN-LSTM model takes a little longer than the single neural network model, but the disadvantage is not obvious. To sum up, the performance of GA-CNN-LSTM model for photovoltaic power predicting is better.
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