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.
2015, 64 (3): 038802. doi: 10.7498/aps.64.038802
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.
2015, 64 (2): 020101. doi: 10.7498/aps.64.020101
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.
2015, 64 (5): 050501. doi: 10.7498/aps.64.050501
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.
2015, 64 (5): 058902. doi: 10.7498/aps.64.058902
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.
Design of an adaptive sliding mode controller for synchronization of fractional-order chaotic systems
2015, 64 (4): 040505. doi: 10.7498/aps.64.040505
In this paper, the synchronization of fractional-order chaotic systems is investigated. Based on sliding mode control and adaptive control theory, a fractional order integral sliding surface with strong robustness is designed, and an adaptive sliding controller is proposed for synchronizing the fractional-order chaotic systems with retaining the nonlinear part. Numerical simulations on synchronizing the Chen chaotic systems, the Liu chaotic systems, and Arneodo chaotic systems are carried out separately. The simulation results show the validity and feasibility of the adaptive sliding controller.
2015, 64 (6): 067503. doi: 10.7498/aps.64.067503
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.
An improved centroid localization algorithm based on iterative computation for wireless sensor network
2016, 65 (3): 030101. doi: 10.7498/aps.65.030101
Wireless sensor network (WSN) is a basic component of internet and it plays an important role in many application areas, such as military surveillance, environmental monitoring and medical treatment. Node localization is one of the interesting issues in the field of WSN. Now, most of the existing node localization algorithms can be divided into two categories. One is range-based measurement and the other is range-free measurement. The localization algorithm of range-based measurement can achieve better location accuracy than the localization algorithm of range-free measurement. However, they are generally very energy consuming. Therefore, the range-free measurements are most widely used in practical applications. According to the application of localization algorithm in WSN by range-free measurements, an improved centroid localization algorithm based on iterative computation for wireless sensor network is proposed. In this algorithm, the position relationship of the closed area surrounded by the anchor nodes inside the unknown node's communication range and the unknown node is obtained by approximate point-in-triangulation test at first. Different position relationships determine different stopping criteria for iteration. Then, the centroid coordinates of the closed area surrounded by the anchor nodes inside the unknown node's communication range and the received signal strength (RSSI) between the centroid node and the unknown node are calculated. The anchor node with the weakest RSSI would be replaced by the centroid node. By this method, the closed area surrounded by the anchor nodes inside the unknown node's communication range is reduced. The location accuracy is increased by the cyclic iterative method. With the change of the anchor node ratio, the communication radius of the unknown node and the effect of RSSI error, the algorithm performance is investigated by using simulated data. The simulation results validate that although the improved centroid localization algorithm performance will be lost when the number of the anchor nodes inside the unknown node communication range decreases, the new approach can achieve good performance under the condition of few anchor nodes inside the unknown node communication range and this method is of strong robusticity against RSSI error disturbance.
Internet public opinion chaotic prediction based on chaos theory and the improved radial basis function in neural networks
2015, 64 (11): 110503. doi: 10.7498/aps.64.110503
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.
2015, 64 (3): 038401. doi: 10.7498/aps.64.038401
The development of highly efficient and low-cost solar cells is the key to large-scale application of solar photovoltaic technology. In recent years, the solution-processed organic-inorganic perovskite solar cells attracted considerable attention because of their advantages of high energy conversion efficiency, low cost, and ease of processing. The ambipolar semiconducting characteristic of perovskite enables the construction of planar heterojunction architecture to be possible in perovskite-based solar cells. This kind of architecture avoids the use of mesoporous metal oxide film, which simplifies the processing route and makes it easier to fabricate flexible and tandem perovskite-based solar cells. Planar heterojunction perovskite solar cells can be divided into n-i-p type and p-i-n type according to the charge flow direction. Two interfaces are formed between perovskite film and hole/electron transport layer, where efficient charge separation can be realized. Hole and electron transport layers can form separated continuous paths for the transport of holes and electrons, thus beneficial to improving exciton separation, charge transportation, and collection efficiency. In addition, this planar architecture avoids the use of high temperature sintered mesoporous metal oxide framework; this is beneficial to expanding the choice of the charge transport materials. In this paper, we review the recent progress on the planar heterojunction perovskite solar cells. First, we introduce the material properties of perovskite, the evolution of device architecture, and the working principle of p-i-n type and n-i-p type planar heterojunction perovskite solar cells. Then, we review the recent progress and optimization of planar heterojunction perovskite solar cells from every aspect of perovskite preparation and the selection of electron/hole transport materials. Finally, we would like to give a perspective view on and address the concerns about perovskite solar cells.
2016, 65 (13): 130501. doi: 10.7498/aps.65.130501
Since wind has an intrinsically complex and stochastic nature, accurate wind power prediction is necessary for the safety and economics of wind energy utilization. Aiming at the prediction of very short-term wind power time series, a new optimized kernel extreme learning machine (O-KELM) method with evolutionary computation strategy is proposed on the basis of single-hidden layer feedforward neural networks. In comparison to the extreme learning machine (ELM) method, the number of the hidden layer nodes need not be given, and the unknown nonlinear feature mapping of the hidden layer is represented with a kernel function. In addition, the output weights of the networks can also be analytically determined by using regularization least square algorithm, hence the kernel extreme learning machine (KELM) method provides better generalization performance at a much faster learning speed. In the O-KELM, the structure and the parameters of the KELM are optimized by using three different optimization algorithms, i.e., genetic algorithm (GA), differential evolution (DE), and simulated annerling (SA), meanwhile, the output weights are obtained by a least squares algorithm just the same as by the ELM, but using Tikhonovs regularization in order to further improve the performance of the O-KELM. The utilized optimization algorithms of the O-KELM are respectively used to select the set of input variables, regularization coefficient as well as hyperparameter of kernel function. The proposed method is first applied to the direct six-step prediction for Mackey-Glass chaotic time series, under the same condition as the existing optimized ELM method. From the analysis of the simulation results it can be verified that the prediction accuracy of the proposed O-KELM method is increased by about one order of magnitude over that of the optimized ELM method. Furthermore, the DE-KELM algorithm can achieve the lowest root mean square error (RMSE). The O-KELM method is then applied to real-world wind power prediction instance, i.e., the Western Dataset from NERL. The 10-minute ahead single-step prediction as well as 20-minute ahead, 30-minute ahead, 40-minute ahead multi-step prediction for wind power time series are respectively implemented to evaluate the O-KELM method. Experimental results of each of the short-term wind power time series predictions at different time horizons confirm that the proposed O-KELM method tends to have better prediction accuracy than the optimized ELM method. Moreover, the GA-KELM algorithm outperforms other two O-KELM algorithms at future 10-minute, 20-minute, 40-minute ahead prediction in terms of the RMSE value. The DE-KELM algorithm outperforms other algorithms at future 30-minute ahead prediction in terms of the normalized mean square error (NMSE) and the RMSE value. The results from these applications demonstrate the effectiveness and feasibility of the proposed O-KLEM method. Therefore, the O-KELM method has a potential future in the field of wind power prediction.
2017, 66 (7): 070705. doi: 10.7498/aps.66.070705
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.
2017, 66 (7): 074207. doi: 10.7498/aps.66.074207
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.
2015, 64 (1): 016102. doi: 10.7498/aps.64.016102
High-purity flower-like MoS2 microspheres have been successfully synthesized by hydrothermal method using Na2MoO4 and CH3CSNH2 as precursors, and H4O40SiW12 as an additive. Samples are characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectrometer (EDS). XRD and EDS patterns show that the as-prepared samples are MoS2, which have good crystallinity with a well-stacked layered structure. SEM images show that the as-prepared MoS2 are composed of flower-like microspheres with a mean diameter about 300 nm, the structures of which are constructed from dozens of hundreds of MoS2 nano-sheet with a thickness of several nanometers. It is also found that the silicotungstic acid plays an important role in the formation of the flower-like MoS2 microspheres, which could affect the size and morphology of the MoS2. Flower-like MoS2 is not found in the as-prepared product without adding silicotungstic acid. A formation mechanism of MoS2 microspheres is tentatively given.
2017, 66 (3): 038902. doi: 10.7498/aps.66.038902
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.
2015, 64 (3): 033301. doi: 10.7498/aps.64.033301
Perovskite solar cells with a solid-state thin film structure have attracted great attention in recent years due to their simple structure, low production cost and superb photovoltaic performance. Because of the boost in power conversion efficiency (PCE) in short intervals from 3.8% to 19.3% at present, this hybrid cells have been considered as the next generation photovoltaic devices. It is expected that the efficiencies of individual devices could ultimately achieve 25%, which is comparable to the single-crystal silicon solar cell.In this article, the perovskite absorber, its basic device structure, and operating principles are briefly introduced. Since most of the high efficiency perovskite solar cells employ hole transporting materials (HTM), they could benefit the hole transport and improve the metal-semiconductor interface in the cells. This perspective gives analyses of some effective hole transporting materials for perovskite solar cell application. The hole transporting materials used in perovskite solar cell are classified into six categories according to their structures, including triphenylamine-based small molecule HTM, small molecule HTM containing N atom, sulfur-based small molecule HTM, sulfur-based polymer HTM, polymer HTM containing N atom and inorganic HTM. Emphasis is placed on the interplay of molecular structures, energy levels, and charge carrier mobility as well as device parameters. A critial look at various approaches applied to achieve desired materials and device performance is provided to assist in the identification of new directions and further advances.
Functional coupling analyses of electroencephalogram and electromyogram based on variational mode decomposition-transfer entropy
2016, 65 (11): 118701. doi: 10.7498/aps.65.118701
The functional corticomuscular coupling (FCMC) is defined as the interaction, coherence and time synchronism between cerebral cortex and muscle tissue, which could be revealed by the synchronization analyses of electroencephalogram (EEG) and electromyogram (EMG) firing in a target muscle. The FCMC analysis is an effective method to describe the information transfer and interaction in neuromuscular pathways. Forthermore, the multiscaled coherence analyses of EEG and EMG signals recorded simultaneously could describe the multiple spatial and temporal functional connection characteristics of FCMC, which could be helpful for understanding the multiple spatial and temporal coupling mechanism of neuromuscular system. In this paper, based on the adaptively decomposing signal into frequency band characteristis of variational mode decomposition (VMD) and the quantitatively detecting the directed exchange of information between two systems of transfer entropy (TE), a new methodvariational mode decomposition-transfer entropy (VMD-TE) is proposed. The VMD-TE method could quantitatively analyze the nonlinear functional connection characteristic on multiple time-frequency scales between EEG over brain scalp and surface EMG signals from flexor digitorum surerficialis, which are recorded simultaneously during grip task with steady-state force output.In this paper, application of VMD-TE method consists of two steps. Firstly, the EEG and EMG signals are adaptively decomposed into multi intrinsic mode functions based on variational mode decomposition method, respectively, to describe the information on different time-frequency scales. Then the transfer entropies between the different timefrequency scales of EEG and EMG are calculated to describe the nonlinear corticomuscular coupling characteristic in different pathways (EEGEMG and EMGEEG), to show the functional coupling strength (namely VMD-TE values). finally, the maximum VMD-TE values between the different time-frequency scales of EEG and EMG signals among the eight subjects are selected, to describe the discrepancies of FCMC interaction strength between all time-frequency scales. The results show that functional corticomuscular coupling is significant in both descending (EEGEMG) and ascending (EMGEEG) directions in the beta-band (15-35 Hz) in the static force output stage. Meanwhile, the interaction strength between EEG signal and the gamma band (50-72 Hz) of EMG signal in descending direction is higher than in ascending direction. Our study confirms that the beta oscillations of EEG travel bidirectionally between sensorimotor
Adaptive fuzzy synchronization for uncertain fractional-order chaotic systems with unknown non-symmetrical control gain
2015, 64 (7): 070503. doi: 10.7498/aps.64.070503
In this paper the synchronization problem for the uncertain fractional-order chaotic systems with unknown non-symmetrical control gain matrices is investigated by means of adaptive fuzzy control. Fuzzy logic systems are employed to approximate the unknown nonlinear functions. We decompose the control gain matrix into a positive definite matrix, a unity upper triangular matrix, and a diagonal matrix with diagonal entries +1 or -1. The positive matrix is used to construct the Lyapunov function; the diagonal matrix is employed to design the controller. Based on the fractional Lyapunov stability theorem, an adaptive fuzzy controller, which is accompanied by fractional adaptation laws, is established. The proposed methods can guarantee the boundedness of the involved signals as well as the asymptotical convergence of the synchronization errors. It should be pointed out that the methods for using quadratic Lyapunov function in the stability analysis of the fractional-order chaotic systems are developed in this paper. Based on the results of this paper, many control methods which are valid for integer-order nonlinear systems can be extended to control fractional-order nonlinear systems. Finally, the effectiveness of the proposed methods is shown by simulation studies.
2016, 65 (1): 018102. doi: 10.7498/aps.65.018102
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.
The model of interdependent network based on positive/negativecorrelation of the degree and its robustness study
2015, 64 (4): 048902. doi: 10.7498/aps.64.048902
The model of interdependent network based on positive/negative correlation of the degree is constructed by the typical Barabási-Albert network in this paper. Dependency modality and dependency degree are considered in the model. Two parameters F and K are defined, which represent the proportion of dependency node and the redundancy of dependency, respectively. We study the influences of different values of F and K on the robustness of interdependent network in cascading failures under degree-based attacks and random attacks and also compare the results with those from the random interdependent network model. The simulation results show that the robustness of both random independency and interdependent network based on positive/negative correlation of the degree decreases as F increases and increases as K increases; in the model of full interdependence (F = 1), the robustness of interdependent network based on positive correlation of the degree is optimal under random attacks; the interdependent network based on negative correlation of the degree shows stronger robustness in the model of partial interdependence (F= 0.2, 0.5, 0.8). While the interdependent network based on positive correlation of the degree shows poorer robustness with any value of F under degree-based attacks.