Semitransparent solar cells can provide not only efficient power‐generation but also appealing images and show promising applications in building integrated photovoltaics, wearable electronics, ...photovoltaic vehicles and so forth in the future. Such devices have been successfully realized by incorporating transparent electrodes in new generation low‐cost solar cells, including organic solar cells (OSCs), dye‐sensitized solar cells (DSCs) and organometal halide perovskite solar cells (PSCs). In this review, the advances in the preparation of semitransparent OSCs, DSCs, and PSCs are summarized, focusing on the top transparent electrode materials and device designs, which are all crucial to the performance of these devices. Techniques for optimizing the efficiency, color and transparency of the devices are addressed in detail. Finally, a summary of the research field and an outlook into the future development in this area are provided.
Recent developments of semitransparent organic solar cells, dye‐sensitized solar cells, and perovskite solar cells are reviewed with a focus on different device design, transparent top electrode materials, and the corresponding device fabrication techniques. Key issues related to the optimization of the efficiency, color, and transparency of the semitransparent photovoltaic devices are discussed in detail.
Virtual Reality (VR) technology uses computers to simulate the real world comprehensively. VR has been widely used in college teaching and has a huge application prospect. To better apply ...computer-aided instruction technology in music teaching, a music teaching system based on VR technology is proposed. First, a virtual piano is developed using the HTC Vive kit and the Leap Motion sensor fixed on the helmet as the hardware platform, and using Unity3D, related SteamVR plug-ins, and Leap Motion plug-ins as software platforms. Then, a gesture recognition algorithm is proposed and implemented. Specifically, the Dual Channel Convolutional Neural Network (DCCNN) is adopted to collect the user’s gesture command data. The dual-size convolution kernel is applied to extract the feature information in the image and the gesture command in the video, and then the DCCNN recognizes it. After the spatial and temporal information is extracted, Red-Green-Blue (RGB) color pattern images and optical flow images are input into the DCCNN. The prediction results are merged to obtain the final recognition result. The experimental results reveal that the recognition accuracy of DCCNN for the Curwen gesture is as high as 96%, and the recognition accuracy varies with different convolution kernels. By comparison, it is found that the recognition effect of DCCNN is affected by the size of the convolution kernel. Combining convolution kernels of size 5×5 and 7×7 can improve the recognition accuracy to 98%. The research results of this study can be used for music teaching piano and other VR products, with extensive popularization and application value.
Entropy weight method (EWM) is a commonly used weighting method that measures value dispersion in decision-making. The greater the degree of dispersion, the greater the degree of differentiation, and ...more information can be derived. Meanwhile, higher weight should be given to the index, and vice versa. This study shows that the rationality of the EWM in decision-making is questionable. One example is water source site selection, which is generated by Monte Carlo Simulation. First, too many zero values result in the standardization result of the EWM being prone to distortion. Subsequently, this outcome will lead to immense index weight with low actual differentiation degree. Second, in multi-index decision-making involving classification, the classification degree can accurately reflect the information amount of the index. However, the EWM only considers the numerical discrimination degree of the index and ignores rank discrimination. These two shortcomings indicate that the EWM cannot correctly reflect the importance of the index weight, thus resulting in distorted decision-making results.
Flexible photodetectors have attracted a great deal of research interest in recent years due to their great possibilities for application in a variety of emerging areas such as flexible, stretchable, ...implantable, portable, wearable and printed electronics and optoelectronics. Novel functional materials, including materials with zero‐dimensional (0D) and one‐dimensional (1D) inorganic nanostructures, two‐dimensional (2D) layered materials, organic semiconductors and perovskite materials, exhibit appealing electrical and optoelectrical properties, as well as outstanding mechanical flexibility, and have been widely studied as building blocks in cost‐effective flexible photodetection. Here, we comprehensively review the outstanding performance of flexible photodetectors made from these novel functional materials reported in recent years. The photoresponse characteristics and flexibility of the devices will be discussed systematically. Summaries and challenges are provided to guide future directions of this vital research field.
Flexible photodetectors are of great importance in next‐generation optoelectronics and can provide a number of new functionalities towards some practical applications. The development of flexible photodetectors based on various novel functional materials are reviewed. Their photoresponse properties and mechanical flexibility are systematically discussed. Conclusions on the current techniques and future challenges are presented.
Modern engineered systems generally work under complex operational conditions. However, most of the existing artificial intelligence (AI)-based prognostic methods still lack an effective model that ...can utilize operational conditions data for remaining useful life (RUL) prediction. This paper develops a novel prognostic method based on bidirectional long short-term memory (BLSTM) networks. The method can integrate multiple sensors data with operational conditions data for RUL prediction of engineered systems. The proposed architecture based on BLSTM networks includes three main parts: first, one BLSTM network is used to directly extract features hidden in the multiple raw sensors signals; second, another BLSTM network is employed to learn higher features from operational conditions signals and the learned features from the sensors signals; and, third, fully connected layers and a linear regression layer are stacked to generate the target output of the RUL prediction. Unlike other AI-based prognostic methods, the developed method can simultaneously model both sensors data and operational conditions data in a consolidated framework. The proposed approach is demonstrated through a case study on aircraft turbofan engines, and comparisons with other popular state-of-the-art methods are also presented.
•A novel refrigerant-based battery thermal management system is proposed.•Temperature distributions and boiling characteristics are predicted.•The maximum temperature is inversely correlated with ...refrigerant inlet velocity.•Temperature uniformity is predominantly affected by nucleate boiling heat transfer.
In this paper, a novel battery thermal management system (BTMS) using the dielectric, non-flammable HFE-7000 refrigerant is proposed for electric vehicles (EVs). Its thermal performance is studied both numerically and experimentally. The refrigerant flows and boils on the battery wall surfaces, which lowers the thermal contact resistance as well as enhances the heat transfer process. Therefore, the thermal performance of the battery module is improved. The results indicate that forced convection heat transfer of the liquid refrigerant is dominating in the control of the temperature rise in the battery module. The maximum battery temperature drops to 35.10°C at 0.3ms-1 inlet velocity and a 5C discharge rate. In contrast, the temperature uniformity between individual battery cells primarily depends on the nucleate boiling heat absorption and local perturbation of the two-phase turbulent flow. A temperature difference of no more than 3.71°C can be observed at 5C discharge rate and 0.1ms- 1. In addition, good agreement was found between the numerical results and experimental data.
Organic thin‐film transistors (OTFTs) show promising applications in various chemical and biological sensors. The advantages of OTFT‐based sensors include high sensitivity, low cost, easy ...fabrication, flexibility and biocompatibility. In this paper, we review the chemical sensors and biosensors based on two types of OTFTs, including organic field‐effect transistors (OFETs) and organic electrochemical transistors (OECTs), mainly focusing on the papers published in the past 10 years. Various types of OTFT‐based sensors, including pH, ion, glucose, DNA, enzyme, antibody‐antigen, cell‐based sensors, dopamine sensor, etc., are classified and described in the paper in sequence. The sensing mechanisms and the detection limits of the devices are described in details. It is expected that OTFTs may have more important applications in chemical and biological sensing with the development of organic electronics.
Organic thin‐film transistors, including organic field‐effect transistors and organic electrochemical transistors, can be used in various types of chemical and biological sensors, such as pH, humidity, ion, glucose, DNA, enzyme, antibody‐antigen, cell and dopamine sensors. The organic transistors are expected to have more important sensing applications with the development of organic electronics.
Beyond the scope of Hermitian physics, non-Hermiticity fundamentally changes the topological band theory, leading to interesting phenomena, e.g., non-Hermitian skin effect, as confirmed in ...one-dimensional systems. However, in higher dimensions, these effects remain elusive. Here, we demonstrate the spin-polarized, higher-order non-Hermitian skin effect in two-dimensional acoustic higher-order topological insulators. We find that non-Hermiticity drives wave localizations toward opposite edges upon different spin polarizations. More interestingly, for finite systems with both edges and corners, the higher-order non-Hermitian skin effect leads to wave localizations toward two opposite corners for all the bulk, edge and corner states in a spin-dependent manner. We further show that such a skin effect enables rich wave manipulation by configuring the non-Hermiticity. Our study reveals the intriguing interplay between higher-order topology and non-Hermiticity, which is further enriched by the pseudospin degree of freedom, unveiling a horizon in the study of non-Hermitian physics.
Abstract
The quantum spin Hall effect lays the foundation for the topologically protected manipulation of waves, but is restricted to one-dimensional-lower boundaries of systems and hence limits the ...diversity and integration of topological photonic devices. Recently, the conventional bulk-boundary correspondence of band topology has been extended to higher-order cases that enable explorations of topological states with codimensions larger than one such as hinge and corner states. Here, we demonstrate a higher-order quantum spin Hall effect in a two-dimensional photonic crystal. Owing to the non-trivial higher-order topology and the pseudospin-pseudospin coupling, we observe a directional localization of photons at corners with opposite pseudospin polarizations through pseudospin-momentum-locked edge waves, resembling the quantum spin Hall effect in a higher-order manner. Our work inspires an unprecedented route to transport and trap spinful waves, supporting potential applications in topological photonic devices such as spinful topological lasers and chiral quantum emitters.
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