Electronic and magnetic properties of transition-metal-doped monolayer B2S2 within GGA + U framework.

Considering the significant role of magnetism induction in two-dimensional (2D) semiconductor materials, we systematically investigate the effects of various dopants from the 3d and 4d transition metal (TM) series, including Ti, V, Mn, Fe, Co, Ni, Cu, Zn, Zr, Nb, Mo, Ru, Rh, Pd, Ag and Cd, on the electronic and magnetic properties of monolayer B2S2 through first-principles calculations. The calculated formation energies indicate that substitutional doping at the B site with various TM atoms could be achieved under S-rich growth conditions. What matters is that with the exception of systems doped with Cu, Tc, and Ag elements, which exhibit non-magnetic semiconductor properties, all other doped systems demonstrate magnetism. Specifically, the Cr-, Ni- and Pd-doped monolayers are magnetic half-metals, while the rest are magnetic semiconductors. We have also performed calculations of magnetic couplings between two TM atoms with an impurity concentration of 3.12%, revealing the prevalence of weak magnetic coupling in the majority of the magnetic systems examined. Moreover, the monolayers doped with Cr, Zr and Pd atoms exhibit ferromagnetic ground states. These findings strongly support the high potential for inducing magnetism in the B2S2 monolayer through B-site doping.

CAW: A Remote-Sensing Scene Classification Network Aided by Local Window Attention.

Remote-sensing image scene data contain a large number of scene images with different scales. Traditional scene classification algorithms based on convolutional neural networks are difficult to extract complex spatial distribution and texture information in images, resulting in poor classification results. In response to the above problems, we introduce the vision transformer network structure with strong global modeling ability into the remote-sensing image scene classification task. In this paper, the parallel network structure of the local-window self-attention mechanism and the equivalent large convolution kernel is used to realize the spatial-channel modeling of the network so that the network has better local and global feature extraction performance. Experiments on the RSSCN7 dataset and the WHU-RS19 dataset show that the proposed network can improve the accuracy of scene classification. At the same time, the effectiveness of the network structure in remote-sensing image classification tasks is verified through ablation experiments, confusion matrix, and heat map results comparison.

Inspection Method of Rope Arrangement in the Ultra-Deep Mine Hoist Based on Optical Projection and Machine Vision.

Due to the importance of safety detection of the drum's rope arrangement in the ultra-deep mine hoist and the current situation whereby the speed, accuracy and robustness of rope routing detection are not up to the requirements, a novel machine-vision-detection method based on the projection of the drum's edge is designed in this paper. (1) The appropriate position of the point source corresponding to different reels is standardized to obtain better projection images. (2) The corresponding image processing and edge curve detection algorithm are designed according to the characteristics of rope arrangement projection. (3) The Gaussian filtering algorithm is improved to adapt to the situation that the curve contains wavelet peak noise when extracting the eigenvalues of the edge curve. (4) The DBSCAN (density-based spatial clustering of applications with noise) method is used to solve the unsupervised classification problem of eigenvalues of rope arrangement, and the distance threshold is calculated according to the characteristics of this kind of data. Finally, we can judge whether there is a rope arranging fault just through one frame and output the location and number of the fault. The accuracy and robustness of the method are verified both in the laboratory and the ultra-deep mine simulation experimental platform. In addition, the detection speed can reach 300 fps under the premise of stable detection.

Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM.

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization-support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

Design of High-Performance Wearable Energy and Sensor Electronics from Fiber Materials.

A fiber material is composed of a group of flexible fibers that are assembled in a certain dimensionality. With its good flexibility, high porosity, and large surface area, it demonstrates a great potential in the development of flexible and wearable electronics. In this work, a kind of nickel/active material-coated flexible fiber (NMF) electrodes, such as Ni/MnO2/reduced graphene oxide (rGO) NMF electrodes, Ni/carbon nanotube (CNT) NMF electrodes, and Ni/G NMF electrodes, is developed by a new general method. In contrast with previous approaches, it is for the first time that porous and rich hydrophilic structures of fiber materials have been used as the substrate to fully absorb active materials from their suspension or slurry and then to deposit a Ni layer on active material-coated fiber materials. The proposed processes of active material dip-coating and then Ni electroless plating not only greatly enhance the electrical conductivity and functional performance of fiber materials but also can be applied to an extensive diversity of fiber materials, such as fabrics, yarns, papers, and so on, with outstanding flexibility, lightweight, high stability, and conductivity for making kinds of energy and sensor devices. As demonstration, a two-dimensional (2D) Ni/MnO2/rGO NMF electrode is obtained for supercapacitors, showing excellent electrochemical performance for energy storage. Then, Ni/CNT NMF electrodes with different dimensionalities, including one-dimensional fiber-shaped, 2D plane, and three-dimensional spatial, are fabricated as various tensile and compressive strain sensors for observation of human's movements and health. Finally, a 2D Ni/graphene NMF electrode is developed for assembling triboelectric nanogenerators for mechanical energy harvesting. Benefiting from wearable property of the textile substrates, the obtained NMF electrodes are expected to be designed into kinds of wearable devices for the future practical applications. The NMF electrode designed in this work provides a simple, stable, and effective approach for designing and fabricating wearable energy and sensor electronics from fiber materials.

Design of Novel Wearable, Stretchable, and Waterproof Cable-Type Supercapacitors Based on High-Performance Nickel Cobalt Sulfide-Coated Etching-Annealed Yarn Electrodes.

Rapid advances in functional electronics bring tremendous demands on innovation toward effective designs of device structures. Yarn supercapacitors (SCs) show advantages of flexibility, knittability, and small size, and can be integrated into various electronic devices with low cost and high efficiency for energy storage. In this work, functionalized stainless steel yarns are developed to support active materials of positive and negative electrodes, which not only enhance capacitance of both electrodes but can also be designed into stretchable configurations. The as-made asymmetric yarn SCs show a high energy density of 0.0487 mWh cm-2 (10.19 mWh cm-3 ) at a power density of 0.553 mW cm-2 (129.1 mW cm-3 ) and a specific capacitance of 127.2 mF cm-2 under an operating voltage window of 1.7 V. The fabricated SC is then made into a stretchable configuration by a prestraining-then-releasing approach using polydimethylsiloxane (PDMS) tube, and its electrochemical performance can be well maintained when stretching up to a high strain of 100%. Moreover, the stretchable cable-type SCs are stably workable under water-immersed condition. The method opens up new ways for fabricating flexible, stretchable, and waterproof devices.

Solvothermal synthesis of hollow urchin-like SnO2 nanospheres with superior lithium storage behavior.

Hollow urchin-like SnO2 nanospheres with ultrathin nanorods have been successfully synthesized via a simple complex solvothermal route. The formation mechanism of the as-synthesized SnO2 nanospheres was simply explained. When tested as anode, the as-obtained hollow urchin-like SnO2 nanospheres exhibit excellent rate and cycling performances. It was expected that the as-synthesized SnO2 nanospheres could be applied as anode materials for future lithium-ion batteries.