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Passivation materials play a crucial role in a wide range of high-efficiency, high-stability photovoltaic applications based on crystalline silicon and state-of-the-art perovskite materials. Currently, for perovskite photovoltaic, the mainstream passivation strategies routinely rely on crystalline materials. Herein, we have invented a new amorphous (lysine) 2 PbI 2 layer-enhanced halide perovskite. By utilizing a solid phase reaction between PbI 2 and lysine molecule, an amorphous (lysine) 2 PbI 2 layer is formed at surface/grain boundaries in the perovskite films. The amorphous (lysine) 2 PbI 2 with fewer dangling bonds can effectively neutralize surface/interface defects, achieving an impressive efficiency of 26.27% (certified 25.94%). Moreover, this amorphous layer not only reduces crystal lattice stress but also functions as a barrier against the decomposition of organic components, leading to suppressed de-structuring of perovskite and highly stable perovskite solar cells. The mainstream passivation strategies routinely rely on crystalline materials for perovskite photovoltaics. Here, authors utilize a solid phase reaction to prepare an amorphous (lysine) 2 PbI 2 layer to neutralize surface and interface defects, achieving device efficiency of over 26% for solar cells.

Underwater imagery is plagued by issues such as image blurring and color distortion, which significantly impede the detection and operational capabilities of underwater robots, specifically Autonomous Underwater Vehicles (AUVs). Previous approaches to image fusion or multi-scale feature fusion based on deep learning necessitated multi-branch image preprocessing prior to merging through fusion modules. However, these methods have intricate network structures and a high demand for computational resources, rendering them unsuitable for deployment on AUVs, which have limited resources at their disposal. To tackle these challenges, we propose a multi-teacher knowledge distillation GAN for underwater image enhancement (MTUW-GAN). Our approach entails multiple teacher networks instructing student networks simultaneously, enabling them to enhance color and detail in degraded images from various perspectives, thus achieving an image-fusion-level performance. Additionally, we employ middle layer channel distillation in conjunction with the attention mechanism to extract and transfer rich middle layer feature information from the teacher model to the student model. By eliminating multiplexed branching and fusion modules, our lightweight student model can directly generate enhanced underwater images through model compression. Furthermore, we introduce a multimodal objective enhancement function to refine the overall framework training, striking a balance between a low computational effort and high-quality image enhancement. Experimental results, obtained by comparing our method with existing approaches, demonstrate the clear advantages of our proposed method in terms of visual quality, model parameters, and real-time performance. Consequently, our method serves as an effective solution for real-time underwater image enhancement, specifically tailored for deployment on AUVs.

Autonomous underwater vehicles (AUVs) have been widely used in ocean missions. When they fail in the ocean, it is important to predict their trajectory. Existing methods rely heavily on historical trajectory data while overlooking the influence of the ocean environment on an AUV’s trajectory. At the same time, these methods fail to use the dependency between variables in the trajectory. To address these challenges, this paper proposes an AUV trajectory prediction model known as the nonlinear Kepler optimization algorithm–bidirectional long short-term memory–time-variable attention (NKOA-BiLSTM-TVA) model. This paper introduces opposition-based learning during the initialization process of the KOA and improves the algorithm by incorporating a nonlinear factor into the planet position update process. We designed an attention mechanism layer that spans both time and variable dimensions, called TVA. TVA can extract features from both the time and variable dimensions of the trajectory and use the dependency between trajectory variables to predict the trajectory. First, the model uses a convolutional neural network (CNN) to extract spatial features from the trajectory. Next, it combines a BiLSTM network with TVA to predict the AUV’s trajectory. Finally, the improved NKOA is used to optimize the model’s hyperparameters. Experimental results show that the NKOA-BiLSTM-TVA model has an excellent parameter optimization effect and higher prediction accuracy in AUV trajectory prediction tasks. It also achieves excellent results in ship trajectory prediction.

In this study, laser additive manufacturing technology is utilized with the objective of controlling the internal stress of the cladding layer to maintain it in a compressive stress state. The cladding material was carefully designed and selected, and an Fe-Cr-Ni alloy spiral gradient multi-layer coating with significant differences in alloy element content was prepared on the surface of 45 steel. Using an optical microscope, scanning electron microscope (SEM), and x-ray diffractometer (XRD), the effects of the angle between cladding directions on the crystallization morphology, microhardness, and wear resistance of the cladding layer were investigated. The experimental results indicate that the interlayer angle significantly affects the crystallization morphology and grain size of the cladding layer. As the interlayer texture angle increases from 30° to 90°, the grain size within the cladding layer is significantly refined, accompanied by improved hardness uniformity and higher hardness values. This phenomenon occurs because, when the layer is rotated by a certain angle, the intersection area between the fusion line of the cladding layer and the dendritic crystals within the contour of the previous layer increases. This results in a greater number of favorable sites for liquid-phase nucleation, thereby promoting grain refinement. Additionally, since the columnar crystals at the solid–liquid interface continue to grow along the secondary dendritic growth direction of the previous cladding layer, the spatial angle between the columnar crystals at the interface of the upper and lower layers changes as the interlayer texture angle increases. Consequently, the angle in the cross-sectional direction also increases. When the interlayer texture angle reaches 90°, an ‘L’-shaped structure is formed. The microstructure of the cladding layer with higher Ni content is mainly composed of martensite and austenite, leading to lower hardness and a higher tendency for adhesive wear. In contrast, the microstructure of the cladding layer with lower Ni content primarily consists of martensite with a small amount of retained austenite, resulting in higher microhardness and better resistance to adhesive wear. The spiral gradient structure of the coating effectively refines the grains and enhances the hardness of the cladding layer, providing a new method for the preparation of high-quality cladding coatings.

Wideband electromagnetic pulse detection is a crucial method for lightning disaster monitoring. However, the random nature of lightning events presents challenges in fulfilling real-time calibration requirements for electromagnetic pulse sensors. This paper introduces a segmented ultra-wideband TEM horn antenna tailored for portable calibration experiments in electromagnetic pulse detection systems. The radiating plates feature a four-section polygonal design, and an end-loaded metal plate is integrated to reduce reflection signal interference. Rigorous simulation analyses were performed on three key factors impacting antenna radiation performance: aperture impedance, tapering profile, and end loading configuration. Experimental results show that the designed antenna achieves a peak field strength of 48.9 V/m at a 10 m distance, with a rise time of 0.87 ns and a full width at half maximum of 1.75 ns. The operating frequency ranges from 48 MHz to 150 MHz, with main lobe beamwidths of 43° and 83° in the E-plane and H-plane radiation patterns, respectively. These parameters meet the technical requirements for electromagnetic pulse sensor calibration experiments.

To reduce the renewable energy waste and carbon emissions predicted for the current expansion plan, this study proposes a hierarchical collaborative optimization model for the planning of generation and transmission expansion plan in cross-regional power systems considering energy storage and load transfer. In the upper layer, the upper limit of expansion is determined according to China’s current policy and expansion plan for the power system. This level completes the annual power expansion plan and provides scale data of power generation facilities and supporting infrastructures for the lower level. The lower layer is the operation level, which simulates the operation of the power system throughout the year. To find the defects of the current plan and provide an optimization scheme, the optimization model is used to analyze China’s power system in 2030. The utilization of renewable energy and power facilities is analyzed, along with the carbon emissions. An improved power expansion plan that comprehensively considers energy storage, transmission and load transfer for China’s carbon peak is proposed. The proposed scheme increases the utilization rate of renewable energy to 97.058%, reduces CO2 emissions by 224 million tons, and reduces the installed capacity of thermal power by about 18.686 million kilowatts, verifying the effectiveness of the scheme.

Pure iodide formamidinium (FA) based-perovskite has emerged as highly promising candidates for perovskite photovoltaics, but it remains challenging to achieve long-term phase-stabilized FA-based perovskites. Herein, we present a physics-driven strategy of interfacial ferroelectricity, achieved by the integration of ferroelectric CsMnBr 3 nanocrystals (NCs) into FA-based perovskites. The ferroelectric field generated by these NCs promotes FA + cation ordering, modulates Pb–I framework, and enhances the structural regulation of the perovskite lattice. This synergistically increases the kinetic barrier for the undesired Pb-I octahedral transformation and raises the energy barrier for ion migration. The resulting perovskite materials exhibit high structure stability, enabling perovskite solar cell (PSC) minimodule to retain 99% of its initial efficiency after 1000 hours’ stability testing under 85% relative humidity at 85 °C. Owing to the improvement at the interface, the PSCs yield an efficiency of 26.62% (certified 26.40%), and the minimodules reach 24.67% (certified 23.23%). This work presents an effective approach to achieving high-performance, long-term stable perovskite optoelectronic devices through interfacial ferroelectric engineering. Achieving long-term phase-stabilized formamidinium (FA)-based perovskites remains challenging. Here, authors integrate ferroelectric CsMnBr3 nanocrystals into FA-based perovskites for ferroelectric field-mediated structural regulation, achieving maximum efficiency of 26.62% for stable solar cells.

The Spaceborne Global Lightning Location Network (SGLLN) serves the purpose of identifying transient lightning events occurring beneath the ionosphere, playing a significant role in detecting and warning of disaster weather events. To ensure the effective functioning of the wideband electromagnetic pulse detector, which is a crucial component of the SGLLN, it must be tested and verified with specific signals. However, the inherent randomness and unpredictability of lightning occurrences pose challenges to this requirement. Consequently, a high-power electromagnetic pulse radiation system with a 20 m aperture reflector is designed. This system is capable of emitting nanosecond electromagnetic pulse signals under pre-set spatial and temporal conditions, providing a controlled environment for assessing the detection capabilities of SGLLN. In the design phase, an exponentially TEM feed antenna has been designed firstly based on the principle of high-gain radiation. The feed antenna adopts a pulser-integrated design to mitigate insulation risks, and it is equipped with an asymmetric protective loading to reduce reflected energy by 85.7%. Moreover, an innovative assessment method for gain loss, based on the principle of Love’s equivalence, is proposed to quantify the impact of feed antenna on the radiation field. During the experimental phase, a specialized E-field sensor is used in the far-field experiment at a distance of 400 m. The measurements indicate that at this distance, the signal has a peak field strength of 2.2 kV/m, a rise time of 1.9 ns, and a pulse half-width of 2.5 ns. Additionally, the beamwidth in the time domain is less than 10°. At an altitude of 500 km, the spaceborne detector records a signal with a peak field strength of approximately 10 mV/m. Particularly, this signal transformed into a nonlinear frequency-modulated signal in the microsecond range across its frequency spectrum, which is consistent with the law of radio wave propagation in the ionosphere. This study offers a stable and robust radiation source for verifying spaceborne detectors and establishes an empirical foundation for investigating the impact of the ionosphere on signal propagation characteristics.

AUVs are autonomous underwater robots equipped with advanced sensors and navigation systems. Due to the complexity and uncertainty of the marine environment, AUVs are susceptible to the effects of the marine environment and may experience communication delays or even accidents. Based on the aforementioned issues, this paper proposes a prediction method for lost AUVs based on an adaptive optimization depth BiLSTM (AWOA-DBiLSTM) neural network model. To enhance prediction accuracy, AWOA-DBiLSTM employs a double BiLSTM to extract AUV features from positional information and physical attitude. Additionally, AWOA-DBiLSTM utilizes a gating mechanism to filter and reset physical attitude feature information to obtain features associated with positional information. After undergoing filtering operations, the physical attitude information of the AUV is fused with the position information to achieve trajectory prediction. For the first time, the differentiation and stratified extraction of AUV data features are presented in this paper. The experimental results demonstrate that the model achieves significant improvements in prediction accuracy and generalization, and the present study is of great significance for application in the task of predicting the trajectories of lost AUVs.

The accurate prediction of drift trajectories holds paramount significance for disaster response and navigational safety. The future positions of underwater drifters in the ocean are closely related to their historical drift patterns. Additionally, leveraging the complex dependencies between drift trajectories and ocean currents can enhance the accuracy of predictions. Building upon this foundation, we propose a Transformer model based on double-branch adaptive span attention (DBASformer), aimed at capturing the multivariate time-series relationships within drift history data and predicting drift trajectories in future periods. DBASformer can predict drift trajectories more accurately. The proposed adaptive span attention mechanism exhibits enhanced flexibility in the computation of attention weights, and the double-branch attention structure can capture the cross-time and cross-dimension dependencies in the sequences. Finally, our method was evaluated using datasets containing buoy data with ocean current velocities and Autonomous Underwater Vehicle (AUV) data. The raw data underwent cleaning and alignment processes. Comparative results with five alternative methods demonstrate that DBASformer improves prediction accuracy.