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The dimple of ball grid array (BGA) area with 70 mm × 70 mm size on load board for high performance integrated circuit final test is investigated by shadow moire at first, the dimple of BGA area decreases from 184.3 to 97.1 μm when six additional prepregs with 60 mm × 60 mm size are added at BGA area before hot lamination process. The micromorphology and stress/strain simulation are conducted to improve the coverage and reliability of copper metallization layer in through hole at that BGA area. The microcracks of electroless copper layer at the position of glass fiber and inner layer copper pad, which leads to serious crack after solder float, are well covered by subsequent electroplating copper layer. When the through holes at BGA area with 0.2 mm diameter and 7.0 mm depth are fabricated based on insulating dielectric material used for high-speed signal transmission, the simulation results point out that IT968 is better than M6G for the thermal shock reliability of through hole metallization layer. A load board vehicle with 126 layers and 8.3 mm thickness based on IT968 shows good interconnection structure reliability after 12 times 288°C solder float.

Solar-driven interfacial evaporation (SIE) faces challenges in practical applications due to low water transport rates and suboptimal light-to-heat conversion efficiency. To address these issues, innovative approaches are needed to enhance solar energy utilization and evaporator performance. This study presents a novel approach using the naturally structured fruit of Platanus acerifolia (PAF) combined with the fibrous crown hair of dandelions. The unique structure of PAF enhances solar light absorption and water transport, crucial for efficient SIE. We optimized PAF through a carbonization process after immersing it in transition metal solutions, leveraging its inherent structural properties to achieve a high carbon yield and improved thermal stability. The optimized material achieved a high evaporation rate of 2.17 kg m−2 h−1 and a light-to-heat energy conversion efficiency of 117.2%. These results demonstrate the potential of PAF as a highly efficient and easily produced material for solar water treatment applications. The facile production process and the natural abundance of PAF make it a promising and sustainable solution for SIE. [ABSTRACT FROM AUTHOR]

Frequent oil spills pose a serious threat to the ecological environment and to human health. Therefore, it is a global topic to develop adsorbents with photothermal and superhydrophobic properties for the effective absorption of a high-viscosity crude oil. Herein, for the first time, the multiwall carbon nanotubes (MWCNTs)-superlight clay (SLC) was prepared using a freeze-drying method. Then, it was modified with octadecyl trichlorosilane (OTS) to obtain a superhydrophobic adsorbent (OTS/MWCNTs-SLC). The surface temperature of OTS/MWCNTs-SLC reaches 70 °C quickly under 1 kW m–2 solar irradiation, which is essential to lower the viscosity of crude oil. Consequently, the adsorbent material demonstrated a crude oil absorption capacity of 19 ± 0.4 g/g and a light/heavy oil absorption capacity of 10–26 g/g. Additionally, the superhydrophobic photothermal OTS/MWCNTs-SLC exhibited mechanical and chemical stability and enabled continuous crude oil recovery. The prepared OTC/MWCNT-SLC broadens the application of SLC and provides a strategy for crude oil cleanup. [ABSTRACT FROM AUTHOR]

Photothermal soft actuators have become a hot research topic in recent years due to the unique light-to-heat conversion, mechanical deformation, contactless operation, and extended applications from bionic design to intelligent robots. Moreover, since superhydrophobic surfaces offer drag reduction, anticorrosive, and oil absorption properties, the synergistic effects expand their smartness in micro-robots, environmental remediation, bioengineering, transportation, wireless delivery, and oil spill recovery applications. In this review, we have provided an overview of soft actuators based on the photothermal effect. We begin with a comprehensive overview of the photothermal conversion mechanism and the photothermal actuation mechanism. It explores three different types of photothermal actuation, discussing their design and mechanisms. Furthermore, we focus on the latest advancements in photothermal soft actuators, categorizing them based on actuation mechanisms and addressing their significance and associated challenges. The photothermal soft actuators enable bionic-like soft movement in the horizontal and vertical directions through bending, contraction, and rotation in response to light-induced heat. Then, this review introduces various applications of photothermal soft actuators. Finally, we draw insightful conclusions and expose exciting new possibilities in the field of photothermal actuators to create innovative solutions for photothermal soft actuators that will eventually benefit society. [ABSTRACT FROM AUTHOR]

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.

Wind power is developing rapidly in the context of sustainable development, and a series of problems such as wind curtailment and power curtailment have gradually emerged. The forecast of power generation output has become one of the hotspots of current research. This paper proposes a wind power plant output ultra-short-time prediction technology based on variational modal decomposition and particle swarm optimization least squares vector machine. Variational Modal Decomposition (VMD) method decomposes the historical output data of wind power plants at multiple levels. At the same time, it explores the impact of various decomposition methods such as EMD decomposition on the prediction accuracy, and uses the least squares support vector machine based on particle swarm optimization algorithm. Predictive summation is performed on each level of data separately to obtain a more accurate prediction effect, which has a certain improvement in prediction accuracy compared with traditional prediction algorithms.

In recent years, under the dual pressure of resource shortage and environmental pollution, the photovoltaic (PV) power generation industry has flourished. The irradiance forecasting technology of PV power plants is of great significance for output prediction, grid dispatching and safe operation. Cloud cover is always the key factor making the irradiance fluctuate. In this article, colorful ground-based cloud images are collected by the all-sky imager every minute as the research object. Based on the traditional threshold method, a hybrid entropy threshold method is proposed to identify cloud clusters. Using the correlation analysis, among many impact factors with high correlation, five are extracted as input parameters of a BP network optimized by genetic algorithm (GA-BP). Through verification and comparison analysis, it is concluded that the recognition accuracy of the hybrid entropy threshold method is higher, and the average relative error can be controlled at about 5%. Based on this, the irradiance prediction of GA-BP also achieved better results than other models. It can meet the application requirements of PV power plants.

Developing an efficient multi-functional electrocatalyst with high efficiency and low cost to replace noble metals is significantly crucial for the industrial water electrolysis process and for producing sustainable green hydrogen (H2) fuel. Herein, ultrathin CuCo2S4 nanosheets assembled into highly open three-dimensional (3D) nanospheres of CuCo2S4 (Cu/Co = 33:67) were prepared by a facile one-pot solvothermal approach and utilized as a bifunctional electrocatalyst for efficient overall water splitting. The as-prepared CuCo2S4 is characterized structurally and morphologically; the BET surface area of the CuCo2S4 (Cu/Co = 33:67) catalyst was found to have a larger specific surface area (21.783 m2g−1) than that of other catalysts with a Cu/Co ratio of 67:33, 50:50, and 20:80. Benefiting from a highly open structure and ultrathin nanosheets with excellent exposure to catalytically active sites, CuCo2S4 (Cu/Co = 33:67) is identified as an efficient catalyst for the proton reduction and oxygen evolution reactions in 1 M KOH with an overpotential of 182 and 274 mV at 10 mA cm−2, respectively. As expected, a low cell voltage of 1.68 V delivers a current density of 10 mA cm−2. Stability and durability are also greatly enhanced under harsh alkaline conditions. Therefore, this work provides a simple strategy for the rational design of spinel-based transition metal sulfide catalysts for electrocatalysis. [ABSTRACT FROM AUTHOR]