Vibration measurement and analysis play an important role in diagnosing mechanical faults, but existing vibration sensors are limited by issues such as dependence on external power sources and high costs. To overcome these challenges, the use of triboelectric nanogenerator (TENG)−based vibration sensors has recently attracted attention. These vibration sensors measure a small range of vibration frequencies and are not suitable for measuring high-frequency vibrations. Herein, a self-powered vibration sensor based on an elastic steel triboelectric nanogenerator (ES−TENG) is proposed. By optimizing the elastic steel sheet structure and combining time-frequency transformation and filtering processing methods, the measurement of medium- and high-frequency vibrations is achieved. These results demonstrate that the ES−TENG can perform vibration measurements in the range of 2–10,000 Hz, with a small average error (~0.42%) between the measured frequency and external vibration frequency values. Therefore, the ES−TENG can be used as a self-powered, highly-accurate vibration sensor for intelligent machinery monitoring.

With the advantages of superior wear resistance, mechanical durability, and stability, the liquid–solid triboelectric nanogenerator (LS-TENG) has been attracting much attention in the field of energy harvesting and self-powered sensors. However, most of the studies on LS-TENG focused on device innovations, changes in solid materials, and the effect of solid properties on output performance, and there is a lack of studies on liquids, especially at the molecular level. A U-tube LS-TENG was assembled to conduct experiments, whereby the effects of molecular structures, including molecular composition, carbon chain length, functional groups and material properties on the output performance were investigated. The deuterium replacing hydrogen and the atomic compositions could not achieve the enhancement of the output performance. Whether the chemical functional groups improve the output performance of LS-TENG depends on the mating solid material. Hydroxyl and cyanogenic groups can improve the output performance for the FEP case, while amide and cyanogenic groups can improve the output performance for the PTFE case. The order of output performances for functional groups of four groups of liquids with both FEP and PTFE materials is also obtained. It was also found that the dielectric constant is not positively correlated with the output performance. The results of this study might provide a reference for the deeper study and application of LS-TENG.

Precise prediction of the power generation of photovoltaic (PV) stations on the island contributes to efficiently utilizing and developing abundant solar energy resources along the coast. In this work, a hybrid short-term prediction model (ICMIC-POA-CNN-BIGRU) was proposed to study the output of a fishing–solar complementary PV station with high humidity on the island. ICMIC chaotic mapping was used to optimize the initial position of the pelican optimization algorithm (POA) population, enhancing the global search ability. Then, ICMIC-POA performed hyperparameter debugging and L2-regularization coefficient optimization on CNN-BIGRU (convolutional neural network and bidirectional gated recurrent unit). The L2-regularization technique optimized the loss curve and over-fitting problem in the CNN-BIGRU training process. To compare the prediction effect with the other five models, three typical days (sunny, cloudy, and rainy) were selected to establish the model, and six evaluation indexes were used to evaluate the prediction performance. The results show that the model proposed in this work shows stronger robustness and generalization ability. K-fold cross-validation verified the prediction effects of three models established by different datasets for three consecutive days and five consecutive days. Compared with the CNN-BIGRU model, the RMSE values of the newly proposed model were reduced by 64.08%, 46.14%, 57.59%, 60.61%, and 34.04%, respectively, in sunny, cloudy, rainy, continuous prediction 3 days, and 5 days. The average value of the determination coefficient $R^{2 }$of the 20 experiments was 0.98372 on sunny days, 0.97589 on cloudy days, and 0.98735 on rainy days.

Conventional solar power tower (SPT) systems often suffer from significant heat transfer exergy destruction due to large temperature differences between the heat source and the working fluid during the heat exchange process. To overcome this limitation, a high–low dual-tower configuration based on segmented thermal utilization is proposed. In this arrangement, the high-temperature tower is mainly responsible for the evaporation, superheating, and reheating processes, whereas the low-temperature tower primarily handles feedwater preheating. Such a configuration improves the temperature matching characteristics during the heat exchange process. A comprehensive model integrating the heliostat field, receiver, thermal energy storage system, and power block was developed and validated against Solar Two experimental data, showing good agreement. Comparative analyses were conducted under identical solar resource and operating conditions. The results indicate that the proposed system achieves a comparable power output while reducing total heat transfer exergy destruction by approximately 24%, with a significant reduction of over 80% in the preheating section. Sensitivity analysis further reveals that optimizing the high tower outlet temperature can effectively reduce irreversibility and slightly enhance power output, although constrained by the pinch temperature difference. Dynamic simulations based on typical meteorological year data demonstrate that the system maintains stable operation and improves cycle efficiency. From an economic perspective, the proposed system reduces the levelized cost of electricity (LCOE) by about 6.6% and shortens the dynamic payback period, indicating enhanced long-term competitiveness. Overall, the high and low dual-tower system effectively improves thermodynamic and economic performance, providing a promising approach for high-efficiency concentrating solar power (CSP) development.