Harvesting wind energy from the ambient environment is a feasible method for powering wireless sensors and wireless transmission equipment. Triboelectric nanogenerators (TENGs) have proven to be a stable and promising technology for harvesting ambient wind energy. This study explores a new method for the performance enhancement and practical application of TENGs. An array of flag-type triboelectric nanogenerators (F-TENGs) for harvesting wind energy is proposed. An F-TENG consists of one piece of polytetrafluoroethylene (PTFE) membrane, which has two carbon-coated polyethylene terephthalate (PET) membranes on either side with their edges sealed. The PTFE was pre-ground to increase the initial charge on the surface and to enhance the effective contact area by improving the surface roughness, thus achieving a significant improvement in the output performance. The vertical and horizontal arrays of F-TENGs significantly improved the power output performance. The optimal power output performance was achieved when the vertical parallel distance was approximately 4D/15 (see the main text for the meaning of D), and the horizontal parallel distance was approximately 2D. We found that the peak output voltage and current of a single flag-type TENG of constant size were increased by 255% and 344%, respectively, reaching values of 64 V and 8 μA, respectively.

Due to the recent rapid growth of ocean oil development and transportation, the offshore oil spill risk accident probability has increased unevenly. The marine oil spill poses a great threat to the development of coastal cities. Therefore, effective and reliable technologies must be used to monitor oil spills to minimize disaster losses. Based on YOLO deep learning network, an automatic oil spill detection method was proposed. The experimental data preprocessing operations include noise reduction, gray adjustment, and local contrast enhancement. Then, real and synthetically generated marine radar oil spill images were used to make slice samples for training the model in the YOLOv5 network. The detection model can identify the effective oil spill monitoring region. Finally, an adaptive threshold was applied to extract the oil slicks in the effective oil spill monitoring regions. The YOLOv5 detection model generated had the advantage of high efficiency compared with existing methods. The offshore oil spill detection method proposed can support real-time and effective data for routine patrol inspection and accident emergency response.

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.

Vortex-induced vibration (VIV) is a process that wind energy converts to the mechanical energy of the bluff body. Enhancing VIV to harvest wind energy is a promising method to power wireless sensor nodes in the Internet of Things. In this work, a VIV-driven square cylinder triboelectric nanogenerator (SC-TENG) is proposed to harvest broadband wind energy. The vibration characteristic and output performance are studied experimentally to investigate the effect of the natural frequency by using five different springs in a wide range of stiffnesses ($27 \mathrm{N}/\mathrm{m}<K<90 \mathrm{N}/\mathrm{m}$). The square cylinder is limited to transverse oscillation and experiments were conducted in the Reynolds regime ($3.93\times {10}^3–3.25\times {10}^4$). The results demonstrate the strong dependency of VIV on natural frequency and lock-in observed in a broad range of spring stiffness. Moreover, the amplitude ratio and range of lock-in region increase by decreasing spring stiffness. On the other hand, the SC-TENG with higher spring stiffness can result in higher output under high wind velocities. These observations suggest employing an adjustable natural frequency system to have optimum energy harvesting in VIV-based SC-TENG in an expanded range of operations.

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.