RESUMO
In light of the crucial role of marine ecosystems and the escalating environmental conservation challenges, it is essential to conduct marine monitoring to help implement targeted environmental protection measures efficiently. Energy harvesting technologies, particularly triboelectric nanogenerators (TENGs), have great potential for prolonging the lifespan and enhancing the reliability of sensors in remote areas. However, the high internal resistance, low current, and friction-induced abrasion issues of TENGs limit their performance in practical applications. This work presents a rolling mode triboelectric nanogenerator that utilizes multi-tunnel grating electrodes and the opposite-charge-enhancement mechanism to harvest wave energy efficiently. The device achieves significant instantaneous and root mean square power density of 185.4 W/(m3·Hz) and 10.92 W/(m3·Hz), respectively. By utilizing stacked devices and an exclusively designed power management module, a self-powered ocean sensing system including computing and long-range wireless communication (0.8 km) capabilities was developed. Laboratory and in-situ ocean tests were conducted to assess and validate the system. This work offers a potential solution for the challenging deployment of marine self-powered sensing nodes.
RESUMO
Self-powered vibration sensor is highly desired for distributed and continuous monitoring requirements of Industry 4.0. Herein, a flexible fiber-shaped triboelectric nanogenerator (F-TENG) with a coaxial core-shell structure is proposed for the vibration monitoring. The F-TENG exhibits higher adaptability to the complex surfaces, which has an outstanding application prospect due to vital compensation for the existing rigid sensors. Initially, the contact characteristics between the dielectric layers, that related to the perceiving performance of the TENG, are theoretically analyzed. Such a TENG with 1D structure endows high sensitivity, allowing for accurately responding to a wide range of vibration frequencies (0.1 to 100 Hz). Even applying to the real diesel engine, the error in detecting the vibration frequencies is only 0.32% compared with the commercial vibration sensor, highlighting its potential in practical application. Further, assisted by deep learning, the recognition accuracy in monitoring nine operating conditions of the system achieves 97.87%. Overall, the newly designed F-TENG with the merits of high-adaptability, cost-efficiency, and self-powered, has offered a promising solution to fulfill an extensive range of vibration sensing applications in the future.