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Physical parameter sensing largely benefits the lifetime and operational costs of machines and has been widely used for machine fault detection. Herein, in this article, we developed a multinode sensor network, which is fully self-powered by harvesting mechanical vibration energy, to establish a machine fault detection system. A multilayered vibrational triboelectric nanogenerator (V-TENG) was designed to scavenge energy from working machines. Triggered by a vibration motion with the frequency of 8 Hz, the V-TENG can generate an output with power density of 3.33 mW/m3. With a power management module, the microcontrol unit integrated with sensors and a wireless transmitter can be continuously powered by the V-TENG to construct a self-powered vibration sensor node (SVSN). A supporting vector machine algorithm-based machine fault detection system was then established through a three-SVSN network by acquiring acceleration and temperature data from the working machine. Based on the system, different working conditions of the machine were recognized with an accuracy of 83.6%. The TENG-based SVSN for machine fault detection has demonstrated wide prospects in production monitoring, intelligent manufacturing, and smart factory. Moreover, the proposed self-powered sensor network has great potential and wide application in the era of distributed Internet of Things, artificial intelligence, and big data. [ABSTRACT FROM AUTHOR]

To meet the great need for distributed and sustainable power sources, mechanical energy in the environment is widely used for electricity generation. Based on the coupling effect of triboelectrification and electrostatic induction, triboelectric nanogenerator (TENG) as an innovative energy technology has advantages of collecting fluctuating amplitude and low-frequency mechanical energy. In this paper, a leaf-shaped TENG has been demonstrated to harvest multiple ambient mechanical energy. By the characterization with different mechanical energy sources, the leaf-shaped TENG has been proven as an effective tool to scavenge wind, vibration, and falling raindrop energy. Furthermore, based on numerous leaf-shaped TENGs, a leaf-shaped TENG tree has been developed, which can be used for continuous collection of environmental mechanical energy at large-scale. This paper has first exhibited a conformal structure for simultaneously harvesting various mechanical energy, which has bright potentials in environmental monitoring, Internet of things, and wireless sensor networks. [ABSTRACT FROM AUTHOR]

In this work, we investigate the effect of sidewall angle, height and space of an air cavity extractor (AC-Extractor) on the polarization-dependent light extraction efficiency (LEE) for AlGaN-based deep-ultraviolet light-emitting diodes (DUV LEDs) using three-dimensional finite difference time-domain method. Compared to the reported inclined sidewall metal reflector (SM-Reflector), DUV LED with AC-Extractor shows better light extraction at any height and inclined angle due to different light extraction mechanisms. The light in SM-Reflector DUV LED is extracted mainly through mirror reflection. However, total internal reflection from the sidewall dominants the contribution to LEE for the AC-Extractor with smaller inclined angle such that the space has little impact, while Fresnel scattering is more important for the AC-Extractor with larger inclined angle and that the additional out-light channel as well as the space play a critical role in enhancing the LEE. And the analysis is further developed into a three-escape-cones analytic model theory. In particular, the AC-Extractor with vertical sidewall is more beneficial to enhance the LEE of DUV LED than that with inclined sidewall, which can be rationally explained based on the three-escape-cones analytic model. [ABSTRACT FROM AUTHOR]