Search

The emerging tribovoltaic nanogenerators (TVNGs) has garnered considerable attention in recent years owing to its higher power density, but it is still limited by unstable output, mismatched impedance, and lower charging speed. Herein, a power‐managed GaN‐based TVNG is first proposed and a self‐powered wireless temperature monitoring system (SWTMS) is developed. The energy backflow from transition capacitor to TVNG is effectively suppressed by applying a high‐voltage‐resistant diode. Benefiting from the combination of the maximum power point tracking (MPPT and the buck converter, an autonomous power management unit (PMU) is designed. The internal impedance of TVNG with PMU decreases by ten times and the charging speed of capacitor increases by 7.18 times compared to those without PMU. Moreover, 100 LEDs and six parallel thermohygrometers can be constantly powered. The wireless temperature monitoring in industrial environments without external power supply is demonstrated through the developed SWTMS. This work proposes an effective power management strategy for TVNG with high output voltage, which exhibits great potential to promote the development of self‐powered sensors and Industrial Internet of Things (IIoT). [ABSTRACT FROM AUTHOR]

In the era of the Internet of Things, various electronics play an important role in information interaction, in which the power supply is an urgent problem to be solved. Triboelectric nanogenerator (TENG) is an emerging mechanical energy harvesting technology that can serve as a power source for electronics, which is developing towards high performance, miniaturization and integration. Herein, the advanced micro-nano manufacturing technologies are systematically reviewed for TENGs. First, film preparation such as physical vapor deposition, chemical vapor deposition, electrochemical deposition, electrospinning and screen printing for triboelectric layers are introduced and discussed. Then, surface processing, such as soft lithography, laser ablation, inductively coupled plasma and nanoimprint for micro-nano structures on the surface of triboelectric layers are also introduced and discussed. In addition, micro-electromechanical system fabrication for TENG devices such as acoustic and vibration sensors, is introduced, and their current challenges are analyzed. Finally, the challenges of the advanced micro-nano manufacturing technologies for the TENGs are systematically summarized, and further development is prospected. [ABSTRACT FROM AUTHOR]

With the extensive application of wireless sensing nodes, the demand for sustainable energy in unattended environments is increasing. Here, we report a self-powered and autonomous vibrational wake-up system (SAVWS) based on triboelectric nanogenerators and micro-electromechanical system (MEMS) switches. The energy triboelectric nanogenerator (E-TENG) harvests vibration energy to power the wireless transmitter through a MEMS switch. The signal triboelectric nanogenerator (S-TENG) controls the state of the MEMS switch as a self-powered accelerometer and shows good linearity in the acceleration range of 1–4.5 m/s2 at 30 Hz with a sensitivity of about 14.6 V/(m/s2). When the acceleration increases, the S-TENG turns on the MEMS switch, and the wireless transmitter transmits an alarm signal with the energy from E-TENG, using only 0.64 mJ. Using TENGs simultaneously as an energy source and a sensor, the SAVWS provides a self-powered vibration monitoring solution for unattended environments and shows extensive applications and great promise in smart factories, autonomous driving, and the Internet of Things. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerators (TENGs) can directly harvest energy via solid–liquid interface contact electrification, making them very suitable for harvesting raindrop energy and as active rainfall sensors. This technology is promising for realizing a fully self-powered system for autonomous rainfall monitoring combined with energy harvesting/sensing. Here, we report a raindrop energy-powered autonomous rainfall monitoring and wireless transmission system (R-RMS), in which a raindrop-TENG (R-TENG) array simultaneously serves as a raindrop energy harvester and rainfall sensor. At a rainfall intensity of 71 mm/min, the R-TENG array can generate an average short-circuit current, open-circuit voltage, and maximum output power of 15 μA, 1800 V, and 325 μW, respectively. The collected energy can be adjusted to act as a stable 2.5 V direct-current source for the whole system by a power management circuit. Meanwhile, the R-TENG array acts as a rainfall sensor, in which the output signal can be monitored and the measured data are wirelessly transmitted. Under a rainfall intensity of 71 mm/min, the R-RMS can be continuously powered and autonomously transmit rainfall data once every 4 min. This work has paved the way for raindrop energy-powered wireless hyetometers, which have exhibited broad prospects in unattended weather monitoring, field surveys, and the Internet of Things. [ABSTRACT FROM AUTHOR]

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]