Usually, high temperature decreases the output performance of triboelectric nanogenerator because of the dissipation of triboelectric charges through the thermionic emission. Here, a temperature difference triboelectric nanogenerator is designed and fabricated to enhance the electrical output performance in high temperature environment. As the hotter friction layer’s temperature of nanogenerator is 0 K to 145 K higher than the cooler part’s temperature, the output voltage, current, surface charge density and output power are increased 2.7, 2.2, 3.0 and 2.9 times, respectively (from 315 V, 9.1 μA, 19.6 μC m⁻², 69 μW to 858 V, 20 μA, 58.8 μC m⁻², 206.7 μW). With the further increase of temperature difference from 145 K to 219 K, the surface charge density and output performance gradually decrease. At the optimal temperature difference (145 K), the largest output current density is 443 μA cm⁻², which is 26.6% larger than the reported record value (350 μA cm⁻²).

Piezotronics with capacity of constructing adaptive and seamless interactions between electronics/machines and human/ambient are of value in Internet of Things, artificial intelligence and biomedical engineering. Here, we report a kind of highly sensitive strain sensor based on piezotronic tunneling junction (Ag/HfO₂/n-ZnO), which utilizes the strain-induced piezoelectric potential to control the tunneling barrier height and width in parallel, and hence to synergistically modulate the electrical transport process. The piezotronic tunneling strain sensor has a high on/off ratio of 478.4 and high gauge factor of 4.8 × 10⁵ at the strain of 0.10%, which is more than 17.8 times larger than that of a conventional Schottky-barrier based strain sensor in control group as well as some existing ZnO nanowire or nanobelt based sensors. This work provides in-depth understanding for the basic mechanism of piezotronic modulation on tunneling junction, and realizes the highly sensitive strain sensor of piezotronic tunneling junction on device scale, which has great potential in advanced micro/nano-electromechanical devices and systems.

Triboelectric nanogenerator is an emerging technology that can convert low-frequency and irregular mechanical energy into electricity and has broad applications in the fields of distributed energy harvesting, self-powered sensing, and the Internet of Things. However, it is still a challenge to achieve high electrical energy density owing to the limited surface tribocharge density. Herein, we report a three-dimensional fractal structured nanogenerator (FSNG), which can efficiently collect the triboelectrification induced space-distributed Maxwell's displacement current, and hence largely improve the electrical energy density. The output charge density, power density, and energy density of FSNG break the record and reach up to 8 mC m⁻², 2.18 MW m⁻², and 0.39 J m⁻² cycle⁻¹, respectively, and can be further improved by optimizing the fractal unit. In addition, the equivalent fractal circuit models for FSNG with the same functionality are developed for a deep understanding of the FSNG. This study not only creates a new record of high electrical output of nanogenerators but also paves a more efficient way for mechanical energy scavenging toward practical applications.

Self-powered sensing related to triboelectric nanogenerator (TENG) as a sustainable self-sufficient power source for environmental monitoring by harvesting energy from a living environment is critical in the field of energy and environmental science. However, features of small energy-density and irregularity of environmental energy make it difficult to use directly for real-time environmental monitoring. Here, we report a self-powered and self-calibrated environmental monitoring system (SSEMS) composed of TENG, a calibration resistor, and a sensor network in parallel for real-time temperature and humidity monitoring. The calibration resistor can monitor in real time the irregular output of TENG from the irregularity of rainfalls. SSEMS uses this calibrating signal to calibrate the sensing signal in real time, achieving accurate sensing with an error margin less than 5.0%. We applied SSEMS under waterfalls and rainfalls to monitor in real time the environmental temperature and relative humidity with a sensing error as low as 1.0%. This work promotes self-powering technology one step further to practical applications.

As a new branch of efficient and low-cost mechanical energy conversion technology, triboelectric nanogenerator (TENG) is a potential solution to provide a long-term power supply for the Internet of Things (IoT) sensors and portable electronic devices. However, due to inherent working properties of TENG itself such as extremely high internal impedance, pulse, and alternating current (AC) output, TENG can not directly supply power to loads such as batteries efficiently. Based on these, we describe TENG’s performance from a new perspective of powering ability. It consists of two aspects: the ability to transport charge effectively and the ability to output high power quality current steadily. In order to push forward the developments and applications of TENG, it is necessary to improve its power supply capacity from different perspectives. Fortunately, in recent years, a variety of output signal’s management strategies aiming at effectively managing the generated electricity and significantly improving powering ability of TENG have obtained significantly progress. Herein, this paper discusses the working mechanisms and different load characteristics of TENG at first to clarify the electric performance of TENG. Then, on basis of theoretical analysis, the output signal’s management strategies are elaborated from four aspects: improving the cycle output electricity of TENG, increasing the surface charge density of TENG, improving the power quality of TENG-based energy harvesting system, promoting the application of TENG through integrated circuit (IC) technology and TENG network, and the relevant principles and applications are discussed systematically. Finally, the advantages and disadvantages of the above output signal’s management strategies are summarized and discussed, and the future development of the output signal’s management strategies for TENG is prospected.