Owing to the advantages of integration and being magnet-free and light-weight, the switched-capacitor-convertor plays an increasing role compared to traditional transformer in some specific power supply systems. However, the high output impedance and switching loss largely reduces its power efficiency, due to imperfect topology and transistors. Herein, we propose a fractal-design based switched-capacitor-convertors with characteristics including high conversion efficiency, minimum output impedance, and electrostatic voltage applicability. As a double-function output power management system for triboelectric nanogenerators, it delivers over 67 times charge boosting and 954 W m−2 power density in pulse mode, and achieves over 94% total energy transfer efficiency in constant mode. The establishment of the fractal-design switched-capacitor-convertors provides significant guidance for the development of power management toward multi-functional output for numerous applications. The successful demonstration in triboelectric nanogenerators also declares its great potential in electric vehicles, DC micro-grids etc.

Vibrations in the environment are usually distributed over a wide frequency spectrum in multiple directions and a weaker amplitude, which makes most of the current vibrational energy collectors limited in practical environmental applications. Herein, a triboelectric−electromagnetic hybridized nanogenerator (TEHG) for low-frequency random microvibrational energy harvesting in all directions and a wide working bandwidth is fabricated. The output peak power of a triboelectric nanogenerator (TENG) up to 3.65 mW is realized (θ = 0.4 rad, f = 1 Hz). In addition, a real self-powered seawater splitting system and electrochemical cathodic protection system are fabricated, directly converting blue energy to hydrogen energy, and the ships can achieve selfprotection against corrosion. Furthermore, relying on the linear relationship between the number of peaks and the amplitude of vibration, a highly sensitive self-powered vibration amplitude sensor system based on LabVIEW software is achieved, which can be used as an amplitude detection of bridges and earthquake monitoring, etc. This work is an important development for harvesting low-frequency random multiple direction microvibrational energy over a wide working bandwidth and the bright future of blue energy. In addition, it has been successfully applied to the power supply of portable electronic equipment, environmental monitors, and self-powered systems.

No abstract

HIGHLIGHTS • A novel windmill-like hybrid nanogenerator with contact-separation structure was proposed for harvesting breeze energy at low wind speed. • A spring steel sheet was creatively used both as an electrode of triboelectric nanogenerator and a booster for contact-separation activity. • A magnetic acting as a bifunctional element supplies magnetic flux variation in electromagnetic generator and overcomes electrostatic adsorption between tribolayers simultaneously.

In human-machine interaction, robotic hands are useful in many scenarios. To operate robotic hands via gestures instead of handles will greatly improve the convenience and intuition of human-machine interaction. Here, we present a magnetic array assisted sliding triboelectric sensor for achieving a real-time gesture interaction between a human hand and robotic hand. With a finger’s traction movement of flexion or extension, the sensor can induce positive/negative pulse signals. Through counting the pulses in unit time, the degree, speed, and direction of finger motion can be judged in real-time. The magnetic array plays an important role in generating the quantifiable pulses. The designed two parts of magnetic array can transform sliding motion into contact-separation and constrain the sliding pathway, respectively, thus improve the durability, low speed signal amplitude, and stability of the system. This direct quantization approach and optimization of wearable gesture sensor provide a new strategy for achieving a natural, intuitive, and real-time human-robotic interaction.

No abstract

No abstract

No abstract