Triboelectric energy harvesting using inductor towards self-powered real-time wireless communication system

A wireless communication has attracted significant research interest in response to recent rapid advancement of the Internet of Things. Typical wireless communication systems utilize a battery to power the transmission of communication via transmitter, which can lead to problems of high design complexity and limited service life. There have been reports of self-powered wireless systems using a triboelectric nanogenerator (TENG) to resolve the challenges of battery issues. However, these systems require a significant time interval to operate the transmitter and these systems are not suitable to handle the signal with low frequency. Hence, the self-powered, real-time wireless communication system, which can handle signals with low frequency, is required. Herein, we demonstrate a self-powered, real-time wireless communication system (SRWS) using a TENG, transmitting inductor, and receiving inductor. At the receiving inductor, a change in the intensity of the magnetic field is observed from 3.79 mA/m to 4.91 mA/m, while the TENG is generating electricity. A variation in the magnetic field induces electricity at the receiving inductor, and the SRWS utilizes the generated electricity to conduct self-powered, real-time wireless communication. Simultaneously, wasted electrical energy in forms of electromagnetic field is harvested in the receiving inductor and utilized as the electrical signal. When the 0.47 μF capacitor is charged using both triboelectricity and induced electricity, the stored energy in the capacitor is 325% higher than the sum of the energies obtained from charging with triboelectricity and induced electricity separately. Also, despite its simple structure, the proposed system achieves wireless communication up to a distance of 60 cm. As a potential application, a rotation speed detection system is implemented using the proposed SRWS and evaluated. The proposed rotation speed detection system successfully detects rotation speeds of 120, 180, and 240 rpm, as well as changes in the rotation speed in each region. The security system is implemented with the aid of machine learning and this system can recognize the security key based on the electrical output. Considering these results, the proposed system shows great potential to overcome issues related to power and charging time and is expected to be utilized in next-generation self-powered, real-time wireless communication system.