Triboelectric nanogenerators (TENG), renowned for their remarkable capability to harness weak mechanical energy from the environment, have gained considerable attention owing to their cost-effectiveness, high output, and adaptability. This review provides a unique perspective by conducting a comprehensive and in-depth analysis of magnetically assisted TENGs that encompass structures, materials, and self-powered sensing systems. We systematically summarize the diverse functions of the magnetic assistance for TENGs, including system stiffness, components of the hybrid electromagnetic-triboelectric generator, transmission, and interaction forces. In the material domain, we review the incorporation of magnetic nano-composites materials, along with ferrofluid-based TENG and microstructure verification, which have also been summarized based on existing research. Furthermore, we delve into the research progress on physical quantity sensing and human-machine interface in magnetic-assisted TENGs. Our analysis highlights that magnetic assistance extends beyond the repulsive and suction forces under a magnetic field, thereby playing multifaceted roles in improving the output performance and environmental adaptability of the TENGs. Finally, we present the prevailing challenges and offer insights into the future trajectory of the magnetic-assisted TENGs development.

Wearable and flexible electronics are shaping our life with their unique advantages of light weight, good compliance, and desirable comfortability. With marching into the era of Internet of Things (IoT), numerous sensor nodes are distributed throughout networks to capture, process, and transmit diverse sensory information, which gives rise to the demand on self-powered sensors to reduce the power consumption. Meanwhile, the rapid development of artificial intelligence (AI) and fifth-generation (5G) technologies provides an opportunity to enable smart-decision making and instantaneous data transmission in IoT systems. Due to continuously increased sensor and dataset number, conventional computing based on von Neumann architecture cannot meet the needs of brain-like high-efficient sensing and computing applications anymore. Neuromorphic electronics, drawing inspiration from the human brain, provide an alternative approach for efficient and low-power-consumption information processing. Hence, this review presents the general technology roadmap of self-powered sensors with detail discussion on their diversified applications in healthcare, human machine interactions, smart homes, etc. Via leveraging AI and virtual reality/augmented reality (VR/AR) techniques, the development of single sensors to intelligent integrated systems is reviewed in terms of step-by-step system integration and algorithm improvement. In order to realize efficient sensing and computing, brain-inspired neuromorphic electronics are next briefly discussed. Last, it concludes and highlights both challenges and opportunities from the aspects of materials, minimization, integration, multimodal information fusion, and artificial sensory system.

For human beings of different ages and physical abilities, the inherent balance control system is ubiquitous and active to prevent falling, especially in motion states. A hybridized electromagnetic-triboelectric nanogenerator (HETNG) is prepared to harvest biomechanical energy during human balance control processes and achieve significant monitoring functions. The HETNG is composed of a symmetrical pendulum structure and a cylinder magnet rolling inside. Four coils are divided into two groups which form into two electromagnetic generators (EMGs). Besides, two spatial electrodes attached to the inner wall constitute a freestanding mode triboelectric nanogenerator (TENG). With a rectification circuit, the HETNG presents a high output power with a peak value of 0.55 W at a load of 160 Ω. Along with human balance control processes during walking, the HETNG can harvest biomechanical energy at different positions on the trunk. Moreover, the HETNG applied in artificial limb has been preliminarily simulated with the positions on thigh and foot, for monitoring the actions of squat and stand up, and lifting the leg up and down. For the elder that walks slowly with a walking aid, the HETNG equipped on the walking aid can help to record the motions of forwarding and unexpected falling, which is useful for calling for help. This work shows the potential of biomechanical energy-driven HETNG for powering portable electronics and monitoring human motions, also shows significant concerns to people lacked action capability or disabled.