Today, energy is essential for every aspect of human life, including clothing, food, housing and transportation. However, traditional energy resources are insufficient to meet our modern needs. Self-powered sensing devices emerge as promising alternatives, offering sustained operation without relying on external power sources. Leveraging advancements in materials and manufacturing research, these devices can autonomously harvest energy from various sources. In this review, we focus on the current landscape of self-powered wearable sensors, providing a concise overview of energy harvesting technologies, conversion mechanisms, structural or material innovations, and energy storage platforms. Then, we present experimental advances in different energy sources, showing their underlying mechanisms, and the potential for energy acquisition. Furthermore, we discuss the applications of self-powered flexible sensors in diverse fields such as medicine, sports, and food. Despite significant progress in this field, widespread commercialization will necessitate enhanced sensor detection abilities, improved design factors for adaptable devices, and a balance between sensitivity and standardization.

The use of water resources for energy generation has become increasingly prevalent, encompassing the conversion of kinetic energy from streams, tides, and waves into renewable electrical power. Water energy sources offer numerous benefits, including widespread availability, stability, and the absence of carbon dioxide and other greenhouse gas emissions, making them a clean and environmentally friendly form of energy. In this work, we develop a droplet-based liquid–solid triboelectric nanogenerator (LS-TENG) using sophisticatedly designed inflatable columnar structures with inner and outer dual-electrodes. This device can be utilized to harvest both the internal droplet-rolling mechanical energy and the external droplet-falling mechanical energy, capable of being assembled into various structures for versatile applications. The design incorporates a combined structure of both internal and external TENG to optimize output performance via multiple energy harvesting strategies. The internal structure features a dual-electrode columnar-shaped LS-TENG, designed to harvest fluid kinetic energy from water droplets. By leveraging the back-and-forth motion of a small amount of water within the air column, mechanical energy can be readily collected, achieving a maximum mass power density of 9.02 W·Kg⁻¹ and an energy conversion efficiency of 10.358%. The external component is a droplet-based LS-TENG, which utilizes a double-layer capacitor switch effect elucidated with an equivalent circuit model. Remarkably, without the need for pre-charging, a single droplet can generate over 140 V of high voltage, achieving a maximum power density of 7.35 W·m⁻² and an energy conversion efficiency of 22.058%. The combined LS-TENG with a sophisticated inflatable columnar structure can simultaneously collect multiple types of energy with high efficacy, exhibiting great significance in potential applications such as TENG aeration rollers, inflatable lifejacket, wind energy harvesting, TENG tents, and green houses.

With the arrival of the era of artificial intelligence (AI) and big data, the explosive growth of data has raised higher demands on computer hardware and systems. Neuromorphic techniques inspired by biological nervous systems are expected to be one of the approaches to breaking the von Neumann bottleneck. Piezotronic neuromorphic devices modulate electrical transport characteristics by piezopotential and directly associate external mechanical motion with electrical output signals in an active manner, with the capability to sense/store/process information of external stimuli. In this review, we have presented the piezotronic neuromorphic devices (which are classified into strain-gated piezotronic transistors and piezoelectric nanogenerator-gated field effect transistors based on device structure) and discussed their operating mechanisms and related manufacture techniques. Secondly, we summarized the research progress of piezotronic neuromorphic devices in recent years and provided a detailed discussion on multifunctional applications, including bionic sensing, information storage, logic computing, and electrical/optical artificial synapses. Finally, in the context of future development, challenges, and perspectives, we have discussed how to modulate novel neuromorphic devices with piezotronic effects more effectively. It is believed that the piezotronic neuromorphic devices have great potential for the next generation of interactive sensation/memory/computation to facilitate the development of the Internet of Things, AI, biomedical engineering, etc.

Two-dimensional (2D) tribotronic devices have been successfully involved in electromechanical modulation for channel conductance and applied in intelligent sensing system, touch screen, and logic gates. Ambipolar transistors and corresponding complementary inverters based on one type of semiconductors are highly promising due to the facile fabrication process and readily tunable polarity. Here, we demonstrate an ambipolar tribotronic transistor of molybdenum ditelluride (MoTe₂), which shows typical ambipolar transport properties modulated by triboelectric potential. It is comprised of a MoTe₂ transistor and a lateral sliding triboelectric nanogenerator (TENG). The induced triboelectric potential by Maxwell’s displacement current (a driving force for TENG) can readily modulate the transport properties of both electrons and holes in MoTe₂ channel and effectively drive the transistor. High performance tribotronic properties have been achieved, including low cutoff current below 1 pA·μm⁻¹ and high current on/off ratio of ~ 10³ for holes and electrons dominated transports. The working mechanism on how to achieve tribotronic ambipolarity is discussed in detail. A complementary tribotronic inverter based on single flake of MoTe₂ is also demonstrated with low power consumption and high stability. This work presents an active approach to efficiently modulate semiconductor devices and logic circuits based on 2D materials through external mechanical signal, which has great potential in human–machine interaction, intelligent sensor, and other wearable devices.