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The metal–insulator transition (MIT) of vanadium dioxide (VO2), which involves significant changes in electrical, optical, and other properties, has attracted widespread attention. Here, we report a triboelectric nanogenerator (TENG)-based self-powered VO2 phase transition. With the TENG-induced ionic gel gating, the hydrogen ion insertion/detachment in the VO2 lattice can be modulated at room temperature to cause phase transitions that control light transmittance. The phase transition behavior is characterized by X-ray diffraction and Raman spectroscopy. Under the continuous power supply from TENG, the X-ray diffraction peak of VO2 shifts, and the Raman intensities of characteristic peaks decrease, accompanied by a color change from brown to translucent. The electrical conductivity undergoes a significant change by two orders of magnitude and persists overnight, indicating an excellent duration of the phase transition. In addition, a raindrop TENG-induced VO2 phase transition is demonstrated. The infrared transmittance of VO2 is decreased dramatically by 28.1% due to the phase transition, achieving a heat preservation effect that holds promise for adaptive thermal insulation in smart windows during rainfall through raindrop power generation. This work has realized the TENG-based self-powered VO2 phase transition, holding significant application potential in smart homes. [ABSTRACT FROM AUTHOR]

This study presents an intelligent soft robotic system capable of perceiving, describing, and sorting objects based on their physical properties. This work introduces a bimodal self‐powered flexible sensor (BSFS) based on the triboelectric nanogenerator and giant magnetoelastic effect. The BSFS features a simplified structure comprising a magnetoelastic conductive film and a packaged liquid metal coil. The BSFS can precisely detect and distinguish touchless and tactile models, with a response time of 10 ms. By seamlessly integrating the BSFSs into the soft fingers, this study realizes an anthropomorphic soft robotic hand with remarkable multimodal perception capabilities. The touchless signals provide valuable insights into object shape and material composition, while the tactile signals offer precise information regarding surface roughness. Utilizing a convolutional neural network (CNN), this study integrates all sensing information, resulting in an intelligent soft robotic system that accurately describes objects based on their physical properties, including materials, surface roughness, and shapes, with an accuracy rate of up to 97%. This study may lay a robotic foundation for the hardware of the general artificial intelligence with capacities to interpret and interact with the physical world, which also serves as an interface between artificial intelligence and soft robots. [ABSTRACT FROM AUTHOR]