The emerging applications of hydrogel ionotronics (HIs) in devices and machines require them to maintain their robustness under complex mechanical environments. Nevertheless, existing HIs still suffer from various mechanical limitations,...

Innovation in the ionotronics field has significantly accelerated the development of ultraflexible devices and machines. However, it is still challenging to develop efficient ionotronic‐based fibers with necessary stretchability, resilience, and conductivity due to inherent conflict in producing spinning dopes with both high polymer and ion concentrations and low viscosities. Inspired by the liquid crystalline spinning of animal silk, this study circumvents the inherent tradeoff in other spinning methods by dry spinning a nematic silk microfibril dope solution. The liquid crystalline texture allows the spinning dope to flow through the spinneret and form free‐standing fibers under minimal external forces. The resultant silk‐sourced ionotronic fibers (SSIFs) are highly stretchable, tough, resilient, and fatigue‐resistant. These mechanical advantages ensure a rapid and recoverable electromechanical response of SSIFs to kinematic deformations. Further, the incorporation of SSIFs into core–shell triboelectric nanogenerator fibers provides outstanding stable and sensitive triboelectric response to precisely and sensitively perceive small pressures. Moreover, by implementing a combination of machine learning and Internet of Things techniques, the SSIFs can sort objects made of different materials. With these structural, processing, performance, and functional merits, the SSIFs prepared herein are expected to be applied in human–machine interfaces.

Ionotronic artificial motion and tactile receptor (i-AMTR) is essential to realize an interactive human-machine interface. However, an i-AMTR that effectively mimics the composition, structure, mechanics, and multi-functionality of human skin, called humanoid i-AMTR, is yet to be developed. To bridge this technological gap, this study proposes a strategy that combines molecular structure design and function integration to construct a humanoid i-AMTR. Herein, a silk fibroin ionoelastomer (SFIE) with double cross-linked molecular structure is designed to mimic the composition and structure of human skin, thereby resolving the conflict of stretchability, softness, and resilience, suffered by many previously reported ionotronics. Functionally, electromechanical sensing and triboelectricity-based tactile perception are integrated into SFIE, to enable simultaneous perception of both motion and tactile inputs. By further leveraging the machine learning and Internet of Things (IoT) techniques, the proposed SFIE-based humanoid i-AMTR precisely senses the movement of human body and accurately sortball objects made of different materials. Notably, the success rate for 610 sorting tests reaches as high as 92.3%. These promising results essentially demonstrate a massive potential of humanoid i-AMTR in the fields of sorting robots, rehabilitation medicine, and augmented reality.

Water seepage-induced geological hazards (SIGHs), including landslides, collapse, debris flow, and ground fissures, often cause substantial human mortality, economic losses, and environmental damage. However, an early warning of geological water seepage remains a significant challenge. A self-powered, cost-effective, reliable, and susceptible SIGH early warning system (SIGH-EWS) is reported herein. This system designed the all-solid, sustainable, fire retardant, and safe-to-use bio-ionotronic batteries to provide a stable power supply for Internet of Things chipsets. Furthermore, the batteries’ outstanding humidity and water sensitivity allow sensing of the water seepage emergence. Integrating energy management and wireless communication systems, the SIGH-EWS realizes timely alerts for early water seepage in different water and soil environments with a time resolution in seconds. Based on these merits, the SIGH-EWS demonstrates promising application prospects for early warning of geological disasters and corresponding design strategies that can potentially guide the designs of next-generation geological hazard alarm systems.