Liquid Metal‐Derived Flexible Hinge Junctions Enable AI‐Enhanced Triboelectric Fingerprinting via Harmonic Decoding

Abstract Flexible electronics face critical challenges in achieving robust interfacial conductivity and dynamic mechanical compliance, while conventional triboelectric sensors suffer from rigid electrodes, contact‐dependent operation, and limited multimodal recognition. Here, by considering the specific surface energy of different materials, liquid metal (LM) and carbon nanotubes (CNTs) are combined together as a flexible hinge‐like junction, which can connect the rigid conductive network fabricated with Ag nanosheets. The introduction of LM reduces the contact resistance by 53.2% and suppresses conductivity degradation by 34.4% after 100 bending cycles at 60°, compared to the Ag/CNT ink without LM. Because of the advantage in flexibility, a triboelectric linkage pendulum (TLP) employing non‐contact electrostatic induction tomography is developed. The LM‐derived hinge junctions enhance charge transfer efficiency, converting material properties, surface topography, and spatial features into high‐fidelity order harmonic signatures. A cascaded machine learning architecture decodes these harmonic fingerprints, achieving 94.5%–99.5% recognition accuracy for material composition, 3D contours, and submillimeter positional shifts. This work establishes an approach of mechano‐responsive interfacial hinging in nanocomposites, bridging the gap between flexible electronics and AI‐enhanced industrial sensing. The self‐adaptive LM junctions offer universal strategies for next‐generation wearable devices and precision automation systems.