Triboelectric Sensory Intelligence: From Mechanical Information Encoding to In-Sensor Memory, Neuromorphic Computing, and Closed-Loop Interaction

Triboelectric interfaces are progressing beyond self-powered sensing toward integrated sensory systems capable of encoding, retaining, transforming, and responding to mechanical information. Yet, the rapid convergence of triboelectric transduction with memory, neuromorphic devices, and intelligent interfaces has also blurred the distinctions between signal generation, state modulation, physical computation, and task-level intelligence. This Review establishes an information-centric framework for triboelectric sensory intelligence, in which mechanical interaction is traced through electrical encoding, signal–device coupling, internal state evolution, sensory processing, perception, and ultimately closed-loop action. Sensory representations based on amplitude, polarity, temporal structure, spatial patterns, material-dependent responses, and multimodal signals are considered in terms of encoding fidelity, separability, and robustness. Stateful mechanisms based on resistive switching, charge storage, ferroelectric polarization, and ionic or electrochemical dynamics are then related to sensory memory, temporal filtering, synaptic plasticity, event-based transformation, and other physical computational primitives. Emphasis is placed on distinguishing structural integration from processing location, device dynamics from implemented computation, and hardware processing from downstream software inference. The Review further links these principles to tactile and material perception, gesture and motion interfaces, multisensory integration, and sensory–motor feedback, while introducing system-level considerations of reproducibility, energy, latency, scalability, and generalization. Looking forward, reliable source–receiver co-design, adaptive processing, scalable heterogeneous integration, and closed-loop operation will be central to translating triboelectric interfaces from isolated device demonstrations into persistent and autonomous sensory systems.