Liquid Metal Fiber Tactile Sensors with Dual-Mechanism Enhancement of Gradient Porosity and Interfacial Polarization for Textile-Integrated Human-Machine Interaction.

Flexible fiber capacitive tactile sensors hold promise for wearable human-machine interaction, yet balancing sensitivity with robustness while preserving textile softness remains challenging. To mitigate this trade-off, a capacitive tactile fiber based on the dual-mechanism enhancement of gradient-porous compression and Maxwell-Wagner interfacial polarization was developed. A fiber with a radial gradient-porous architecture, comprising a liquid metal (LM)/thermoplastic polyurethane (TPU) conductive core and a titanium dioxide (TiO2)/TPU dielectric sheath, was fabricated via coaxial wet spinning through non-solvent induced phase separation. A sub-percolating carbon nanotube/graphene oxide (CNT/GO) network was subsequently introduced onto the fiber surface to amplify the effective permittivity via interfacial charge accumulation. A sensitivity of 18.32 kPa-1, a response time of 170 ms, a hysteresis error of 4.47%, and stable signal retention over 1000 cycles were achieved. An all-textile wireless tactile platform was constructed and progressively validated from transient mouse clicking to quasi-static sitting posture monitoring (99.6% recognition accuracy) and further to a 64-key textile keyboard, where signal crosstalk was effectively decoupled through a fabric topology design and a one-dimensional convolutional neural network algorithm, yielding a character recognition accuracy of 96.36% and enabling context-aware generative artificial intelligence communication via integration with a large language model. This work demonstrates the significant potential of fiber-based tactile sensors for complex, multi-scenario human-machine interactions and provides new insights into the development of next-generation intelligent textile interaction platforms.