A multi-modal flexible sensor based on a liquid-metal-enhanced fluorinated ionogel for intelligent motion monitoring...

Ionogel-based flexible sensors show great potential for applications in wearable electronics, intelligent human-machine interfaces, and soft bioelectronics. However, it remains challenging to simultaneously achieve high mechanical compliance, multimodal sensing capability, and reliable signal discrimination under complex dynamic conditions. Herein, we report a liquid-metal-enhanced fluorinated ionogel (GATIL) multimodal flexible sensor fabricated via a facile one-pot ultraviolet (UV)-initiated polymerization strategy. By dispersing eutectic gallium-indium (EGaIn) droplets into the ionic liquid phase of 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide ([BMIM][TFSI]) and subsequently copolymerizing acrylamide (AAm) with hexafluorobutyl acrylate (HFA), a three-dimensional conductive network composed of a polymer framework, ionic-liquid conduction pathways, and LM droplets is successfully constructed. Owing to the synergistic interactions among these components, the as-prepared GATIL exhibits high stretchability (over 1000%), good toughness, excellent linear response, and high strain sensitivity (GF = 9.7), together with fast resistive response/recovery times (0.36/0.31 s). In addition, when used as a dielectric layer, the GATIL-based capacitive sensor delivers an average sensitivity of 0.31 kPa^-1 over a pressure range of approximately 0-12 kPa, together with fast response/recovery times of 0.16/0.12 s. The GATIL sensor can be conformally attached to the human body as a wearable device for monitoring multiscale physiological and motion signals, ranging from finger bending and spinal movement to smiling, swallowing, and phonation. Moreover, we propose a spatially separated "antiphase synergistic" sensing strategy, in which the resistive and capacitive units are positioned on the extensional and flexional sides of joints, respectively, to exploit the complementary response relationship of increasing resistance and decreasing capacitance for enhanced motion discrimination. Assisted by a deep residual network (ResNet50), an intelligent recognition framework is further established for capacitive air-writing recognition of Chinese numerals and bimodal multi-node joint-motion classification. This study provides a promising strategy for the development of high-performance LM-based ionogel sensors for wearable monitoring and intelligent human-machine interaction.