Interfacial Electrochemical Polymerization for Spinning Liquid Metals into Core-Shell Wires

Metal wires are of great significance in applications such as three-dimensional (3D) printing, soft electronics, optics, and metamaterials. Ga-based liquid metals (e.g., EGaIn), though uniquely combining metallic conductivity, fluidity, and biocompatibility, remain challenging to be spun due to their low viscosity, high surface tension, and Rayleigh-Plateau instability. In this work, we showed that EGaIn as a working electrode could induce the oxidization of EGaIn and interfacial electrochemical polymerization of electroactive monomers (e.g., acrylic acid, dopamine, and pyrrole), thus spinning itself from an opening of a blunt needle. During the spinning process, the high surface tension of EGaIn was reduced by electrowetting and electrocapillarity and stabilized by polymer shells (tunable thickness of -0.6-30 mu m on wires with a diameter of 90-300 mu m), which were chelated with metal ions. The polymeric shells offered EGaIn wires with an enhanced endurance to mechanical force and acidity. By further encapsulating into elastomers through a facile impregnation process, the resultant elastic EGaIn wires showed a combination of high stretchability (up to 800%) and metallic conductivity (1.5 X 10(6) S m(-1)). When serving as wearable sensors, they were capable of sensing facial expressions, body movements, voice recognition, and spatial pressure distributions with high sensitivity, good repeatability, and saticfactory durability. Machine-learning algorithms further assisted to detect gestures with high accuracy.

Automated summary

EGaIn, a Ga-based liquid metal, can be spun into wires through a unique electrochemical reaction and surface treatment. The wires are enhanced with polymer shells, providing improved endurance and acid resistance. When encapsulated in elastic materials, the wires exhibit high stretchability and metallic conductivity. As wearable sensors, they can detect facial expressions, body movements, voice, and spatial pressure with high sensitivity, and machine learning algorithms can accurately recognize gestures.