A flexible, stretchable and wearable strain sensor based on physical eutectogels for deep learning-assisted motion identification.

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Abstract

Physical eutectogels as a newly emerging type of conductive gel have gained extensive interest for the next generation multifunctional electronic devices. Nevertheless, some obstacles, including weak mechanical performance, low self-adhesive strength, lack of self-healing capacity, and low conductivity, hinder their practical use in wearable strain sensors. Herein, lignin as a green filler and a multifunctional hydrogen bond donor was directly dissolved in a deep eutectic solvent (DES) composed of acrylic acid (AA) and choline chloride, and lignin-reinforced physical eutectogels (DESL) were obtained by the polymerization of AA. Due to the unique features of lignin and DES, the prepared DESL eutectogels exhibit good transparency, UV shielding capacity, excellent mechanical performance, outstanding self-adhesiveness, superior self-healing properties, and high conductivity. Based on the aforementioned integrated functions, a wearable strain sensor displaying a wide working range (0-1500%), high sensitivity (GF = 18.15), rapid responsiveness, and excellent stability and durability (1000 cycles) and capable of detecting diverse human motions was fabricated. Additionally, by combining DESL sensors with a deep learning technique, a gesture recognition system with accuracy as high as 98.8% was achieved. Overall, this work provides an innovative idea for constructing multifunction-integrated physical eutectogels for application in wearable electronics.