Robust, Washable and Stretchable Liquid-Metal-Based Electronic Textiles Enabled by Double Encapsulation

Electronic textiles (e-textiles) have enabled the integration of smart devices into daily outfits for various applications, such as healthcare, human-machine interaction and fashion. Ideally, these electronics should preserve the pristine comfort, elasticity and washability of garments while achieving high functionality. In recent years, a promising strategy towards this goal is to interconnect soft and printed conductors, such as nanocomposites and room-temperature liquid metal, with rigid and miniaturized electronic components, followed by encapsulation. However, the washability of these electronics is limited by mechanical failures at the soft-rigid interfaces. Herein, we develop a double encapsulation strategy for preparing liquid-metal-based stretchable textile electronic patch (STEP) with enhanced durability and washability. This strategy involves the combination of soft encapsulation (Ecoflex) and stiff encapsulation (thermoplastic urethanes (TPU)) in the interconnects of conductors and electronic components. Our strategy enables stable soft-rigid interfaces between conductors and electronic components even against cyclic tensile test (100% strain, 10,000 cycles) and cyclic standard machine washing (100 cycles). Compared to single encapsulation with Ecoflex, the double encapsulation significantly improves the stretchability (5 folds) after cyclic stretch-wash durability test. To demonstrate the applicability of our strategy and applications of STEP, we fabricate a conformal, multifunctional, and washable electronic textile system for healthcare. This system has three distributed functional devices on a single sport shirt: one 5*9 matrix LED display on the arm, one temperature sensor on the armpit and one accelerometer at on the chest. The system can simultaneously monitor body temperature, movements, and display the information for real-time data visualization. The system could also maintain functionality after machine washing. With data fusion and artificial intelligence, the system is expected to generate energy expenditure estimation for obesity patients in the future.