3D printing of self-healing longevous multi-sensory e-skin - Antonia Georgopoulou; Sudong Lee; Benhui Dai; Francesca Bono; Josie Hughes; Esther Amstad (Communications Materials)
Abstract Electrically conductive hydrogels can simulate the sensory capabilities of natural skin, such that they are well-suited for electronic skin. Unfortunately, currently available electronic skin cannot detect multiple stimuli in a selective manner. Inspired by the deep eutectic solvent chemistry of the frog Lithobates Sylvaticus, we introduce a double network granular organogel capable of simultaneously detecting mechanical deformation, structural damage, changes in ambient temperature, and humidity. The deep eutectic solvent chemistry adds an additional benefit: Thanks to strong hydrogen bonding, our sensor can recover 97% of the Young’s modulus after being damaged. The sensing performance and self-healing capacity are maintained within a temperature range of −20 °C to 50 °C for at least 2 weeks. We exploit the granular nature of this system to direct ink to write a cm-sized frog and e-skin wearables. We realize selective tactile perception by training recurrent neural networks to achieve sensory stimulus classification between the temperature and strain with 98% accuracy.