Textured Ionic Skin via Nonequilibrium Growth for Highly Sensitive Tactile Sensing and Contour Recognition

Human fingertips can accurately discern an object's shape and mass, thanks to their unique fingerprint ridge textures and graded modulus structure. However, replicating fingerprint-like tactile capabilities in artificial skin has been challenging due to the difficulty in balancing fine texture and sensitivity. Herein, we report a bioinspired electronic skin developed through a nonequilibrium ionic Liesegang ring growth process. The resulting hydrogel-based ionic skin features periodic fingerprint-like microstructures embedded within a soft matrix, yielding a low elastic modulus, high sensitivity (4.90 kPa^-1 in the 0-4.25 kPa pressure range), and fine texture resolution. This tactile sensor can distinguish between heavy and light objects, capture force distribution, and even detect very gentle touches, such as a feather or a hair strand. Integrated with a machine learning algorithm, it achieves the accurate recognition of complex surface contours (95.60% classification of six distinct shapes). This approach effectively overcomes the softness-texture trade-off in ionic skins and may inspire the design of next-generation, high-performance tactile sensors for soft robotic and intelligent prosthetic applications.