Fingerpad‐Inspired Multimodal Electronic Skin for Material Discrimination and Texture Recognition

TLDR

Human skin enables environmental interaction through touch, and although electronic skins have surpassed human skin in some functions, stretchable E‑skins capable of simultaneously distinguishing materials and textures remain undeveloped. This study presents the first stretchable multimodal E‑skin that leverages piezoelectric, triboelectric, and piezoresistive responses to exceed human tactile perception. The device consists of a wrinkle‑patterned silicon elastomer, silver‑nanowire and zinc‑oxide‑nanowire hybrids, and a thin elastomeric dielectric layer with fingerprint‑like roughness. It can detect mechanical stress from single or combined stimuli, enabling material discrimination and texture recognition for a biomimetic prosthesis applied to a robotic hand.

Abstract

Human skin plays a critical role in a person communicating with his or her environment through diverse activities such as touching or deforming an object. Various electronic skin (E-skin) devices have been developed that show functional or geometrical superiority to human skin. However, research into stretchable E-skin that can simultaneously distinguish materials and textures has not been established yet. Here, the first approach to achieving a stretchable multimodal device is reported, that operates on the basis of various electrical properties of piezoelectricity, triboelectricity, and piezoresistivity and that exceeds the capabilities of human tactile perception. The prepared E-skin is composed of a wrinkle-patterned silicon elastomer, hybrid nanomaterials of silver nanowires and zinc oxide nanowires, and a thin elastomeric dielectric layer covering the hybrid nanomaterials, where the dielectric layer exhibits high surface roughness mimicking human fingerprints. This versatile device can identify and distinguish not only mechanical stress from a single stimulus such as pressure, tensile strain, or vibration but also that from a combination of multiple stimuli. With simultaneous sensing and analysis of the integrated stimuli, the approach enables material discrimination and texture recognition for a biomimetic prosthesis when the multifunctional E-skin is applied to a robotic hand.