Fish‐Scale‐Inspired Giant Piezocapacitive Sensors for Human‐Level Touch Perception

Y Yulian Peng Z Zelong Li (Key Laboratory of Advanced Catalysis, Gansu Province; State Key Laboratory of Natural Product Chemistry, College of Chemistry and Chemical Engineering) J Jiayuan Zhang (State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics) Y Yueyang Wang S Siduo Wang (Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei China) H Houping Wu H Hongbo Wang (State Key Laboratory of High Pressure and Superhard Materials, College of Physics)

Abstract

ABSTRACT Achieving human‐level touch perception in robotics requires flexible sensors that combine a low detection limit, rapid response, robust reliability, and ease of fabrication. Yet, integrating these diverse characteristics into a single device remains a formidable challenge. This work presents a giant piezocapacitive sensor (GPCS) that matches human touch perception capabilities, based on a fish‐scale‐inspired electric‐field gating film. This mechanically compliant and robust biomimetic film consists of high‐permittivity rigid scales separated by air gaps within an elastomer matrix, resulting in a high bulk permittivity. These gaps act as electric‐field gates that modulate the fringing electric field between electrode pairs, translating subtle mechanical deformations into substantial capacitance changes. Consequently, the GPCS achieves an exceptional bidirectional bending resolution of 0.005° over a range of ± 90° with a response time of 0.6 ms, showing no performance degradation in a 100 000‐cycle bending test. This performance enables the precise discrimination of 16 fabric textures and the detection of surface topographies as fine as 1.8 µm—sufficient to resolve printed toner lines on paper. Finally, a GPCS array is integrated onto a robotic gripper, demonstrating in situ ripeness evaluation of kiwis during grasping, automated fruit sorting, and intuitive human–robot interactions.

Article Details

Volume / Issue Vol. 38, Issue 37
Published July 01, 2026
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (7)

Y

Yulian Peng

Z

Zelong Li

Key Laboratory of Advanced Catalysis, Gansu Province; State Key Laboratory of Natural Product Chemistry, College of Chemistry and Chemical Engineering

J

Jiayuan Zhang

State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics

Y

Yueyang Wang

S

Siduo Wang

Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei China

H

Houping Wu

H

Hongbo Wang

State Key Laboratory of High Pressure and Superhard Materials, College of Physics