Seconds‐Integrated Monolithic System of Zn‐Ion Micro‐Battery and Multi‐Functional Sensors for Robotic Autonomous Tactile Sensing

X Xiangyang Li X Xiaoyu Zheng C Chunlong Dai Q Qun Niu (School of Mechanical Engineering Beijing Institute of Technology Beijing P. R. China) X Xuting Jin (Interdisciplinary Research Center for Sustainable Energy Science and Engineering (IRC4SE2), School of Chemical Engineering) Y Ying Wang Z Zhiao Zheng (Interdisciplinary Research Center for Sustainable Energy Science and Engineering School of Chemical Engineering Zhengzhou University Zhengzhou P. R. China) J Jieliang Zhao (School of Mechanical Engineering, Beijing Institute of Technology) X Xin Li Y Yang Zhao

Abstract

ABSTRACT Highly integrated devices that combine energy storage with multi‐functional sensing capabilities are pivotal for advancing the practical implementation of intelligent microsystems. However, their development is often hindered by inefficient manufacturing, reliability degradation stemming from interfacial mismatches, and challenges in achieving high‐performance, interference‐free operation. Herein, we demonstrate an ultrafast and efficient strategy to construct a monolithic multifunctional sensing and energy storage system via the Joule heating effect. This strategy enables seamless integration of all components within 8 s, intrinsically mitigating interfacial incompatibility and signal interference. Through the instantaneous thermal activation, the energy storage unit of anode‐free Zn‐ion micro‐battery delivers a capacity of 850 µAh cm −2 and energy density of 1060 µWh cm −2 , superior to most reported aqueous Zn‐based micro‐batteries. With a rapid charging time of 150 s, it powers the integrated device for over 6 h, achieving an impressive 24–h standby under low‐current conditions after full charge. Furthermore, this flexible monolithic system can be directly integrated into unmanned systems such as robotic arms, enabling autonomous environmental perception and adaptive decision‐making via machine learning, with object identification and classification accuracy exceeding 99%. This work paves the way for next‐generation autonomous microrobots, smart healthcare, and human‐machine interfaces.

Article Details

Volume / Issue Vol. 38, Issue 18
Published March 01, 2026
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (10)

X

Xiangyang Li

X

Xiaoyu Zheng

C

Chunlong Dai

Q

Qun Niu

School of Mechanical Engineering Beijing Institute of Technology Beijing P. R. China

X

Xuting Jin

Interdisciplinary Research Center for Sustainable Energy Science and Engineering (IRC4SE2), School of Chemical Engineering

Y

Ying Wang

Z

Zhiao Zheng

Interdisciplinary Research Center for Sustainable Energy Science and Engineering School of Chemical Engineering Zhengzhou University Zhengzhou P. R. China

J

Jieliang Zhao

School of Mechanical Engineering, Beijing Institute of Technology

X

Xin Li

Y

Yang Zhao