Fully‐Printed Optical‐Electric Dual Mode Flexible Sensor

H Hui Zhou (Department of Chemistry and Materials) Y Yue Qiu (Grimwade Centre for Cultural Materials Conservation, School of Historical and Philosophical Studies, Faculty of Arts University of Melbourne Parkville) P Pei He (Frontier Institute of Science and Technology and Interdisciplinary Research Centre of Frontier Science and Technology) Z Zhidong Ma (School of Materials Science and Engineering Shanghai University of Engineering Science Shanghai P. R. China) J Jianwei He Y Yongchao Li G Guozhang Dai (School of Physics, Central South University 1 , Changsha 410083,) S Sichao Tong (Hunan Nanoup Electronics Technology Co., Ltd Changsha P. R. China) Z Zhaofeng Wang (Key Laboratory of Organic Integrated Circuit, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University) J Junliang Yang (Hunan Key Laboratory for Super-microstructure and Ultrafast Process, School of Physics)

Abstract

ABSTRACT As a pivotal component of smart home control systems (SHCS), the human‐computer interaction (HCI) interface facilitates human‐home information exchange and control. But contemporary HCI interfaces still harbor substantial security vulnerabilities due to the static and singular nature of the collected information. In this study, we formulate high‐performance ZnS:Mn 2+ mechanoluminescence (ML) ink featuring high‐resolution printing capability (around 100 µm) and achieve a stress detection threshold as low as 0.01 MPa. Subsequently, we present an optical‐electric dual‐mode flexible sensor (OEDM‐FS) crafted combining 2D and 3D printing technologies. This sensor integrates a capacitive pressure sensor (CPS) with a ML sensor. It precisely responds to the magnitude and distribution of applied stress through capacitive output and luminescent imaging. The system boasts exceptional stability in stress sensing (signal fluctuation below ±0.3% under fatigue testing involving over 10 000 cycles), and it can achieve a very high‐resolution stress spatial distribution up to about 200 micrometers. By amalgamating the ML signals with machine learning algorithms, we can accurately identify stress distribution with distinct characteristics, attaining an impressive accuracy rate of 98.5%. Furthermore, we demonstrate the functionality of this OEDM‐FS as an HCI interface for human recognition and its potential applications within SHCS.

Article Details

Volume / Issue Vol. 38, Issue 11
Published February 01, 2026
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (10)

H

Hui Zhou

Department of Chemistry and Materials

Y

Yue Qiu

Grimwade Centre for Cultural Materials Conservation, School of Historical and Philosophical Studies, Faculty of Arts University of Melbourne Parkville

P

Pei He

Frontier Institute of Science and Technology and Interdisciplinary Research Centre of Frontier Science and Technology

Z

Zhidong Ma

School of Materials Science and Engineering Shanghai University of Engineering Science Shanghai P. R. China

J

Jianwei He

Y

Yongchao Li

G

Guozhang Dai

School of Physics, Central South University 1 , Changsha 410083,

S

Sichao Tong

Hunan Nanoup Electronics Technology Co., Ltd Changsha P. R. China

Z

Zhaofeng Wang

Key Laboratory of Organic Integrated Circuit, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University

J

Junliang Yang

Hunan Key Laboratory for Super-microstructure and Ultrafast Process, School of Physics