Large Area and Flexible Flexion Sensing Matrix for Detection of Strain Distribution in Bendable and Curved Surface

Flexible flexion sensors are attracting attention due to their wide range of applications. It is urgent to develop a flexible sensor matrix to detect strain distribution on curved surfaces for object surface posture reconstruction, fault detection, and predictive maintenance. Herein, a convenient and universal method for preparing a flexible flexion sensor matrix is proposed using a versatile screen-printing technique. Compared to traditional thin film configurations, this process improved the sensitivity by introducing multiple interfaces and can be used for the fabrication of large-area flexion sensor matrix with high stability and consistency. The prepared flexible flexion sensors performed with a low detection limit (0.07%), a remarkable gauge factor (>50), and high stability (no apparent decay after 2000 bending-releasing cycles). We also demonstrated their applications in monitoring human body movement and gesture recognition. The sensors were integrated into a data glove for real-time robotic arm control, and achieved an accuracy rate of over 96% in recognizing various gestures with a neural network model. A large area flexible flexion sensor matrix (8 × 8) was fabricated by full-printing technique and enables simultaneous monitoring of multiposition bending states, which has significant potential in real-time tracking the strain distribution in bendable and curved surfaces.

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Institutions

Publication Details

Journal
ACS Sensors
Published
2025-04-11
DOI
https://doi.org/10.1021/acssensors.5c00153
Citations
11
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
7.42

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article

Large Area and Flexible Flexion Sensing Matrix for Detection of Strain Distribution in Bendable and Curved Surface

Gaojie Chen, Qixuan Zhu, Huihui Ma, Manzhang Xu et al.
11 citations
ACS Sensors
Advanced Sensor and Energy Harvesting Materials
7.42
article

Large Area and Flexible Flexion Sensing Matrix for Detection of Strain Distribution in Bendable and Curved Surface

Gaojie Chen, Qixuan Zhu, Huihui Ma, Manzhang Xu, Siying Dang, Wei Huang, Weiwei Li, Zhipeng Xu, Xuewen Wang, Yuxuan Xu, Lu Zheng, Yunqiang Cao
article en
11 citations

Abstract

Flexible flexion sensors are attracting attention due to their wide range of applications. It is urgent to develop a flexible sensor matrix to detect strain distribution on curved surfaces for object surface posture reconstruction, fault detection, and predictive maintenance. Herein, a convenient and universal method for preparing a flexible flexion sensor matrix is proposed using a versatile screen-printing technique. Compared to traditional thin film configurations, this process improved the sensitivity by introducing multiple interfaces and can be used for the fabrication of large-area flexion sensor matrix with high stability and consistency. The prepared flexible flexion sensors performed with a low detection limit (0.07%), a remarkable gauge factor (>50), and high stability (no apparent decay after 2000 bending-releasing cycles). We also demonstrated their applications in monitoring human body movement and gesture recognition. The sensors were integrated into a data glove for real-time robotic arm control, and achieved an accuracy rate of over 96% in recognizing various gestures with a neural network model. A large area flexible flexion sensor matrix (8 × 8) was fabricated by full-printing technique and enables simultaneous monitoring of multiposition bending states, which has significant potential in real-time tracking the strain distribution in bendable and curved surfaces.

ACS SensorsVol. 10(7)
Nanjing Tech University (CN), Sun Yat-sen University (CN), Northwestern Polytechnical University (CN), Nanjing University of Posts and Telecommunications (CN)
National Natural Science Foundation of China, Northwestern Polytechnical University, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 3%
Advanced Sensor and Energy Harvesting Materials
7.42
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