Enhanced sensitivity and detection range of a flexible pressure sensor utilizing a nano-cracked PVP hierarchical nanofiber membrane formed by BiI3 sublimation

被引：25

作者：

Guo, Dedong ^{[1
]}

Dong, Shuheng ^{[1
]}

Wang, Qingzhou ^{[1
]}

Yu, Huixin ^{[1
]}

Kim, Eun-Seong ^{[2
]}

Xu, Qing ^{[1
]}

Sung, Ho-Kun ^{[3
]}

Yao, Zhao ^{[1
]}

Li, Yuanyue ^{[1
]}

Kim, Nam-Young ^{[2
]}

机构：

[1] Qingdao Univ, Coll Elect & Informat, Qingdao 266071, Peoples R China

[2] Kwangwoon Univ, Dept Elect Engn, Seoul 01897, South Korea

[3] Korea Adv Nano Fab Ctr KANC, Dept thin film, Suwon 443270, South Korea

来源：

CHEMICAL ENGINEERING JOURNAL | 2023年 / 476卷

基金：

中国国家自然科学基金; 新加坡国家研究基金会;

关键词：

Flexible pressure sensor; Hierarchical nanofiber structure; High sensitivity; Wide detection range; Motion posture recognition;

D O I：

10.1016/j.cej.2023.146464

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The development of a highly sensitive and wide-range flexible pressure sensor is great significant to realize the practical applications in human-computer interaction, health monitoring, and motion detection. Here, a dielectric layer of nano-cracked polyvinyl pyrrolidone (PVP) hierarchical nanofiber membrane (HNM) was fabricated by sublimating BiI3 from an electrospun BiI3/PVP composite nanofiber membrane (NM) via one-step annealing process. The upper and lower electrodes of porous laser-induced graphene (LIG) were produced through the engraving process on polyimide (PI) substrates. Subsequently, a flexible capacitive pressure sensor was constructed by assembling the middle PVP HNM dielectric layer with upper and lower LIG electrodes, which demonstrates enhanced sensitivity and detection range for motion posture recognition. Experimental results indicate that compared with traditional PVP NM-based pressure sensor, the assembled sensor in this work exhibits 22 times higher sensitivity (at 2-100 kPa) and 4 times wider detection range (0-200 kPa). Additionally, the sensor boasts a fast response/recovery time of 29/41 ms, an exceptionally low detection limit of 2.7 Pa, and an outstanding stability of 3000 cycles. With the aid of a convolutional neural network (CNN) algorithm, a shooting posture recognition system was developed by multiple sensors to accurately identify (accuracy: 93.89 %) and guide the shooting postures of basketball players.

引用

页数：12