Ecofriendly and high-performance flexible pressure sensor derived from natural plant materials for intelligent audible and silent speech recognition

被引:7
|
作者
Zheng, Xuqi [1 ]
Yi, Bingcheng [2 ]
Zhou, Qihui [2 ]
Xu, Ruijie [1 ]
Liu, Xueqian [1 ]
Sung, Ho-Kun [3 ]
Chernogor, Leonid [1 ]
Cao, Minghui [1 ]
Yao, Zhao [1 ]
Li, Yang [4 ]
Li, Yuanyue [1 ]
机构
[1] Qingdao Univ, Coll Elect & Informat, Qingdao 266071, Peoples R China
[2] Univ Hlth & Rehabil Sci, Sch Rehabil Sci & Engn, Qingdao 266113, Peoples R China
[3] Korea Adv Nano Fab Ctr KANC, Dept thin film, Suwon 443270, South Korea
[4] Shandong Univ, Sch Microelect, Jinan 250101, Peoples R China
基金
中国国家自然科学基金;
关键词
Flexible pressure sensor; Starch; Lignocellulose; Intelligent speech recognition; Biodegradability; HYDROGEL;
D O I
10.1016/j.nanoen.2024.109701
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Flexible pressure sensors (FPSs) are widely used in human-computer interaction (HCI) and medical diagnoses, but their further development faces several challenges. Integrating artificial intelligence (AI) with FPSs is indispensable for advancing the cognitive capabilities of HCI systems, but the FPS must also be ecofriendly, cost-effective, and high-performance to meet the demands of environmental protection and sustainable development. Here, a biodegradable capacitive FPS is presented that is fabricated from natural plant materials (i.e., cornstarch and lignocellulose) while exhibiting remarkable attributes such as high sensitivity (2.427 kPa(-1) at 0-2 kPa), rapid response/recovery time (20/24 ms), extremely low detection limit (similar to 10 Pa), outstanding stability (> 6000 fatigue tests), excellent biocompatibility (survival rate exceeding 99 % in cell viability assays), and prominent biodegradability (complete degradation within 24 h). Prepared sensors were integrated with optimized AI algorithms, embedded systems, and wireless transmission technologies to develop a dual-functional (i.e., audible and silent) speech recognition system that achieved recognition rates exceeding 95 %. The developed sensor is expected to play a crucial role in future HCI and medical assistance applications.
引用
收藏
页数:13
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