文献类型：期刊文献

英文题名：High-sensitivity omnidirectional recognition strain sensor based on two-dimensional materials

作者：Fu, Chuanjie[1,2,3];Rong, Chao[1,2,3];Zhang, Bowei[1,2,3];Xuan, Fu-Zhen[1,2,3]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety, Minist Educ, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2025

卷号：18

期号：6

外文期刊名：NANO RESEARCH

收录：;EI(收录号：20252718706552);WOS:【SCI-EXPANDED(收录号:WOS:001522559500001)】；

基金：This work was supported by the National Natural Science Foundation of China (Nos. 52422505 and 12274124) , the Shanghai Pilot Program for Basic Research (No. 22TQ1400100-6) , the Fundamental Research Funds for the Central Universities, and the Innovative Research Group Project of the National Natural Science Foundation of China (No. 52321002) .

语种：英文

外文关键词：omnidirectional strain detection; machine learned; flexible strain sensor; MXene

摘要：Flexible strain sensors are essential in fields such as medicine, sports, robotics, and virtual reality but face challenges in achieving excellent sensing performance and accurate multi-directional detection simultaneously. To address this issue, we have developed a spider-web structured multi-directional flexible strain sensor using Ti3C2Tx (MXene) conductive ink and three-dimensional (3D) printing technology. Combined with a multi-class, multi-output neural network model algorithm, the sensor achieves signal decoupling from the sensor array, allowing for precise detection of strain direction and intensity. It exhibits good sensitivity (gauge factor similar to 26.3), a moderate sensing range (0%-10%), and high reliability (1000 stretching cycles). Using neural network algorithms, a four-unit spider-web sensor array achieves approximately 97% accuracy in identifying strain intensity and direction within the 0%-10% strain range under various surface stimuli. Additionally, it can track complex human motions, demonstrating significant potential in applications such as motion monitoring and human-machine interaction.