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Design of a wearable and shape-memory fibriform sensor for the detection of multimodal deformation.
Li, Li; Shi, Peipei; Hua, Li; An, Jianing; Gong, Yujiao; Chen, Ruyi; Yu, Chenyang; Hua, Weiwei; Xiu, Fei; Zhou, Jinyuan; Gao, Guangfa; Jin, Zhong; Sun, Gengzhi; Huang, Wei.
Affiliation
  • Li L; Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University (NanjingTech), 30 South Puzhu Road, Nanjing 211816, P. R. China. iamgzsun@njtech.edu.cn iamwhuang@njtech.edu.cn.
Nanoscale ; 10(1): 118-123, 2017 Dec 21.
Article in En | MEDLINE | ID: mdl-29211073
ABSTRACT
A wearable and shape-memory strain sensor with a coaxial configuration is designed, comprising a thermoplastic polyurethane fiber as the core support, well-aligned and interconnected carbon nanotubes (CNTs) as conductive filaments, and polypyrrole (PPy) coating as the cladding layer. In this design, the stress relaxation between CNTs is well confined by the outer PPy cladding layer, which endows the fibriform sensor with good reliability and repeatability. The microcracks generated when the coaxial fiber is under strain guarantee the superior sensitivity of this fibriform sensor with a gauge factor of 12 at 0.1% strain, a wide detectable range (from 0.1% to 50% tensile strain), and the ability to detect multimodal deformation (tension, bending, and torsion) and human motions (finger bending, breathing, and phonation). In addition, due to its shape-memory characteristic, the sensing performance of the fibriform sensor is well retained after its shape recovers from 50% deformation and the fabric woven from the shape-memory coaxial fibers can be worn on the elbow joints in a reversible manner (original-enlarged-recovered) and fitted tightly. Thus, this sensor shows promising applications in wearable electronics.

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Nanoscale Year: 2017 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Diagnostic_studies Language: En Journal: Nanoscale Year: 2017 Document type: Article