Highly-stretchable porous thermoplastic polyurethane/carbon nanotubes composites as a multimodal sensor. (15th August 2022)

• Record Type: Journal Article
• Authors: Zhu, Guoxuan; Li, Hua; Peng, Meiling; Zhao, Guiyan; Chen, Jianwen; Zhu, Yutian
• Is Part Of: Carbon. Volume 195(2022)
• Page Start: 364
• Page End: 371
• Publication Date: 2022-08-15
• Subjects: Stretchable conductive polymer composites -- Multimodal sensors -- Stretchability -- Porous structure -- Crack structure; Carbon -- Periodicals; Carbone -- Périodiques; Koolstof; Toepassingen; Electronic journals; 546.681
• DOI: 10.1016/j.carbon.2022.04.033
• Languages: English
• ISSNs: 0008-6223

Abstract

Abstract: Conductive polymer composites (CPCs) can be designed into stretchable sensors because of their flexibility and responses to external stimuli. However, sensors based on traditional CPCs normally exhibit unsatisfactory detection limits and narrow sensing ranges, which astrict their practical applications. Herein, we designed a new multimodal sensor based on porous CPCs composed of thermoplastic polyurethane (TPU) and carbon nanotubes (CNTs). The sensor can be used to detect the stimuli of strain, pressure, and temperature. As a strain sensor, ultra-low detection limit (0.01%) and ultrawide sensing range (0.01%–900%) are achieved simultaneously, owing to the highly stretchable TPU skeleton anchored with a microcracked CNT conductive layer. To our knowledge, the strain sensor possesses the lowest detection limit and the widest sensing range compared to previously reported CPC-based strain sensors. Meanwhile, this sensor can also output repeatable electrical responses to different pressure stimuli due to the reversible variation of the porous TPU skeleton and CNT networks during cyclic compression and release process. More interestingly, the as-prepared sensor is also capable of monitoring respiration because of its excellent linear negative temperature coefficient effect. It is credible that this highly stretchable sensor possesses tremendous potentials in human motion monitoring, personal healthcare monitoring, and human-computer interaction. Graphical abstract: Image 1