Welded Carbon Nanotube-Graphene Hybrids with Tunable Strain Sensing Behavior for Wide-Range Bio-Signal Monitoring

Carbon nanotubes (CNTs) and graphene have commonly been applied as the sensitive layer of strain sensors. However, the buckling deformation of CNTs and the crack generation of graphene usually leads to an unsatisfactory strain sensing performance. In this work, we developed a universal strategy to p...

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Published inPolymers Vol. 16; no. 2; p. 238
Main Authors Hong, Zixuan, Zheng, Zetao, Kong, Lingyan, Zhao, Lingyu, Liu, Shiyu, Li, Weiwei, Shi, Jidong
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.01.2024
MDPI
Subjects
Analysis
Annealing
bio-signal monitoring
Bonding strength
Carbon
carbon nanotube
Carbon nanotubes
Chemical processes
Chemical vapor deposition
Contact angle
Copper
Deformation
Graphene
Human motion
hybridization
Identification and classification
Methods
Nanocomposites
Nanotubes
Properties
Raw materials
Sensors
Signal monitoring
Silicon wafers
Silver
strain sensor
Temperature
Wrist
China
United States > US
carbon nanotube
hybridization
strain sensor
bio-signal monitoring
graphene
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Summary:Carbon nanotubes (CNTs) and graphene have commonly been applied as the sensitive layer of strain sensors. However, the buckling deformation of CNTs and the crack generation of graphene usually leads to an unsatisfactory strain sensing performance. In this work, we developed a universal strategy to prepare welded CNT-graphene hybrids with tunable compositions and a tunable bonding strength between components by the in situ reduction of CNT-graphene oxide (GO) hybrid by thermal annealing. The stiffness of the hybrid film could be tailored by both initial CNT/GO dosage and annealing temperature, through which its electromechanical behaviors could also be defined. The strain sensor based on the CNT-graphene hybrid could be applied to collect epidermal bio-signals by both capturing the faint skin deformation from wrist pulse and recording the large deformations from joint bending, which has great potential in health monitoring, motion sensing and human-machine interfacing.
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These authors contributed equally to this work.
ISSN:2073-4360
2073-4360
DOI:10.3390/polym16020238