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Research On Flexible Strain Sensors Based On 3D Graphene Networks

Posted on:2019-01-18Degree:MasterType:Thesis
Country:ChinaCandidate:C L GuoFull Text:PDF
GTID:2428330596451026Subject:Engineering
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Currently widely used strain sensors are mostly made of metal and semiconductor materials,resulting in poor flexibility,very limited measurement range,low sensitivity and complex analysis circuits,which limits their use in flexible,intelligent systems.In recent years,with the development of technologies in human physiological health monitoring,artificial electronic skin,virtual reality and flexible touch screen,there is a huge demand for strain sensing devices with high sensitivity,flexibly,large strain tolerance and self-healing ability.In search of technological breakthroughs,many research efforts have been focused on nanomaterials.Graphene,as the thinnest material with exceed mechanical-electrical properties and flexibility in nature,has shown extensive potential applications in the next generation of high-performance strain sensors.In this thesis,we fabricated two kinds of flexible graphene strain sensors with different three-dimensional graphene networks using improved chemical-vapor-deposition(CVD)growth methods and transfer technique.We systematically investigated the performance and mechanism of these two kinds of devices,respectively,and explored their practical applications.The highly transparent three-dimensional graphene trench networks(T-GTN)and their strain sensors were firstly studied in this thesis.The T-GTNs were prepared using a nickel-vapor-assist CVD process.Using three-dimensional copper meshes as templates,few layers(2-3 layers)graphene were grown into transparent three-dimensional networks.Then,the graphene networks were transferred onto the polydimethylsiloxane(PDMS)flexible substrate through a "soft-gel one-step transfer" method.The three-dimensional,highly transparent T-GTN networks with a trench-like microstructure were preserved after transferring.The strain sensors constructed with the T-GTN networks exhibit more than 80% transparency,response time as small as 280 ms and strain detecting limit as small as 0.015%.After 3650 cycles of 0.3% strain testing,the drift of resistance is less than 5%.The results of in-situ scanning electron microscopy and Raman spectroscopy analysis show that the strain-resistance responses of these devices are mainly based on the recoverable microcracks generated on the graphene surface during the stretching process.Further,to obtain sensors suitable for large strain measurement,we fabricated three-dimensional graphene foam networks(GFN)and corresponding strain sensors.The GFN was prepared through a well-established CVD process using three-dimensional nickel foam as growth substrate.The three-dimensional graphene foam network was still transferred to PDMS substrate using the above "soft-gel one-step transfer" method.The strain sensors constructed with these GFNs exhibit a very small detection limit(< 150 ??),large gauge factor(>100),broad measurement range(0.015% to 27.8%).In particular,when the device is stretched to more than 30%,that the electrical connection has been broken,it can still return to its initial state through a several seconds long self-healing process.Microscopic observations shown that the resistance change is mainly caused by the changes of overlapping areas between the graphene microtablets and the conductive channels.Finally,we demonstrated some possible applications of our graphene strain sensors.Applying a flexible sensor onto the human skin,physiological signals for heartbeat pulse,breathing,blinking and swallowing could be real-time monitored and transmitted to a smart phone via the Bluetooth module.A variety of strain signals,as large as the body and joint movements of human,as small as the air vibrations caused by talking,all can be easily obtained with the same sensor,which shows that such devices will have great potential in the future.
Keywords/Search Tags:graphene, strain sensor, chemical vapor deposition, gauge factor, flexible device, physiological signals monitoring
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