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Design Of Carbon-Based Elastic Conductive Fiber Sensor For Health Applications

Posted on:2021-04-28Degree:MasterType:Thesis
Country:ChinaCandidate:Y B FanFull Text:PDF
GTID:2381330611981902Subject:Engineering
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In recent years,the continuous innovation of materials science and micro-nano processing technology has provided strong support for the research of flexible strain sensors with wide detection range,Flexible sensors have been closely watched by high-tech fields such as machinery,medical treatment,and communication due to it's ultra-long stretching range,excellent torsional bending ability and excellent biocompatibility,for achieving high-sensitivity,multi-application scenarios of wearable medical devices,bio-sensing,flexible robots and other intelligent devices.At the same time,although current research results in the field of flexible sensing continue to emerge,flexible strain sensors based on piezoresistive,piezoelectric,and capacitive types can basically meet the test function,but the research on the dynamic strain stability,hysteresis and durability of flexible sensor is still unsatisfactory.The study found that fibrous flexible sensors have better physical characteristics than traditional structural flexible sensors,The Sensitivity of the flexible sensor can be improved by preparing microstructures on the surface of the flexible substrate material and forming a conductive layer between the carbon nanomaterial and the flexible substrate,Based on this situation,a carbon-based elastic conductive fiber sensor with flexibility,high stretch,high biocompatibility and high sensitivity was designed and prepared.Make the Ecoflex material fibrous by glass capillary,Ecoflex has good biocompatibility.Take advantage of Ecoflex's hydrolysis characteristics under strongly acidic conditions,Place different dimeter Ecoflex fibers into High concentration HF solution for Microstructured fiber.Hereafter mix the materials which have high conductive properties?such as carbon nanofibers,carbon nanotubes,etc.?with the flexible fibers,for improving the sensitivity of the elastic conductive fiber sensor.Set sensitivity as the main reference factor for testing Carbon-based elastic conductive fiber.Using Orthogonal experiments to Optimize sensor preparation process.The optimized crafts is: The flexible substrate material utilize Ecoflex0050,the conductive layer material utilize carbon nanofibers,the flexible fiber substrate diameter set as 0.5mm,and the reaction time in HF solution keep 90 seconds.Test and analyze the elastic conductive fiber sensor prepared after the optimized process,Set sensitivity as the main reference coefficient to detect Carbon-based elastic conductivefiber;The results show that the sensor has almost no change in its ?R/R0 value after 1000 times of repeated stretching,Shows that it has excellent repeatability and stability,The hysteresis of this sensor is as low as 14%,The response to the tensile-recovery test is faster at the millisecond level,and the linearity is better at 4.5588%,The lower limit of its monitoring is 0.01%,which is one order of magnitude better than other sensors reported at present,and the Gauge Factor can reach 696.57 in the case of large stretch?300%?,It is an order of magnitude higher than other carbon-based strain sensors reported so far.Using the prepared elastic conductive fiber sensor to test the key physiological signals of human body,It realizes the function of monitoring the weak physiological characteristics such as pulse and breathing of the human body,and the large-scale limb movements such as jumping,running,gesture recognition,etc.In order to realize the function of portable real-time monitoring,Based on the prepared elastic conductive fiber sensor,Build a real-time monitoring system with n RF52832 chip as the core,which Transmit data to mobile terminal by Bluetooth technology for Convenient monitoring,Initially achieve the intended purpose.
Keywords/Search Tags:Flexible Sensor, Wearable Medical Equipment, Carbon-Based Elastic Conductive Fiber, Portable Real-Time Monitoring System, Human Physiological Signal Monitoring
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