碳纳米管复合亚麻纤维柔性传感材料的制备

Preparation of flexible sensing material of flax fiber combined carbon nanotubes

  • 摘要: 导电的碳纳米管(CNTs)与不导电的亚麻纤维(CEL)相结合,可以得到柔性导电复合材料。拉伸或弯曲该材料对其导电性能影响很大。根据电阻变化率(ΔR/R0)可以敏锐地检测到材料形状的变化,因此CNTs/CEL复合材料适用于柔性传感器。用NaOH/尿素水体系处理亚麻纤维,得到CEL浆,再与不同浓度的CNTs悬浊液混合、抽滤、干燥,制得了CNTs/CEL复合材料。用XRD、FTIR和SEM分析了CNTs/CEL复合材料的结构形态。将CNTs/CEL复合材料制成形变传感器,用拉伸导电性能测试了拉伸对传感器导电性能的影响;将传感器应用到手指关节上,用电阻变化监测了手指弯曲时传感器的形变敏感性。结果发现,随着拉伸应变的增加,CNTs/CEL传感器的电阻变化率ΔR/R0逐渐增大,50%应变下,ΔR/R0达到980以上,能灵敏地感知到形状的变化;随着手指关节弯曲程度的增加,CNTs/CEL传感器电阻随之增大,手指最大程度弯曲时,CNTs/CEL传感器电阻可以达到12000 Ω以上,而且重复性良好。

     

    Abstract: The combination of conductive carbon nanotubes (CNTs) and non-conductive flax fibers (CEL) can produce flexible conductive composites. Stretching or bending the material greatly affects its electrical conductivity. According to the resistance change rate(ΔR/R0), the change in the shape of the material can be sensitively detected, so the material is suitable for deformation sensors. The flax fiber pulp was obtained using sodium hydroxide/urea water system to treat flax fibers, which is then mixed with different concentrations CNTs suspensions, filtered, and dried to prepare conductive CNTs/CEL composite. The structure and morphology of the CNTs/CEL composites were evaluated withXRD, FTIR and SEM. The CNTs/CEL composite materials were made into deformation sensors, and the tensile conductivity was used to test the effect of stretching on the conductivity of the sensors; the optimized sensor was used to monitor the finger joints bent movement on the bases of the resistance change to test the deformation sensitivity of the sensor. The results show that with the increase of tensile strain, ΔR/R0 of CNTs/CEL sensor increases gradually. Under 50% strain, ΔR/R0 reaches over 980 and could be sensitively sensed shape changes. As the degree of bending of the finger joint increases, the resistance of the CNTs/CEL sensor increases as well. When the finger is bent to the greatest extent, the resistance of the CNTs/CEL sensor can reach more than 12000 Ω, and the repeatability is good.

     

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