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Double layer carbon-nanoparticle-based flexible pressure sensor with high precision and stability
Extreme Mechanics Letters ( IF 4.3 ) Pub Date : 2020-04-10 , DOI: 10.1016/j.eml.2020.100715
Xin Yang , Zi-Mei Cao , Yan Chen , Di Wu , Xian-Bo Qiu , Du-li Yu , Xiao-Liang Guo

Recently, the development of electronic skin and smart wearable sensor have put forward high requirements for flexible sensor with excellent performance. As for a flexible sensor, precision and stability are also significant parameters besides the sensitivity and measuring range. Flexible piezoresistive sensors based on nanomaterial/nanostructure have been reported to possess the high sensitivity. Precision and stability are also important for a sensor, hence we focused on promoting them in this paper. Furthermore, a mechanism for random error generated by a nano-based piezoresistive sensor was proposed. Based on the mechanism, a certain high frequency testing signal was used instead of the direct current signal to promote the precision of the sensor. The relative random error of the sensor decrease greatly above 800 kHz (by three order at low pressure range), which increase the precision greatly. Long-term stability was tested using a total of 12 000 consecutive compression tests. After a 250 s aging process, the sensor remained incredibly stable and only 0.08% change occurred which is much better than those of reported sensors. The sensor also possessed a sensitivity of 0.24 kPa−1 and a linear measuring range of 0–4 kPa. Some applications, including respiratory/arterial pulse measurement and gait recognition, were demonstrated finally.



中文翻译:

基于双层碳纳米粒子的柔性压力传感器,具有高精度和稳定性

近年来,电子皮肤和智能穿戴式传感器的发展对具有优异性能的柔性传感器提出了更高的要求。对于柔性传感器,除了灵敏度和测量范围外,精度和稳定性也是重要的参数。据报道,基于纳米材料/纳米结构的柔性压阻传感器具有很高的灵敏度。精度和稳定性对于传感器也很重要,因此我们在本文中重点介绍它们。此外,提出了一种基于纳米压阻传感器产生随机误差的机制。基于该机制,使用一定的高频测试信号代替直流信号来提高传感器的精度。传感器的相对随机误差在800 kHz以上时会大大降低(在低压范围内降低三阶),从而大大提高了精度。使用总共12 000个连续压缩测试对长期稳定性进行了测试。经过250 s的老化过程后,传感器保持了令人难以置信的稳定,仅发生了0.08%的变化,这比报告的传感器要好得多。该传感器还具有0.24 kPa的灵敏度-1,线性测量范围为0–4 kPa。最后展示了一些应用,包括呼吸/动脉搏动测量和步态识别。

更新日期:2020-04-10
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