Ultrasensitive Nanostructured Capacitive Sensors; Fabrication, Auxeticity, Capacitive Sensitivity, and Human-Machine Interface

Abstract

Machine and robots have been widely employed in modern manufacturing, factories, medicares, and our daily lives. Various sensors have been developed for human-robot collaboration (HRC) to offer a customized interface. However, the multiple challenges including high cost, production, sensitivity, and flexibility limit versatile sensor applications to HRC.In this dissertation, a novel capacitive sensor made of carbon nanotube paper composite (CPC) will be studied and characterized to enable a low-cost, high-performance sensing platform. Based on a wet stretching method, the auxetic behavior of the carbon nanotube composite (CPC) is studied and characterized, which is related to the capacitive sensing performance. Using the fabrication process, the capacitive sensors made of molecular junctions are produced to enable the highly sensitive detection of humidity and liquid level. Using the co-planar configuration of CPC electrodes, a capacitive sensor is fabricated to monitor humidity change. The sensing mechanism of capacitive response is studied in the context of the molecular junctions among the conductive fibers. Due to auxeticity and buckling, the capacitance forms among the radially diverging structures, which enhances the sensitivity to humidity and moisture. A highly sensitive capacitive sensor is studied for non-contact liquid level detection by using the single electrode sensor made of fractured CPC. The liquid level is measured by the capacitance value change induced by the dimensional change of the liquid. The detection for both non-conductive and conductive containers is characterized by developing the differential measurement configuration. Simulation and experimental studies are conducted to understand the liquid sensing mechanism. The sensitivity and accuracy in terms of liquid volume and liquid level are characterized by the dimensions. To validate the use of CPC sensors on human-machine interface, a smart pad is fabricated for gesture recognition. The real-time monitoring model is developed and applied to motion recognition. Artificial intelligence algorithm is used to develop the initial gesture recognition platform. The accuracy of the prediction model is evaluated. In summary, the dissertation presents the characteristics of the capacitive sensor made of fractured CPC and the applications to humidity and liquid detection. In combination with the sensors, the developed gesture recognition platform can contribute to human-machine interface and human-robot collaboration. For example, monitoring of liquid level will aid the automation and smart control of vending machine, factory production, food processor, etc. The fabricated sensor is cost-effective, flexible, and versatile with a small form factor, which will offer huge potential in the next generation sensing platform that benefits the HRC.