Piezoelectric Bending Motion Sensor for Simultaneous Detection of Bending Curvature and Speed

Abstract

Development of portable and flexible bending motion sensors has been recently flourished for a variety of devices involving integration with wearable electronics, human artificial skin, and biomimetic robots. Most studies on bending motion sensors exploit transduction mechanism including resistivity, capacitance changes and optical phenomena, which essentially require external power supplies for operation of sensor system. However, self-powered system might be significant for portable and environmentally compatible and persistent system for potential uses in robotics or portable mobile electronics. In this regard, adopting piezoelectric bending elements is desirable for active sensing system as well as the superiority for ultra-flexible properties for wearable manufacturing. Multi-functional capabilities of motion sensing will enable high accurate detection of any external stimuli of bending motions. Most researches on piezoelectric bending motion sensors, however, have demonstrated a sensing algorithm based on the change of piezoelectric potential as a function of solely bending curvature. In fact, the piezoelectric signals are comprehensive results affected by various factors such as bending curvature and speed. More precisely, the output signal values are differentiated as a function of bending curvature, otherwise also distinguished by different bending speed at the same time. In this point of view, the piezoelectric output signals at the same bending curvature conditions with different bending speed will apparently have different signal values. Therefore, the dependence on output signals in response of bending motion across the piezo elements should be separately investigated in order to enhance the accuracy of sensing. For analysis of bending curvature and speed for detection of bending motions across the piezo elements, a systematic algorithm tool is required to be organized. Herein, a piezoelectric bending motion sensor has been developed for simultaneous recognition of bending curvature and speed through systematic analysis based on experimentally ratiocinative algorithm tool. Logically, the strategy for detection bending motion is to separate the influence of the bending curvature and speed while figure out the relationship between dependent variables characterizing a given piezoelectric signals with the independent variables. The dependent variables to be measured are a height, an area, and a width of voltage signals, whereas the independent variables to be estimated are bending curvature and speed. On the basis of the hypothesis, an experimentally ratiocinative algorithm was established for simultaneous recognition of bending curvature and speed across the piezoelectric bending motion sensors. Through simple three steps: measurement of output signals; construction of an algorithm; estimation of independent variables, sensing of the bending curvature and speed is simultaneously available with high accuracy. In addition, elastic piezoelectric composite material; ZnO nanorods (NR) - polydimethylsiloxane (PDMS) sandwiched structure and the mechanically stable and robust electrodes; Ag nanowire (NW) -single-wall carbon nanotube (SWCNT) hybrids are both remarkable to apply in flexible bending motions in piezoelectric sensory system. The hydrothermally grown ZnO NRs with bi-axial configuration were facile for bending motion sensing. The bi-axially grown ZnO NRs form uniform monolayer by simple rubbing process because of strong Van der Waals forces. Moreover, adoption of elastic electrodes of Ag NW-SWCNT guarantees flexibility without any structural cracks with consistent maintenance of electrical conductivity. From the configuration of piezoelectric structures, highly flexible piezoelectric bending motion sensing was actualized for any external stimuli. This reciprocal piezoelectric bending motion sensor and algorithm system can be a practical sensing methodology toward realization of artificial and wearable motion sensors. Figure 1