Pre-Strain Method for Stretchable Strain Sensors With Linear Sensitivity Using Acrylic Elastomer and CNTs Powder

Abstract

Stretchable sensors can measure large strains generated in the joints of the human body. Generally, in the characteristics of carbon nanotubes (CNTs) based stretchable resistive sensors, the reference resistance (resistance at 0% strain) increases with repeated strain, and the gauge factor (GF) which indicates sensitivity is not constant. These characteristics are related to the rupture of the CNT networks. This study proposes a pre-strain method to produce the resistive strain sensor with linear sensitivity. We discovered that this method aligns the agglomerated CNTs depending on the pre-strain. The pre-strain method is simple because it requires just a brush and stretching jig without the splay, bar-coater or 3D printer. The sensor is completed by applying CNT powder to an elastomer that has been pre-strained 47% in the uniaxial direction and then returning it to its initial state (0% strain). In the strain from 0% to 100%, the GF of the sensor is 1.46 constantly, and the variance of the normalized change in resistance of the sensor is less than 0.06. Therefore, the sensibility of the sensor has strong linearity. This result solves the problem of non-linear sensitivity of sensors using CNT powder in previous research. As a demonstration, we mounted this sensor onto a wearable device for the knee joint. We acquired static angle data and dynamic walking and running data. In static experiments, the relationship between knee joint angle and normalized change in resistance was linear. The maximum uniaxial deformation velocity of 122.1 mm/s could be measured in the dynamic experiment. When the angle was returned to 0° after repeated knee joint bending, the normalized change in resistance returned to the reference value, suggesting that the rupture of the CNT networks was suppressed. These features of constant gauge factor and reference resistance are expected to simplify the system's data processing.