Quantifying the Contributing Factors toward Signal Fatigue in Nanocomposite Strain Sensors
Conor S. Boland
Abstract
With unparalleled sensitivities, nanocomposites are believed to be key components in future bodily sensors and healthcare devices. However, there is a lack in understanding of how repeated strain cycles affect their electromechanical performance and what measures can be taken to accommodate changes in measurement using modeling and signal processing. Here, the author examines published cyclic data from a wide range of nanocomposite strain sensors. From the data sets, the author reports a near universal scaling in an electromechanical signal with cycle number (C) as a result of the Mullins effect. Using a modified model based on Basquin’s law of fatigue, for all nanocomposites, the signal was found to follow a nearly identical C–0.1 power-law scaling with cycle number. Using the presented model, the author demonstrated that a critical conditioning cycle number for a nanocomposite at which a steady-state signal occurs, known as the endurance limit, can be predicted. The endurance limit was reported to be highly dependent on the scaling exponent noted in the cyclic data.