Quantifying the Contributing Factors toward Signal Fatigue in Nanocomposite Strain Sensors

With unparalleled sensitivities, nanocomposites are believed to be key components in future bodily sensor and healthcare devices. However, there is a lack in understanding of how repeated strain cycles effect their electromechanical performance and what measures can be taken to accommodate changes in measurement using modelling and signal processing. Here, the author examines published cyclic data from a wide range of nanocomposite strain sensors. From the datasets, the author reports a near universal scaling in electromechanical signal with cycle number (C) as a result of the Mullin’s effect. Using a modified model based on Basquin’s law of fatigue, for all nanocomposites, signal was found to following 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. Endurance limit was reported to be highly dependent on the scaling exponent noted in the cyclic data.