| Literature DB >> 30844231 |
Min-Young Cho, Jeong Heon Lee, Seong-Hoon Kim, Ji Sik Kim, Suman Timilsina.
Abstract
Here, we describe the utility of a carbon fiber (CF) electrode that is inexpensive, simple, and flexible and can be embedded with elastomeric nanocomposite piezo-resistive sensors fabricated from silicone rubber (Ecoflex) blended with carbon nanotubes (CNTs) and various wt % of silicone thinner to tune the sensitivity and softness range. The performance of the CF electrode was evaluated on the basis of piezo-resistive responses from the sensors subjected to dynamic sinusoidal compressive strains at different levels and frequencies. The responses were positive-pressure effects with rate-dependent asymmetric nonlinear hysteresis characteristics. Developing a mathematical model to describe the rate-dependent asymmetric nonlinear hysteresis behavior is technically impossible; therefore, we employed artificial intelligence-based hysteresis modeling, long short-term memory recurrent neural network, to describe the hysteresis nonlinearity. The debonding strength of the CF electrode was determined in the pull-off testing and was found to be much higher than that of a copper wire electrode. The debonding mechanism was further elucidated via an in situ resistance profile. The importance of a robust conductive interface between a CF electrode and a nanocomposite was experimentally demonstrated. It was found that the inherent piezo-resistance of the CF was negligible compared with the piezo-resistance of the sensor; therefore, the signals from the sensor were free of interference. We believe CF-embedded tunable piezo-resistive sensors could be used in biomedical devices, artificial e-skins, robotic touch applications, and flexible keyboards where the required stretchability of the electrode can be introduced via an appropriate geometrical design.Entities:
Keywords: artificial intelligence; carbon fiber electrode; conductive interface; nonlinear hysteresis; positive-pressure-effect; tunable piezo-resistive sensor
Year: 2019 PMID: 30844231 DOI: 10.1021/acsami.9b00464
Source DB: PubMed Journal: ACS Appl Mater Interfaces ISSN: 1944-8244 Impact factor: 9.229