journal article·2021-11Open access

Understanding the sensitivity of thin-film graphene/polymer nanocomposite strain sensors to ultrasonic waves: Analytical and experimental analysis

Ruiqi Guan, Fangxin Zou, Dan Li, Wenming Liu, Congping Wu

Abstract

Abstract Thin-film graphene/polymer nanocomposite sensors have been shown to be exceptionally sensitive to ultrasonic waves, making them promising next-generation candidates for structural integrity monitoring. However, the ultrasonic sensing mechanism of these sensors has never been scrutinized, restricting the deployment of these sensors to real-life applications. Herein, we carry out the first-ever study on the ultrasonic sensing mechanism of thin-film graphene/polymer nanocomposite sensors, through complementary physical experiments and analytical modelling. At first, sensors were precisely fabricated from nanofillers of different sizes and different matrix materials, and their electrical conductivities and ultrasonic sensitivities were measured. Analytical models that are based on the effective medium theory and the various contact modes between graphene nanofillers, entailing interphase regions and the quantum tunneling effect, were then established and fitted to the experimental results to reveal a series of microscopic characteristics of the sensors fabricated. Through a systematic analysis, it was found that the sizes of nanofillers and the properties of matrices significantly influence the microscopic morphologies and strain-induced dynamics of the sensors, in turn dictating their electrical conductivities and ultrasonic sensitivities. This insightful study will serve as the foundation for realizing applications of high-sensitivity thin-film graphene/polymer nanocomposite sensors in real-life ultrasound-based structural integrity monitoring scenarios.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Smart Materials for Construction · Advanced Sensor and Energy Harvesting Materials · Non-Destructive Testing Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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