Graphene-functionalized polymer composites for self-sensing of ultrasonic waves: An initiative towards “sensor-free” structural health monitoring
Source abstract
Abstract
Abstract With recognized bottlenecks of guided ultrasonic wave (GUW)-based structural health monitoring (SHM) for composites, conventional polymers are nano-engineered and endowed with capability of self-perceiving GUWs. A built-in sensing network is formed with graphene nanoparticles, optimized and diffused in fibre-reinforced polymers, in which the quantum tunneling effect can be locally triggered when GUWs traverse the composites. The diffuse sensing network makes it possible to acquire GUWs at any site of the functionalized composites, avoiding use of conventional ultrasonic transducers that must be externally attached to or internally embedded in the composites. With an optimized nano-structure, the functionalized composites have been demonstrated self-responsive to GUWs up to 500 kHz. In experimental validation, GUWs propagating in a glass fibre/epoxy laminate are self-sensed by the laminate at the sites arbitrarily selected, to observe no discrepancy against counterpart signals obtained with piezoelectric sensors. To take a step further, barely visible impact damage (BVID) in the laminate is located accurately using the self-sensed GUW signals. This study has spotlighted a new breed of functional polymers with capability of self-health monitoring, without using external sensors. The use of associated cables and wires is also minimized. Not only does it facilitate a reduced weight/volume penalty to the original composites, but also minimizes mechanical degradation of the composites due to the intrusion of sensors, blazing a potential trail in developing “sensor-free” SHM for composites.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Smart Materials for Construction · Concrete Corrosion and Durability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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