Graphite-enhanced GNP/PVA composite piezoresistors with in-house data collection and prediction algorithm for structural health monitoring
Year: 2025
Type: article
Abstract: Carbonous polymer matrix composites (PMCs) are considered among the potent candidates for piezoresistive sensing with their high gauge factor, high resolution, and applicability over large areas. However, non-linearity of their gauge factor at high strains and loss of sensitivity under cycling loading limit their widespread use. We produced a graphite-enhanced graphene nanoplatelet (GNP)/polyvinyl alcohol (PVA) composite piezoresistor to stabilize the gauge factor under various loading conditions. As a highly conductive ubiquitous material, graphite provided a backbone conduction pattern. GNPs in conjunction with the PVA matrix, on the other hand, introduced piezoresistivity to the composite paste. GNP/PVA piezoresistor pastes with graphite loadings from 5 wt.% to 40wt.% were tested both free standing and as applied on a plexiglass substrate. We report reliable and repeatable responses at a gauge factor of up to 40 and without sensitivity loss under cycling loading. The graphite enhanced GNP/PVA combining it with structural health monitoring (SHM) provided a failure prediction without the requirement of big data storage or high computational capability. We developed an in-house read-out circuit that was a low-cost replica of commercial multimeter to measure the change in resistance. We also developed an algorithm that determined the failure of a brittle material 350 s (first warning) and 10 s (second warning) before the actual failure.
Source: Journal of Composite Materials
Institutions: Sabancı Üniversitesi, California State University, Northridge
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,150
Award: G6631647810
OpenAlex ID: W4416192147