Ultra-low detection limit self-sensing nanocomposites with self-assembled conductive microsphere arrays for asphalt pavement health monitoring

Abstract

Accurately monitoring micro-level strain within pavements poses a significant challenge in contemporary road engineering, limiting the effectiveness of early-stage pavement health diagnostics. Addressing this critical gap, this study introduces an innovative ultra-low detection limit sensor, specifically designed for intricate pavement health monitoring. It pivots on the unique structural design of a well-ordered 3D conductive network, where the self-assembly effect of microsphere arrays ingeniously mitigates the prevalent issue of nanofiller agglomeration within polymer matrices. The study highlights the significant role of carbon nanotubes (CNTs) length in shaping the structure of conductive networks, revealing that while longer CNTs lead to less uniform microsphere assemblies, they significantly enhance the bridging effect crucial for conductive pathways. To balance these traits, surface chemical modifications were strategically applied, effectively enhancing microsphere organization and dispersion, thereby elevating the conductive and sensing performance. The optimized sensor stands out for the ultra-low detection limit of 0.0005% (5 uε), significantly surpassing those of current strain sensors. Moreover, it exhibits remarkable sensitivity, evidenced by a gauge factor of 13.2, along with swift response and recovery times of 19 ms and 26 ms respectively, and impressive durability, enduring over 100,000 loading-unloading cycles. Field tests under diverse vehicular loads and speeds further validate the sensor's accuracy in capturing dynamic responses within pavement structures, a key factor in proactive pavement maintenance and management. Hence, this advancement shows promise in contributing to the development of safer and more resilient road networks, playing a beneficial role in the significant progress of intelligent transportation and sustainable infrastructure management.