In situ laser synthesis of Pt nanoparticles embedded in graphene films for wearable strain sensors with ultra-high sensitivity and stability

Carbon-based materials have recently attracted tremendous attention for their applications in highly sensitive wearable devices for human motion monitoring. Embedding rare metal nanoparticles such as Au, Ag, and Pt has been proved to significantly improve the sensitivity of the carbon-based strain sensors. However, the existing method to prepare nanoparticles usually involves multi-step, time-consuming procedures. Here, we report a facile one-step laser process to synthesize porous 3D graphene structures embedded with Pt nanoparticles. This is achieved by irradiating an ultraviolet (355 nm) picosecond laser on a film formed with a mixture of polybenzimidazole (PBI) and Pt(acac)2 solution to simultaneously induce the formation of graphene and reduction of the Pt precursor. The assembled strain sensor with loaded Pt nanoparticles in laser-induced graphene (Pt/LIG) demonstrates ultra-high sensitivity (a gauge factor, GFmax z 489.3) and a wide strain range up to 20%. Additionally, the strain sensor with an optimized concentration of Pt(acac)2 has low hysteresis, minor lag errors, and a highly stable response (>5000 cycles). Various human motion signals are detected, from subtle physical activities (including chewing muscles movement, pulse beating) to large-scale limb track (finger bending, knee movement, and waist extending). This one-step laser process is facile, rapid, and potentially scalable for the mass production of high-performance wearable strain sensors. Crown Copyright (c) 2022 Published by Elsevier Ltd. All rights reserved.

Automated summary

Carbon-based materials have gained significant attention for their potential in highly sensitive wearable devices. This study presents a one-step laser process to synthesize porous 3D graphene structures embedded with Pt nanoparticles, achieving ultra-high sensitivity and a wide strain range. The synthesized strain sensor demonstrates excellent performance in detecting various human motion signals.