Highly Sensitive and Reliable Piezoresistive Strain Sensor Based on Cobalt Nanoporous Carbon-Incorporated Laser-Induced Graphene for Smart Healthcare Wearables

The development of highly sensitive, reliable, and cost-effective strain sensors is a big challenge for wearable smart electronics and healthcare applications, such as soft robotics, point-of-care systems, and electronic skins. In this study, we newly fabricated a highly sensitive and reliable piezoresistive strain sensor based on polyhedral cobalt nanoporous carbon (Co-NPC)-incorporated laser-induced graphene (LIG) for wearable smart healthcare applications. The synergistic integration of Co-NPC and LIG enables the performance improvement of the strain sensor by providing an additional conductive pathway and robust mechanical properties with a high surface area of Co-NPC nanoparticles. The proposed porous graphene nanosheets exploited with Co-NPC nanoparticles demonstrated an outstanding sensitivity of 1,177 up to a strain of 18%, which increased to 39,548 beyond 18%. Additionally, the fabricated sensor exhibited an ultralow limit of detection (0.02%) and excellent stability over 20,000 cycles even under high strain conditions (10%). Finally, we successfully demonstrated and evaluated the sensor performance for practical use in healthcare wearables by monitoring wrist pulse, neck pulse, and joint flexion movement. Owing to the outstanding performance of the sensor, the fabricated sensor has great potential in electronic skins, human-machine interactions, and soft robotics applications.

Automated summary

In this study, a highly sensitive and reliable piezoresistive strain sensor based on polyhedral cobalt nanoporous carbon (Co-NPC)-incorporated laser-induced graphene (LIG) was fabricated for wearable smart healthcare applications. The sensor exhibited outstanding sensitivity and stability, with a detection limit of 0.02% and excellent performance even under high strain conditions. The sensor's potential applications include electronic skins, human-machine interactions, and soft robotics.