Stress and Airflow-Sensitive 3D-Printed Hydrogel Sensor Based on Cu2+-Alanine Coordination and Graphene Sheet Networks

Abstract

Flexible sensors with applications in real-time monitoring of human health conditions, e.g., breath, are of vital importance. Herein, we report a 3D-printed hybrid network (HN) hydrogel sensor showing stress and airflow monitoring performance based on the sensitive Cu2+-alanine (Ala) coordination cross-linked poly(N,N-dimethylacrylamide-co-3-alanine-2-hydroxypropylmethacrylate) (PDA) network and the graphene (GR) filler network. The highly stress and airflow-sensitive HN hydrogel sensor is prepared by sequential 3D printing of the polyacrylic acid hydrogel layer and the patterned PDA/Cu/GR hydrogel layer cross-linked by the Cu2+-Ala coordination. The graphene sheets in the PDA/Cu/GR hydrogel form lamellar structures, which generate a rough and porous network structure. The stress sensitivity of the HN hydrogel sensor stems from the dynamic Cu2+-Ala coordination within the PDA network and the rough, porous network structure of the graphene filler, while its airflow sensitivity is attributed to the reversible and rapid water loss and reabsorption behavior. The sensor’s stress sensitivity enables the detection of the motion speed of the object, hand gestures, and facial expressions. The fist punching out/pulling back test and human breath detection demonstrate the application for airflow detection. Our work provides a new opportunity for fabricating multimodal hydrogel sensors with potential applications for human healthcare and activity monitoring.