Additive-free oil-bath-printing dual-hyperbolic graphene aerogel for smart wearable sensors

Direct ink writing demonstrates the potential to print functional 3D graphene devices, while self-weight-induced shear thinning and structural collapse cause it to fail to construct complex architectures. In this study, a lightweight 3D graphene metamaterial lattice (GML) with a dual-hyperbolic pattern is generated using a deep-learning-driven framework. An oil-bath printing strategy is developed to overcome the challenge of shear thinning and structural collapse. Benefiting from the hierarchical structure, the resulting 3D GML and its polymer-reinforced device demonstrate anomalous super-elastic deformability (90% compression), a negative Poisson's ratio (NPR) (−0.62–0), and excellent fatigue resistance. Moreover, the highly interconnected porous scaffolds endowed the 3D GML with exceptional electrical conductivity (1.998 S/cm) and thermal insulation (0.0256 W/(m·K)). Consequently, when acting as ultra-flexible wearable sensors, the 3D GML exhibits highly sensitive and robust responses to external physical stimuli, offering a pathway for developing 3D graphene-based multifunctional devices, with significant potential for thermal barriers, smart sensors, etc.