No abstract

Carbon aerogels demonstrate wide applications for their ultralow density, rich porosity, and multifunctionalities. Their compressive elasticity has been achieved by different carbons. However, reversibly high stretchability of neat carbon aerogels is still a great challenge owing to their extremely dilute brittle interconnections and poorly ductile cells. Here we report highly stretchable neat carbon aerogels with a retractable 200% elongation through hierarchical synergistic assembly. The hierarchical buckled structures and synergistic reinforcement between graphene and carbon nanotubes enable a temperature-invariable, recoverable stretching elasticity with small energy dissipation (~0.1, 100% strain) and high fatigue resistance more than 106 cycles. The ultralight carbon aerogels with both stretchability and compressibility were designed as strain sensors for logic identification of sophisticated shape conversions. Our methodology paves the way to highly stretchable carbon and neat inorganic materials with extensive applications in aerospace, smart robots, and wearable devices.

Understanding and modulating the conformation of graphene are pivotal in designing graphene macroscopic materials. Here, we revealed the sheet collapsing behavior of graphene oxide (GO) sheets by poor solvents in an analogy with linear macromolecules. Triggered by poor solvents, extended GO sheets in good solvents can collapse to hierarchically wrinkled conformations. The collapsing behavior of GO enabled the fabrication of amorphous self-standing GO and graphene papers with rich hierarchical wrinkles and folds over mutliple size scales. The collapsed GO and graphene papers had a rubber-like mechanical behavior with viscoelasticity. By our collapsing method, GO and graphene self-standing papers were designed to be stiff with high modulus or to become soft with low modulus of 100 MPa at a remarkably large breakage elongation up to 23%. Our philosophy of treating graphene as a 2D polymer enables the efficient control of molecular conformations of graphene and other 2D polymers and the design of macroscopic materials of 2D nanomaterials as in the polymer industry.

Soft robots driven by stimuli-responsive materials have their own unique advantages over traditional rigid robots such as large actuation, light weight, good flexibility and biocompatibility. However, the large actuation of soft robots inherently co-exists with difficulty in control with high precision. This article presents a soft artificial muscle driven robot mimicking cuttlefish with a fully integrated on-board system including power supply and wireless communication system. Without any motors, the movements of the cuttlefish robot are solely actuated by dielectric elastomer which exhibits muscle-like properties including large deformation and high energy density. Reinforcement learning is used to optimize the control strategy of the cuttlefish robot instead of manual adjustment. From scratch, the swimming speed of the robot is enhanced by 91% with reinforcement learning, reaching to 21 mm/s (0.38 body length per second). The design principle behind the structure and the control of the robot can be potentially useful in guiding device designs for demanding applications such as flexible devices and soft robots.