Tailoring Stress-Strain Curves of Flexible Snapping Mechanical Metamaterial for On-Demand Mechanical Responses via Data-Driven Inverse Design.

By incorporating soft materials into the architecture, flexible mechanical metamaterials enable promising applications, e.g., energy modulation, and shape morphing, with a well-controllable mechanical response, but suffer from spatial and temporal programmability towards higher-level mechanical intelligence.

Partial Liquid Alloy Microdroplet Sedimentation Induced a Gradient Porous Structured Elastomer with a Tunable Property for an Anisotropic Robotic Bulk.

With the confrontation of ever increasing complicated working objects and unstructured environments, it is necessary for soft robots to be equipped with diverse intelligent mechanical structures, for example, anisotropically motorial bulk and timely proprio/exteroceptive sensing with programmable morphologies. Owing to abundant pores inside, porous media are promising to host various intelligent functions as interfaces/structures of robots yet challenging because of a limited anisotropic response inherited from a random hierarchical pore distribution. Here, an electron competition between Ga, N, and Pt is found and used to tune the polymerization of a gradient liquid alloy and NHHCO-suspended silicone precursor mixture and, thus, decompose gas movements in gradient pore formation under high-temperature heating (120 °C).