Adaptive Mechanical Metamaterials with On‐Demand Binary Local Modulus for Embodied Intelligence

作者：Richard Nash, Yunzheng Yang, Yaning Li · 发表于：Advanced Science · 年份：2025 · DOI：10.1002/advs.202509675 · 被引用次数：2 · 研究领域：Advanced Materials and Mechanics、Modular Robots and Swarm Intelligence、Cephalopods and Marine Biology

Biological materials in nature are inherently adaptive, evolving through continuous interaction with their environment. Achieving such adaptability and self-optimization in artificial materials remains a major challenge. In this work, a simple yet robust mechanism is introduced that enables instantaneous changes in local stiffness components in response to strain. This is realized by designing binary meta-capsules with two discrete states 0 and 1, each corresponding to a different modulus in one direction. These strain-responsive capsules switch states based on applied deformation, serving as the building blocks for a new class of adaptive mechanical metamaterials (AMMs). Computational tools are developed to guide the design, and selected structures are fabricated via multi-material polymer jetting. Mechanical experiments, including compression and indentation tests, confirm the functionality of the AMMs. Because the stiffness change in each meta-capsule is reversible, the material can reconfigure itself after loading-unloading cycle. This enables AMMs to dynamically adjust their local properties based on external loads and/or constraints, effectively "reprogramming" or redesigning themselves post-fabrication, paving the way for transforming 3D/4D printing into adaptive, "infinity-D" printing.