Manchester Metropolitan University's Research Repository

    Lightweight Self-Forming Super-Elastic Mechanical Metamaterials with Adaptive Stiffness

    Diver, Carl ORCID logoORCID: https://orcid.org/0000-0002-8743-1182, Wu, Rui, Soutis, Constantinos, Roberts, Peter, Zhou, Dekai, Li, Longqiu and Deng, Zongquan (2021) Lightweight Self-Forming Super-Elastic Mechanical Metamaterials with Adaptive Stiffness. Advanced Functional Materials, 31 (6). p. 2008252. ISSN 1616-301X

    Accepted Version
    Download (7MB) | Preview


    Scarcity of stiff, yet compliant materials is a major obstacle toward biological-like mechanical systems that perform precise manipulations while being resilient under excessive load. We introduce a macroscopic cellular structure comprising of two pre-stressed elastic “phases”, which displays a load-sensitive stiffness that drops by 30 times upon a “pseudo-ductile transformation” and accommodates a fully-recoverable compression of over 60%. This provides an exceptional 20 times more deform-ability beyond the linear-elastic regime, doubling the capability of previously reported super-elastic materials. In virtue of the pre-stressing process based on thermal-shrinkage, it simultaneously enables a heat-activated self-formation that transforms a flat laminate into the metamaterial with 50 times volumetric growth. The metamaterial is thereby inherently lightweight with a bulk density in the order of 0.01 g cm−3, which is one order of magnitude lower than existing super-elastic materials. Besides the highly-programmable geometrical and mechanical characteristics, this paper is the first to present a method that generates single-crystal or poly-crystal-like 3D lattices with anisotropic or isotropic super-elasticity. This pre-stress-induced adaptive stiffness with high deform-ability could be a step toward in-situ deployed ultra-lightweight mechanical systems with a diverse range of applications that benefit from being stiff and compliant.

    Impact and Reach


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics for this dataset are available via IRStats2.


    Repository staff only

    Edit record Edit record