Our work "Realizing negative Poisson's ratio in spring networks with close-packed lattice geometries" was published in Physical Review Materials, July 2019. Congratulations to the authors!

Work by Jun Liu, Yunhuan Nie, Hua Tong, and Ning Xu*

Abstract

Previously, auxetic materials were realized by designing building blocks with specific geometries or tuning topology of spring networks. Here, we propose a new approach by properly manipulating the disorder, especially the bond stiffness disorder, of an unstressed spring network. When randomly distorting a spring network initially with a close-packed lattice structure, we find that the resultant unstressed network becomes auxetic. Resulted from the distortion, longer bonds are likely to contribute more to the shear modulus but less to the bulk modulus. Inspired by this correlation, we realize isotropic auxeticity in the unstressed triangular lattice, by distributing each bond a spring constant according to a virtual lattice distortion. In the perspective of previous approaches, however, it is not straightforward to figure out how the close-packed lattices can be turned into auxetic independent of directions. The key of our approach is the correlated disorder induced by the (virtual) lattice distortion, which leads to a sufficiently large nonaffine contribution responsible to the auxeticity.

Link

https://doi.org/10.1103/PhysRevMaterials.3.055607

Cite this article

Jun Liu, Yunhuan Nie, Hua Tong, and Ning Xu, Realizing negative Poisson's ratio in spring networks with close-packed lattice geometries. Physical Review Materials 3, 055607 (2019).