Dynamic diagnosis of metamaterials through laser-induced vibrational signatures

Yun Kai, Somayajulu Dhulipala, Rachel Sun, Jet Lem, Washington DeLima, Thomas Pezeril () and Carlos M. Portela ()
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Yun Kai: Massachusetts Institute of Technology
Somayajulu Dhulipala: Massachusetts Institute of Technology
Rachel Sun: Massachusetts Institute of Technology
Jet Lem: Massachusetts Institute of Technology
Washington DeLima: Department of Energy
Thomas Pezeril: Massachusetts Institute of Technology
Carlos M. Portela: Massachusetts Institute of Technology

Nature, 2023, vol. 623, issue 7987, 514-521

Abstract: Abstract Mechanical metamaterials at the microscale exhibit exotic static properties owing to their engineered building blocks1–4, but their dynamic properties have remained substantially less explored. Their design principles can target frequency-dependent properties5–7 and resilience under high-strain-rate deformation8,9, making them versatile materials for applications in lightweight impact resistance10–12, acoustic waveguiding7,13 or vibration damping14,15. However, accessing dynamic properties at small scales has remained a challenge owing to low-throughput and destructive characterization8,16,17 or lack of existing testing protocols. Here we demonstrate a high-throughput, non-contact framework that uses MHz-wave-propagation signatures within a metamaterial to non-destructively extract dynamic linear properties, omnidirectional elastic information, damping properties and defect quantification. Using rod-like tessellations of microscopic metamaterials, we report up to 94% direction-dependent and rate-dependent dynamic stiffening at strain rates approaching 102 s−1, as well as damping properties three times higher than their constituent materials. We also show that frequency shifts in the vibrational response allow for characterization of invisible defects within the metamaterials and that selective probing allows for the construction of experimental elastic surfaces, which were previously only possible computationally. Our work provides a route for accelerated data-driven discovery of materials and microdevices for dynamic applications such as protective structures, medical ultrasound or vibration isolation.

Date: 2023
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DOI: 10.1038/s41586-023-06652-x

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