Journal of Materials Science

, Volume 49, Issue 6, pp 2496–2508 | Cite as

Mechanical characterization of hollow ceramic nanolattices

  • Lucas R. MezaEmail author
  • Julia R. Greer


In the analysis of complex, hierarchical structural meta-materials, it is critical to understand the mechanical behavior at each level of hierarchy in order to understand the bulk material response. We report the fabrication and mechanical deformation of hierarchical hollow tube lattice structures with features ranging from 10 nm to 100 μm, hereby referred to as nanolattices. Titanium nitride (TiN) nanolattices were fabricated using a combination of two-photon lithography, direct laser writing, and atomic layer deposition. The structure was composed of a series of tessellated regular octahedra attached at their vertices. In situ uniaxial compression experiments performed in combination with finite element analysis on individual unit cells revealed that the TiN was able to withstand tensile stresses of 1.75 GPa under monotonic loading and of up to 1.7 GPa under cyclic loading without failure. During the compression of the unit cell, the beams bifurcated via lateral-torsional buckling, which gave rise to a hyperelastic behavior in the load–displacement data. During the compression of the full nanolattice, the structure collapsed catastrophically at a high strength and modulus that agreed well with classical cellular solid scaling laws given the low relative density of 1.36 %. We discuss the compressive behavior and mechanical analysis of the unit cell of these hollow TiN nanolattices in the context of finite element analysis in combination with classical buckling laws, and the behavior of the full structure in the context of classical scaling laws of cellular solids coupled with enhanced nanoscale material properties.


Central Node Atomic Layer Deposition Titanium Nitride Constituent Material Tensile Yield Strength 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors gratefully acknowledge the financial support from the Dow-Resnick Innovation Fund at Caltech, the Office of Naval Research (Grant N000140910883) and the Army Research Office through the Institute for Collaborative Biotechnologies (ICB) at Caltech (ARO Award number UCSB.ICB4b). Part of this work was carried out at the Jet Propulsion Laboratory under a contract with NASA. The authors acknowledge critical support and infrastructure provided by the Kavli Nanoscience Institute at Caltech. The authors thank Dongchan Jang for his help with nanomechanical experiments. The authors also thank Frank Greer for his help in the ALD deposition of the TiN films.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Division of Engineering and Applied ScienceCalifornia Institute of TechnologyPasadenaUSA
  2. 2.Kavli Nanoscience InstituteCalifornia Institute of TechnologyPasadenaUSA

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