3D printable bone-mimicking functionally gradient stochastic scaffolds for tissue engineering and bone implant applications

A number of designs have been used in the field of tissue engineering for the purposes of bone biomimi-cry (e.g.) triply periodic minimal surfaces (TPMS), honeycomb lattice, and face centered cubic (FCC). While the usage of these structures was met with some success, the results were not optimal due to fac-tors such as lack of localized porosity control and stress concentration at nodes of gradient scaffolds. This led to a push for novel designs, such as, stochastic scaffolds with locally tunable porosity as well as anisotropically varying mechanical properties. A number of methods exist for generating these stochastic structures, such as, random set method, stochastic optimization method, iterative non-linear transforma-tive method and others, but while these techniques led to a greater approximation of the bone microstructure, they did not help in the creation of a structure with controlled modulus along different directions. The novelty of the method used in this paper is that it not only helps in generating stochastic structures in a computationally inexpensive and non-complex way, but also generates a porosity gradient along three different axes in a controlled manner. Various additive manufacturing techniques have been successfully utilized for fabricating these structures at different scales, using different materials, thus, proving their versatility. The proposed design approach was implemented in the case of a 3D recon-structed rabbit model. Magics software was used to extract the internal microstructure of the rabbit model and our design approach was used for generating a stochastic scaffold which accurately mimicked the porosity gradient of the rabbit model in all the three directions. On comparing the mechanical prop-erties of the rabbit microstructure and the gradient mimicking scaffold, the variations were significant as the properties depend not only on the porosity gradient but also on the internal microstructure, which warrants further multi-property optimization.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).