Melt Electrowriting of Complex 3D Anatomically Relevant Scaffolds

Navid T. Saidy(University of Queensland), Tara Shabab(Queensland University of Technology), Onur Bas(Queensland University of Technology), Diana M. Rojas‐González(Technical University of Munich), Matthias Menne(RWTH Aachen University), Tim Henry(Queensland University of Technology), Dietmar W. Hutmacher(Institute for Advanced Study), Petra Mela(RWTH Aachen University), Elena M. De‐Juan‐Pardo(Queen Elizabeth II Medical Centre)
Frontiers in Bioengineering and Biotechnology
July 24, 2020
Cited by 116Open Access
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Abstract

The manufacture of fibrous scaffolds with tailored micrometric features and anatomically relevant three-dimensional (3D) geometries for soft tissue engineering applications remains a great challenge. Melt electrowriting (MEW) is an advanced additive manufacturing technique capable of depositing predefined micrometric fibers. However, it has been so far inherently limited to simple planar and tubular scaffold geometries because of the need to avoid polymer jet instabilities. In this work, we surmount the technical boundaries of MEW to enable the manufacture of complex fibrous scaffolds with simultaneous controlled micrometric and patient-specific anatomic features. As an example of complex geometry, aortic root scaffolds featuring the sinuses of Valsalva were realized. By modeling the electric field strength associated with the MEW process for these constructs, we found that the combination of a conductive core mandrel with a non-conductive 3D printed model reproducing the complex geometry minimized the variability of the electric field thus enabling the accurate deposition of fibers. We validated these findings experimentally and leveraged the micrometric resolution of MEW to fabricate unprecedented fibrous aortic root scaffolds with anatomically relevant shapes and biomimetic microstructures and mechanical properties. Furthermore, we demonstrated the fabrication of patient-specific aortic root constructs from the 3D reconstruction of computed tomography clinical data.


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