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Rheological Characterization of Biomaterials Directs Additive Manufacturing of Strontium‐Substituted Bioactive Glass/Polycaprolactone Microfibers
Author(s) -
Paxton Naomi C.,
Ren Jiongyu,
Ainsworth Madison J.,
Solanki Anu K.,
Jones Julian R.,
Allenby Mark C.,
Stevens Molly M.,
Woodruff Maria A.
Publication year - 2019
Publication title -
macromolecular rapid communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.348
H-Index - 154
eISSN - 1521-3927
pISSN - 1022-1336
DOI - 10.1002/marc.201900019
Subject(s) - polycaprolactone , microfiber , materials science , rheology , biomaterial , composite number , bioactive glass , extrusion , characterization (materials science) , composite material , tissue engineering , polymer , chemical engineering , biomedical engineering , nanotechnology , medicine , engineering
Additive manufacturing via melt electrowriting (MEW) can create ordered microfiber scaffolds relevant for bone tissue engineering; however, there remain limitations in the adoption of new printing materials, especially in MEW of biomaterials. For example, while promising composite formulations of polycaprolactone with strontium‐substituted bioactive glass have been processed into large or disordered fibres, from what is known, biologically‐relevant concentrations (>10 wt%) have never been printed into ordered microfibers using MEW. In this study, rheological characterization is used in combination with a predictive mathematical model to optimize biomaterial formulations and MEW conditions required to extrude various PCL and PCL/SrBG biomaterials to create ordered scaffolds. Previously, MEW printing of PCL/SrBG composites with 33 wt% glass required unachievable extrusion pressures. The composite formulation is modified using an evaporable solvent to reduce viscosity 100‐fold to fall within the predicted MEW pressure, temperature, and voltage tolerances, which enabled printing. This study reports the first fabrication of reproducible, ordered high‐content bioactive glass microfiber scaffolds by applying predictive modeling.