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Journal of Materials Science

, Volume 52, Issue 15, pp 8973–8985 | Cite as

Effect of processing parameters on textural and bioactive properties of sol–gel-derived borate glasses

  • William C. Lepry
  • Shiva Naseri
  • Showan N. NazhatEmail author
In Honor of Larry Hench

Abstract

A compositional range of recently developed bioactive sol–gel-derived borate glasses (SGBGs) have demonstrated remarkably rapid rates of conversion to hydroxy-carbonated apatite (HCA) in simulated body fluid (SBF). Although the composition of SGBGs did not greatly impact HCA conversion rates, it is still unknown how the sol–gel processing parameters affect the textural properties and thus bioactivity of the glass. In this study, a borate-substituted Bioglass® “45S5” formulation [(46.1)B2O3-(26.9)CaO-(24.4)Na2O-(2.6)P2O5; mol%] was fabricated using different sol–gel processing parameters including precursor materials, ageing time and temperature, along with calcination rate and temperature. It was found that a higher calcination temperature led to a partially crystallized glass with almost a magnitude decrease in specific surface area relative to the other glasses. All processing routes resulted in highly bioactive glasses according to Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy, which confirmed HCA formation in SBF in as little as 2 h. The majority of ion-exchange occurred within 30 min, facilitating this rapid conversion to bone-like HCA. Interestingly, the partially crystallized glasses (i.e., glass–ceramics) also underwent full conversion to HCA in SBF. Furthermore, ageing time and temperature did not affect the bioactive properties of these glasses, which allow for significantly reduced processing times. In summary, this study demonstrates that SGBGs can be tailored for targeted tissue engineering applications by varying the sol–gel processing parameters.

Keywords

Simulated Body Fluid Bioactive Glass Glass Powder Triethyl Phosphate Trimethyl Borate 
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.

Notes

Acknowledgements

This study was supported by Canada NSERC, CFI and McGill University Faculty of Engineering Hatch Faculty Fellowship for S.N.N. W.C.L., and S.N. are supported by the McGill Engineering Doctoral Award and W.C.L. is also supported by the Fonds de recherche du Québec (FQRNT)–Bourses de doctorat en recherché.

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • William C. Lepry
    • 1
  • Shiva Naseri
    • 1
  • Showan N. Nazhat
    • 1
    Email author
  1. 1.Department of Mining and Materials EngineeringMcGill UniversityMontrealCanada

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