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Journal of Materials Science

, Volume 52, Issue 15, pp 8793–8811 | Cite as

Effects of boron oxide substitution on the structure and bioactivity of SrO-containing bioactive glasses

  • Xiaonan Lu
  • Lu Deng
  • Po-Hsuen Kuo
  • Mengguo Ren
  • Ian Buterbaugh
  • Jincheng DuEmail author
In Honor of Larry Hench

Abstract

B2O3/SiO2 substitution in 55S4.3 bioactive glasses with 5 mol% of SrO has been synthesized and characterized to understand their structure and bioactivity as a function of composition by combining experimental and computer simulation techniques. Raman spectrometry, X-ray diffraction (XRD) and Fourier transform infrared spectrometry (FTIR) were utilized to characterize the structural changes induced by boron content and to identify the formation of hydroxyapatite (HAp). In vitro bioactivity tests were performed in simulated body fluid with a fixed glass mass to solution volume ratio and a particle size range. Needle-like HAp was found to form on the surface of the 55S4.3 with SrO sample from scanning electron microscopy and confirmed from XRD and FTIR. In addition to the experimental efforts, these glasses were also simulated using classical molecular dynamics simulations with partial charge potentials and recently developed parameters for boron oxide to understand their short- and medium-range structures. The glasses from simulations were analyzed in terms of the local structure around the glass network formers, especially the boron coordination number, and found to agree well with theoretical models. The medium-range structural information such as Q n distribution and network connectivity was also obtained and used to understand the compositional dependence of property and bioactivity. The results show that additional boron oxide increased the network connectivity of the 55S4.3 glass and inhibited or delayed the formation of HAp in vitro.

Keywords

Simulated Body Fluid Bioactive Glass Glass Powder Borate Glass Boron Oxide 
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

We gratefully acknowledge support by National Science Foundation (NSF) (project # 1508001). ESEM, XRD, Raman and FTIR experiments were conducted at the Center for Advanced Research and Technology (CART) at University of North Texas (UNT). Computer simulations were performed on UNT Talon 2 high-performance computer (HPC) cluster. We would also like to acknowledge Dr. Narendra Dahotre and Dr. Yee Hsien Ho for the helpful discussions. Lastly, we want to thank our anonymous reviewers for both insightful comments and suggestions.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Xiaonan Lu
    • 1
  • Lu Deng
    • 1
  • Po-Hsuen Kuo
    • 1
  • Mengguo Ren
    • 1
  • Ian Buterbaugh
    • 1
  • Jincheng Du
    • 1
    Email author
  1. 1.Department of Materials Science and EngineeringUniversity of North TexasDentonUSA

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