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Porosity prediction of calcium phosphate cements based on chemical composition

  • Caroline ÖhmanEmail author
  • Johanna Unosson
  • Elin Carlsson
  • Maria Pau Ginebra
  • Cecilia Persson
  • Håkan Engqvist
Biomaterials Synthesis and Characterization
Part of the following topical collections:
  1. Biomaterials Synthesis and Characterization

Abstract

The porosity of calcium phosphate cements has an impact on several important parameters, such as strength, resorbability and bioactivity. A model to predict the porosity for biomedical cements would hence be a useful tool. At the moment such a model only exists for Portland cements. The aim of this study was to develop and validate a first porosity prediction model for calcium phosphate cements. On the basis of chemical reaction, molar weight and density of components, a volume-based model was developed and validated using calcium phosphate cement as model material. 60 mol% β-tricalcium phosphate and 40 mol% monocalcium phosphate monohydrate were mixed with deionized water, at different liquid-to-powder ratios. Samples were set for 24 h at 37 °C and 100 % relative humidity. Thereafter, samples were dried either under vacuum at room temperature for 24 h or in air at 37 °C for 7 days. Porosity and phase composition were determined. It was found that the two drying protocols led to the formation of brushite and monetite, respectively. The model was found to predict well the experimental values and also data reported in the literature for apatite cements, as deduced from the small absolute average residual errors (<2.0 %). In conclusion, a theoretical model for porosity prediction was developed and validated for brushite, monetite and apatite cements. The model gives a good estimate of the final porosity and has the potential to be used as a porosity prediction tool in the biomedical cement field.

Keywords

Portland Cement Calcium Phosphate Cement DCPD Calcium Aluminate Cement Internal Porosity 
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

Acknowledgments

This work was partially supported by Stiftelsen Lars Hiertas minne (FO2011-0445), the Swedish Foundation for International Cooperation in Research and Higher Education (STINT, GA IG2011-2047), FP7 NMP Project Biodesign (GA 262948), the Swedish Research Council (VR, Project Number 621-2011-6258 and Project Number 2011-3399) and the Swedish Institute (SI, 00845/2011).

Conflict of interest

The authors declare that there are no conflict of interest.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Caroline Öhman
    • 1
    • 2
    Email author
  • Johanna Unosson
    • 1
  • Elin Carlsson
    • 1
  • Maria Pau Ginebra
    • 3
  • Cecilia Persson
    • 1
  • Håkan Engqvist
    • 1
  1. 1.Division of Applied Materials Science, Department of Engineering Sciences, The Ångström LaboratoryUppsala UniversityUppsalaSweden
  2. 2.Division of Applied Mechanics, Department of Engineering Sciences, The Ångström LaboratoryUppsala UniversityUppsalaSweden
  3. 3.Biomaterials, Biomechanics and Tissue Engineering Group, Department of Materials Science and MetallurgyTechnical University of CataloniaBarcelonaSpain

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