Journal of Materials Science

, Volume 47, Issue 23, pp 8035–8043 | Cite as

The distribution of carbonate in enamel and its correlation with structure and mechanical properties

  • Changqi Xu
  • Rachel Reed
  • Jeffrey P. Gorski
  • Yong Wang
  • Mary P. WalkerEmail author


The correlation of carbonate content with enamel microstructure (chemical and crystal structure) and mechanical properties was evaluated via linear mapping analyses by Raman microspectroscopy and nanoindentation. Mappings started at the outer enamel surface and ended in the inner enamel near the dentin-enamel junction (DEJ) in lingual and buccal cervical and cuspal regions. The carbonate peak intensity at 1070 cm−1 gradually increased from outer to inner enamel. Moreover, the phosphate peak width, as measured by the full width at half maximum of the peak at 960 cm−1, also increased, going from ~9 cm−1 in outer enamel to ~13 cm−1 in enamel adjacent to the DEJ, indicating a decrease in the degree of crystallinity of hydroxyapatite from outer to inner enamel. In contrast, Young’s modulus decreased from 119 ± 12 to 80 ± 19 GPa across outer to inner enamel with a concomitant decrease in enamel hardness from 5.9 ± 1.4 to 3.5 ± 1.3 GPa. There were also significant correlations between carbonate content and associated crystallinity with mechanical properties. As carbonate content increased, there was an associated decrease in crystallinity and both of these changes correlated with decreased modulus and hardness. Collectively, these results suggest that enamel carbonate content and the associated change in the crystal structure of hydroxyapatite, i.e., degree of crystallinity, may have a direct effect on enamel mechanical properties. The combination of Raman microspectroscopy and nanoindentation proved to be an effective approach for evaluating the microstructure of enamel and its associated properties.


Raman Microspectroscopy Outer Enamel Lingual Cusp Tooth Section Enamel Prism 
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.



This investigation was supported by the USPHS research grant DE021462 from the National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA. We also want to thank Dr. Ying Liu for her assistance with the statistical analyses.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Changqi Xu
    • 1
  • Rachel Reed
    • 1
  • Jeffrey P. Gorski
    • 1
  • Yong Wang
    • 1
  • Mary P. Walker
    • 1
    Email author
  1. 1.Department of Oral BiologyUniversity of Missouri-Kansas City School of DentistryKansas CityUSA

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