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Get Free AccessBone continuously adapts to its mechanical environment by structural reorganization to maintain mechanical strength. As the adaptive capabilities of bone are portrayed in its nano- and microstructure, the existence of dark and bright osteons with contrasting preferential collagen fiber orientation (longitudinal and oblique-angled, respectively) points at a required tissue heterogeneity that contributes to the excellent fracture resistance mechanisms in bone. Dark and bright osteons provide an exceptional opportunity to deepen our understanding of how nanoscale tissue properties influence and guide fracture mechanisms at larger length scales. To this end, a comprehensive structural, compositional, and mechanical assessment is performed using circularly polarized light microscopy, synchrotron nanocomputed tomography, focused ion beam/scanning electron microscopy, quantitative backscattered electron imaging, Fourier transform infrared spectroscopy, and nanoindentation testing. To predict how the mechanical behavior of osteons is affected by shifts in collagen fiber orientation, finite element models are generated. Fundamental disparities between both osteon types are observed: dark osteons are characterized by a higher degree of mineralization along with a higher ratio of inorganic to organic matrix components that lead to higher stiffness and the ability to resist plastic deformation under compression. On the contrary, bright osteons contain a higher fraction of collagen and provide enhanced ductility and energy dissipation due to lower stiffness and hardness.
Kilian E. Stockhausen, Mahan Qwamizadeh, Eva M. Wölfel, Haniyeh Hemmatian, Imke A.K. Fiedler, Silja Flenner, Elena Longo, Michael Amling, Imke Greving, Robert O. Ritchie, Felix N. Schmidt, Björn Busse (2021). Collagen Fiber Orientation Is Coupled with Specific Nano-Compositional Patterns in <i>Dark</i> and <i>Bright</i> Osteons Modulating Their Biomechanical Properties. ACS Nano, 15(1), pp. 455-467, DOI: 10.1021/acsnano.0c04786.
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Type
Article
Year
2021
Authors
12
Datasets
0
Total Files
0
Language
English
Journal
ACS Nano
DOI
10.1021/acsnano.0c04786
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