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Freeze casting of hydroxyapatite scaffolds for bone tissue engineering

Open access
Deville, Sylvain, Saiz, Eduardo, Tomsia, Antoni P.·2006·Biomaterials

Abstract

Although extensive efforts have been put into the development of porous scaffolds for bone regeneration, with encouraging results, all porous materials have a common limitation: the inherent lack of strength associated with porosity. Hence, the development of porous hydroxyapatite scaffolds has been hindered to non-load bearing applications. We report here how freeze casting can be applied to synthesize porous scaffolds exhibiting unusually high compressive strength, e.g. up to 145 MPa for 47% porosity and 65 MPa for 56% porosity. The materials are characterized by well-defined pore connectivity along with directional and completely open porosity. Various parameters affecting the porosity and compressive strength have been investigated, including initial slurry concentration, freezing rate, and sintering conditions. The implications and potential application as bone substitute are discussed. These results might open the way for hydroxyapatite-based materials designed for load-bearing applications. The biological response of these materials is yet to be tested.

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Metadata

Authors
Deville, S., Saiz, E., & Tomsia, A. P.
Venue
Biomaterials
Publisher
Elsevier BV
Published
2006-11
Volume
27
Issue
32
Pages
5480-5489
ISSN
0142-9612 (ISSN-L)
OpenAlex
W2036978569
Cited by
873
References
50
FWCI
22.18
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Freeze casting of hydroxyapatite scaffolds for bone tissue engineering. https://app.cochat.ai/papers/10.1016/j.biomaterials.2006.06.028
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Deville, S., Saiz, E., & Tomsia, A. P. (2006). Freeze casting of hydroxyapatite scaffolds for bone tissue engineering. Biomaterials. https://doi.org/10.1016/j.biomaterials.2006.06.028

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.