Mathematical analysis of shrinkage In HA+ZrO2 composites
Agata Dudek
Quarterly No. 1, 2009 pages 58-61
DOI:
keywords: biomaterials, composites, hydroxyapatite
abstract Calcium hydroxyapatite is the main mineral component of bones, with the size of its crystals of 450 nm. Hydroxyapatite constitutes a kind of ‘warehouse’ for storage of calcium (99%) and phosphorus (85%), while its crystals account for 77% of organic stroma the bones are made of. Nowadays doctors, in their practice, often come across the problem of bone defects while bone graft is found to be a standard procedure. Nowadays, great emphasis is put on searching for new bone-replacing materials which would allow for replacement of bone defects that appear for a variety of reasons. Materials for implants must not be hazardous for human body, i.e. carcinogenic, toxic or radioactive, they have to be resistant to corrosion (depending on the environment they work within), biocompatible (the materials which show tissue compatibility and do not cause allergic reactions), well tolerated by living tissues. It is important, from the standpoint of general application of hydroxyapatites and HA-based composites (with addition of ZrO2 phase) for medicine (dentistry, maxillofacial surgery, orthopaedics or otolaryngology) to determine accurate percentage content of ZrO2 phase addition in the mixture that ensures maintaining of invariable or previously predicted dimensions of an implant or a coating after the process of sintering. The composites for the investigations were prepared by means of axial compaction of two ceramic powders (HA+ZrO2), which were then sintered at the temperature of 1300oC for two hours. The investigations aimed to obtain mathematical description that enables estimation of volume shrinkage in HAp + ZrO2 sinters after the process of sintering. In order to obtain a generalized dependence between contents of zirconium phase and shrinkage in the obtained sinters, regression and correlation concepts were employed. For the obtained experimental points several linear regression equations were matched using least squares method and correlation coefficient was calculated.