Nano-particle hydroxyapatite （ HA ） was prepared with a novel ultrasonic precipitation process and the as-prepared nanopowder was used to produce hydroxyapatite （ HA ） coatings on titanium substrate via plasma spr...Nano-particle hydroxyapatite （ HA ） was prepared with a novel ultrasonic precipitation process and the as-prepared nanopowder was used to produce hydroxyapatite （ HA ） coatings on titanium substrate via plasma spraying. The phase and the microstructare of the coating were characterized by X- ray diffraction （XRD） and scanning electron microscopy （SEM）. Results showed that spherical particles could be prepared by ultrasonic precipitation process; and a corresponding dense HA coating with molten surface and low-porosity cross-section structure was acquired. During the plasma spraying process, new phases of Ca3 （ PO4 ）2 and Ca2 P2O7 were generated. After heat-treating at 800℃ for 1 h, the contents of Ca3 （ PO4 ）2 and Ca2 P2O7 decreased while HA content increased. Tensile adhesion tests showed that the plasma sprayed coating prepared with the spherical nanoparticles exhibited high tensile bond strength.展开更多
The ultrasonic precipitation technique for preparing hydroxyapatite nanoparticles is a complex process that was strongly influenced by temperature, reaction time and ultrasonic power. The use of a modified artificial ...The ultrasonic precipitation technique for preparing hydroxyapatite nanoparticles is a complex process that was strongly influenced by temperature, reaction time and ultrasonic power. The use of a modified artificial neural network （ANN） was proposed to model the non-linear relationship between ultrasonic precipitation parameters and the hydroxyapatite content. The improved model for processing dataset and selecting its topology was developed using the Levenberg-Marquardt training algorithm and was trained with comprehensive dataset of hydroxyapatite nanoparticles collected from experimental data. A basic repository on the domain knowledge of ultrasonic precipitation process for the preparation of hydroxyapatite is established via sufficient data mining by the network. With the help of the repository stored in the trained network, the influence of preparation temperature, preparation time and ultrasonic sonicating power on the hydroxyapatite content can be analyzed and predicted. The results show that the ANN system is effective and successful in analyzing the influence of ultrasonic precipitation parameters on the preparation of hydroxyapatite nanoparticles.展开更多
文摘Nano-particle hydroxyapatite （ HA ） was prepared with a novel ultrasonic precipitation process and the as-prepared nanopowder was used to produce hydroxyapatite （ HA ） coatings on titanium substrate via plasma spraying. The phase and the microstructare of the coating were characterized by X- ray diffraction （XRD） and scanning electron microscopy （SEM）. Results showed that spherical particles could be prepared by ultrasonic precipitation process; and a corresponding dense HA coating with molten surface and low-porosity cross-section structure was acquired. During the plasma spraying process, new phases of Ca3 （ PO4 ）2 and Ca2 P2O7 were generated. After heat-treating at 800℃ for 1 h, the contents of Ca3 （ PO4 ）2 and Ca2 P2O7 decreased while HA content increased. Tensile adhesion tests showed that the plasma sprayed coating prepared with the spherical nanoparticles exhibited high tensile bond strength.
文摘The ultrasonic precipitation technique for preparing hydroxyapatite nanoparticles is a complex process that was strongly influenced by temperature, reaction time and ultrasonic power. The use of a modified artificial neural network （ANN） was proposed to model the non-linear relationship between ultrasonic precipitation parameters and the hydroxyapatite content. The improved model for processing dataset and selecting its topology was developed using the Levenberg-Marquardt training algorithm and was trained with comprehensive dataset of hydroxyapatite nanoparticles collected from experimental data. A basic repository on the domain knowledge of ultrasonic precipitation process for the preparation of hydroxyapatite is established via sufficient data mining by the network. With the help of the repository stored in the trained network, the influence of preparation temperature, preparation time and ultrasonic sonicating power on the hydroxyapatite content can be analyzed and predicted. The results show that the ANN system is effective and successful in analyzing the influence of ultrasonic precipitation parameters on the preparation of hydroxyapatite nanoparticles.
