| 1 | Influences of Reaction Parameters and Ce Contents on Structure and Properties of Nano-scale Ce-HA Powders显示文摘Ce-incorporated apatite(Ce-HA) nano-scale particles with different Ce percentage contents(atomic ratio of Ce to Ce + Ca is 5%,10%and 20%,respectively) were synthesized via a simple wet chemical method in this study.The crystal structure,chemical groups,thermal stability,crystal morphologies and crystal sizes of the Ce-HA nano-particles were characterized by X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR),and transmission electron microscopy(TEM).The influences of reaction temperature,reaction time,pH value,and the atomic ratio of Ce to Ce + Ca on the structure and performance of Ce-HA particles were studied.The results show that the lattice constants,particle sizes,crystallinity and thermal stability of Ce-HA vary with the doped Ce contents.With the increase of Ce content,the lattice constants of the Ce-HA nano-particles remarkably increase but the particle size,crystallinity and thermal stability gradually decrease.The reaction temperature as well as the reaction time has no significant effect on the properties of the final products,while the pH value has a direct relationship with their final chemical composition.The obtained Ce-HA nanosize particles possess potential application in preparing artificial bone implants,bone tissue engineering scaffold and other bioactive coatings. | L.J.Sun D.G.Guo W.A.Zhao L.Y.Wang K.W.Xu | 2014 | Journal of Materials Science & Technology2014，30，8： | 2 |
| 2 | A NEURAL NETWORK-BASED MODEL FOR PREDICTION OF HOT-ROLLED AUSTENITE GRAIN SIZE AND FLOW STRESS IN MICROALLOY STEEL显示文摘For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection of hot-rolling control parameters was studied for microalloy steel by following the neural network principle. An experimental scheme was first worked out for acquisition of sample data, in which a gleeble-1500 thermal simolator was used to obtain rolling temperature, strain, stain rate, and stress-strain curves. And consequently the aust enite grain sizes was obtained through microscopic observation. The experimental data was then processed through regression. By using the training network of BP algorithm, the mapping relationship between the hotrooling control parameters (rolling temperature, stain, and strain rate) and the microstructural paramete rs (austenite grain in size and flow stress) of microalloy steel was function appro ached for the establishment of a neural network-based model of the austeuite grain size and flow stress of microalloy steel. From the results of estimation made with the neural network based model, the hot-rolling control parameters can be effectively predicted. | J. T.Niu,L.J.Sun and P.Karjalainen 1) Harbin Institute of Technology, Harbin 150001, China 2) University of Oulu, FIN-90571, Oulu, Finland | 2000 | Acta Metallurgica Sinica(English Letters)2000，13，2： | 0 |
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