The Intelligent Properties of Micro-reactors for Preparating Nanoparticles

TiO2 nanoparticles were synthesized by using micro-reactors. The shape and size of the nanoparticles produced from the original micro-reactors and the five times recycled micro-reactors mother liquor were investigated on transmission electron microscopy (TEM) by using the original sample, freeze prepared sample, and dyeing treated sample, respectively. UV-VIS spectrometry was used to study the growth process of TiO2 nanoparticles in main reactors. The results showed that micro-reactors with nanometer magnitude had spherical or oval structures, and could restore to their original structure after they were destroyed. The products prepared in the original micro-reactors were similar to that in the micro-reactors recycled for many times, suggesting that the micro-reactors had memory function.

Fast determination of meso-level mechanical parameters of PFC models

To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.

Effect of CF and RPP on the Mechanical and Electrical Properties of Smart Aggregate

By using redispersible polymer powder（RPP） and carbon fiber（CF） to adjust the flexibility and electrical properties of the smart aggregate, a new kind of smart aggregate with Z type structure was proposed. The study shows that Z type aggregate is more sensitive to the feedback of external force than the prism aggregate in the same loading environment, and it indicates that Z type aggregate is more suitable for the research and application of concrete health monitoring. Although the incorporation of RPP would cause the compressive strength of the aggregates and the elastic modulus of hardened cement mortar to reduce slightly within the dosage of RPP by 2.25% because of the polymer film formed in the internal system, this would improve the deformability of the aggregates. In the early loading stage（in the first 60 seconds）, the intelligent concrete specimens implanted with Z type smart aggregate do not show higher sensitivity as expected, although the resistance change rate changes a little bit more, the overall of it is still in balance. Adding RPP could improve the flexibility of smart aggregates exactly, and it plays an active role in prolonging the life of the smart aggregates. By implanting Z type aggregates the damage and failure of the concrete structure could be predicted accurately in this study. The results of this paper will help to promote further research and application of intelligent concrete.