Three-dimensional Expansion:In Suspension Culture of SD Rat's Osteoblasts in a Rotating Wall Vessel Bioreactor

Objective To study large-scale expansion of SD （Sprague-Dawley） rat＇s osteoblasts in suspension culture in a rotating wall vessel bioreactor （RWVB）. Methods The bioreactor rotation speeds were adjusted in the range of 0 to 20 rpm, which could provide low shear on the rnicrocarriers around 1 dyn/cm^2. The cells were isolated via sequential digestions of neonatal （less than 3 days old） SD rat calvaria. After the primary culture and several passages, the cells were seeded onto the microcarriers and cultivated in T-flask, spinner flask and RWVB respectively. During the culture period, the cells were counted and observed under the inverted microscope for morphology every 12 h. After 7 days, the cells were evaluated with scanning electron microscope （SEM） for histological examination of the aggregates. Also, the hematoxylin-eosin （HE） staining and alkaline phosphatase （ALP） staining were performed. Moreover, von-Kossa staining and Alizarin Red S staining were carded out for mineralized nodule formation. Results The results showed that in RWVB, the cells could be expanded by more than ten times and they presented better morphology and vitality and stronger ability to form bones. Conclusions The developed RWVB can provide the culture environment with a relatively low shear force and necessary three-dimensional （3D） interactions among cells and is suitable for osteopath expansion in vitro.

Machine learning in materials science

Traditional methods of discovering new materials,such as the empirical trial and error method and the density functional theory(DFT)-based method,are unable to keep pace with the development of materials science today due to their long development cycles,low efficiency,and high costs.Accordingly,due to its low computational cost and short development cycle,machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection,material analysis,and material design.In this article,we discuss the basic operational procedures in analyzing material properties via machine learning,summarize recent applications of machine learning algorithms to several mature fields in materials science,and discuss the improvements that are required for wide-ranging application.

Morphology enhancement of SiO_(2)aerogel films grown on Si substrate using dense SiO_(2)buffer layer

Porous SiO_(2)film has been widely studied due to its extensive applications in many fields.This paper presents a newly produced porous SiO_(2)film made by traditional sol-gel method.Bare Si and Si with SiO_(2)buffer layer were used as substrate.The SiO_(2)buffer layer was 500 nm in thickness and was grown by thermal oxidization.The structural properties of SiO_(2)aerogel films spincoated on both materials were observed by optical microscope(OM)and scanning electron microscope(SEM).Results reveal that the surface of SiO_(2)aerogel films on bare Si is rough and discontinuous.While flat and smooth surface is observed on sample with SiO_(2)buffer layer.This indicates that by inserting SiO_(2)buffer layer,the structural property of SiO_(2)aerogel film deposited on Si is improved.

Enhanced pyroelectric properties of PZT/PVDF-TrFE composites using calcined PZT ceramic powders

The effects of calcined lead--zirconate-titanate(PZT)powders on the electric properties of PZT/polyvinylidene-tifluorethylene copolymer(PVDF-TrFE)composites thick films were studied in this paper.Firstly,the PZT powders synthesized by hydrothermal method were calcined at different temperatures ranging from 300℃ to 900℃,and then the PZT/PVDF-TrFE composites films were produced by casting PZT/PVDF-TrFE suspension onto the indium-tin-oxide(ITO)-coated glass substrates.Electric properties,including dielectric and pyroelectric performances of thick films consisting of PZT powders calcined at different temperatures were tested.The highest pyroelectric cofficient obtained in the sample using 700℃ calcined PZT powders was 96μCm^(-2)K^(-1),which was 20%higher than the composites made of uncalcined powders.Additionally,the highest detectivity figure-of merit(FOM)(FD)of the composite was 1.36×10^(-5)Pa^(-1/2),which increased about 13.5%compared to the one using uncalcined powders.