The operation of nuclear power plants, nuclear waste depositories, and the decontamination and decommissioning of nuclear power plants all have the possibility of generating various kinds of radionuclides that can be formed as gaseous or liquid phases. Among the radionuclides, strontium is considered as most harmful substance due to its abundance in nuclear accident effluent, long half-life, high fission yield, high water solubility, and high mobility in aquatic environment. To remove strontium from aquatic environment, adsorption technique is mainly used with high economic feasibility, efficiency, and selectivity. Previously, we synthesized sodium titanates with mid-temperature hydrothermal method as selective strontium adsorbent in aqueous solution. Moreover, it was demonstrated that synthesized sodium titanates show high strontium adsorption rate with high selectivity with high surface area, pore diameter and volume. Herein, we investigated the surface structure of synthesized sodium titanates before and after strontium adsorption in aqueous solution using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS) analysis. According to SEM and EDS experimental results, aquatic strontium can be adsorbed as surface precipitation with formation of cube-shaped structure, which is quite similar strontium titanate structure crystals onto the surface of sodium titanates. In addition, XPS experimental results revealed that the titanium ions on the surface of sodium titanates were oxidized during strontium surface precipitation process, and the sodium ion on the surface of sodium titanates were exchanged with aquatic strontium ions via ion exchange process during strontium adsorption process.

The properties of powder metallurgy products are related to their densities. In the present work, we demonstrate a method to apply artificial neural networks (ANNs) trained on experimental data to predict the bulk density of barium titanates. The density is modeled as a function of pressure, press rate, heating rate, sintering temperature, and soaking time using the ANN method. The model predictions with the training and testing data result in a high coefficient of correlation (R2 = 0.95 and Pearson’s r = 0.97) and low average error. Moreover, a graphical user interface for the model is developed on the basis of the transformed weights of the optimally trained model. It facilitates the prediction of an infinite combination of process parameters with reasonable accuracy. Sensitivity analysis performed on the ANN model aids the identification of the impact of process parameters on the density of barium titanates.

[ BaO·Nd2O3·5TiO2 ] (BNT) ceramics modified with a borate glass containing Ba, Nd and Ti as glass constituents were investigated with regard to their sintering behavior and microwave dielectric properties. An addition of iso-component glass significantly improved the sinterabilty of the BNT ceramics and lowered the sintering temperature. A maximum density of 5.29 g/cm3 and an x-y shrinkage of 17% were obtained for BNT ceramics containing 10wt.% of the glass sintered at 1100˚C. The dielectric composition without the glass additive was only slightly densified at 1100˚C. The resulting sample exhibited two crystalline phases, BaNd2Ti5O14 and Ba2Ti9O20, regardless of sintering temperature and glass content. When >10wt.% glass was added, exaggerated grain growth with a less uniform microstructure was found, resulting in the subsequent reduction of the fired density and the dielectric properties. BNT ceramics containing 10wt.% of the isocomponent glass sintered at 1100˚C for 4 h showed promising dielectric properties of k = 71.3 and Q = 1,330.