The influence of various sintering atmospheres (vacuum, CO₂ and N₂) on the transparency of alumina ceramics was investigated using the two-step pulsed electric current sintering (TS-PECS). Samples sintered under a CO₂ atmosphere exhibited lower transparency with a darker coloration due to carbon contamination resulting from CO₂ gas during TS-PECS. In contrast, sample darkening was reduced by switching from CO₂ to vacuum during the TS-PECS process. Switching between CO₂ and vacuum atmospheres suppressed carbon deposition caused by CO gas generated from the CO₂ and graphite die/sheet. Samples sintered under an N₂ atmosphere exhibited lower transparency and density compared to those sintered in vacuum. This is due to the slow diffusion of N₂ in alumina, which leads to N₂ retention in closed pores and impedes densification. Notably, higher transparency was observed in samples sintered by switching the atmosphere from vacuum to N₂ during the TS-PECS process compared to those sintered solely under vacuum. Optimizing gas and atmosphere profiles during PECS is essential for achieving the desirable transparency and density of alumina ceramics.

Using a raw material solution in which nano-sized magnetite particles were dispersed in a silica solution, we synthesized γ -Fe₂O₃/SiO₂ composite fine particles by the spray pyrolysis method. These fine particles consisted of primary particles of about 60 nm in size in which γ -Fe₂O₃ of about 10 nm was dispersed in a SiO₂ matrix, and these primary particles aggregated to form secondary particles of about 1 μm in size. These fine particles exhibited superparamagnetism and had greater magnetic response than commercially available products. In addition, the zeta potential of these fine particles was -30 to -25 mV, the surface was negatively charged, and they had a stronger tendency to adsorb the basic protein such as cytochrome C than the acidic protein such as albumin.

Due to the complex and nonlinear correlations between microstructures and mechanical properties in dual phase alloys, it is difficult to estimate their strength by conventional methods. This study attempts to model the relationship between microstructure and mechanical properties in sintered and hot-rolled α + β titanium-iron (Ti-Fe) alloys using machine learning. In the preparation of the models, 4-9 microstructural factors were investigated to identify the most important predictors of mechanical properties. A Random Forest (RF) model was found to have best predictive power, producing a good match with experimental data in samples which were outside of the training dataset. Moreover, the average α grain diameters, β phase widths (by intercept method), and β phase area fractions were found to be the strongest predictors of mechanical behavior.