Up-grading of natural ilmenite ore by combining oxidation and acid leaching

Rutile （TiO2） is heavily used in pigments mid colormlts, mid the most abundmlt source of rutile is ilmeinte. Upon oxidation of ilmeinte, mtile cml be formed wiffl modest energy consumption; furfflermore, after leaching, only a few byproducts are formed. Unfortunate- ly, one drawback is file necessarily long oxidative process of typically used mefflods. In fflis study, we show fflat a fluidized bed reactor cml be used to oxidize ilmeinte ore to rapidly form ruffle mid pseudobrookite （Fe2TiOs） phases. Ilmeinte was oxidized wiffl 5vo1% 02 in Ar at temperatures of 1173 K or 1223 K mid subsequently leached using a diluted H2SO4 solution to dissolve file pseudobrookite phase. The effects of acid concentration, temperature, mid cooling rate after oxidation were investigated. We show fflat file ilmeinte was rapidly oxidized to form rutile mid pseudobrookite phases at 1173 mid 1223 K in a 5vo1% O2/95vo1% Ar environment wifflin 40 min. The fmal maximum mtile yield was 84.2mo1% after leaching in （1 ＋ 1） H2SO4 solution at 393 K for 12 h.

Comment on: Long-term hemodialysis improved and stabilized diabetic macular edema: two case reports

Dear Editor,With great excitement, we read the article titled “Longterm hemodialysis improved and stabilized diabetic macular edema: two case reports” by Fan and Yuan[1];a case series study which provided interesting novel insights into the beneficial effect of hemodialysis(HD) on diabetic macular edema(DME).

Automated crystal structure analysis based on blackbox optimisation

In the present study,we show that time-consuming manual tuning of parameters in the Rietveld method,one of the most frequently used crystal structure analysis methods in materials science,can be automated by considering the entire trial-and-error process as a blackbox optimisation problem.The automation is successfully achieved using Bayesian optimisation,which outperforms both a human expert and an expert-system type automation despite the absence of expertise.This approach stabilises the analysis quality by eliminating human-origin variance and bias,and can be applied to various analysis methods in other areas which also suffer from similar tiresome and unsystematic manual tuning of extrinsic parameters and human-origin variance and bias.

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade.One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments,which has given rise to the need for accelerated and accurate automated estimation of the properties of materials.In this regard,spectroscopic data are widely used for materials discovery because these data include essential information about materials.An important requirement for the realisation of the automated estimation of materials parameters is the selection of a similarity measure,or kernel function.The required measure should be robust in terms of peak shifting,peak broadening,and noise.However,the determination of appropriate similarity measures for spectra and the automated estimation of materials parameters from these spectra currently remain unresolved.We examined major similarity measures to evaluate the similarity of both X-ray absorption and electron energy-loss spectra.The similarity measures show good correspondence with the materials parameter,that is,the crystal-field parameter,in all measures.The Pearson's correlation coefficient was the highest for the robustness against noise and peak broadening.We obtained the regression model for the crystal-field parameter 10 Dq from the similarity of the spectra.The regression model enabled the materials parameter,that is,10 Dq,to be automatically estimated from the spectra.With regard to research progress in similarity measures,this methodology would make it possible to extract the materials parameter from a large-scale dataset of experimental data.