Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning.

Titanium alloys have been present for decades as the main components for the production of various orthopedic and dental elements. However, modern times require titanium alloys with a low Young's modulus, and without the presence of cytotoxic alloying elements. Machine learning was used with aim to analyze biocompatible titanium alloys and predict the composition of Ti alloys with a low Young's modulus. A database was created using experimental data for alloy composition, Young's modulus, and mechanical and thermal properties of biocompatible titanium alloys. The Extra Tree Regression model was built to predict the Young's modulus of titanium alloys. By processing data of 246 alloys, the specific heat was discovered to be the most influential parameter that contributes to the lowering of the Young's modulus of titanium alloys. Further, the Monte Carlo method was used to predict the composition of future alloys with the desired properties. Simulation results of ten million samples, with predefined conditions for obtaining titanium alloys with a Young's modulus lower than 70 GPa, show that it is possible to obtain several multicomponent alloys, consisting of five main elements: titanium, zirconium, tin, manganese and niobium.

Breaking of double emulsions based on electrohydrodynamics principles.

This research focuses on the modeling of the liquid-liquid dispersed system, including particular insight on the electrocoalescence (EC) process that occurs during the breaking of double emulsions. The representative system, used in this work, was taken from the pilot plant for solvent extraction of uranium from wet phosphoric acid. The chosen framework required for elucidation of the EC process is based on the electrohydrodynamic (EHD) principles. During the model development it was necessary to consider several theoretical concepts for easier understanding and description of the related events. The first is the concept of entities, and corresponding classification of finely dispersed systems. The second concept is an introduction of almost forgotten basic electrodynamics element the memdiode or memristor as a current controlled device, and corresponding memristive systems. Hence, the conclusions that may be withdrawn from the presented results and findings could enable easier designing of the solutions for a breaking of double emulsions problems, that is, the entrainment problems that may arise in some pilot or industrial plants. Finally, the perspectives and the remaining challenges, considering the here discussed concepts and model based on the EHD principles, are mentioned.