Alginate Cryogels as a Template for the Preparation of Edible Oleogels.

A high consumption of solid fats is linked to increased inflammation and a risk of cardiovascular diseases. Hence, in recent years, there has been increasing interest in the development of oleogels as a fat substitute in food products. Oleogels are edible gels that contain a large amount of liquid oils entrapped in a 3D network and that can potentially be applied to spreads, bakery goods, meat, and dairy products in order to lower their saturated fat content while maintaining a desirable food texture and mouthfeel. In this work, alginate cryogels were studied as templates for three different edible oils in the process of oleogel formation. Two different freezing regimes to obtain cryogels were employed in order to evaluate better the textural and morphological capabilities of cryogels to adsorb and retain edible oils. It was shown that rapid freezing in liquid nitrogen produces alginate cryogels with a lower density, higher porosity, and a greater ability to adsorb the tested oils. The highest uptake and holding oil capacity was achieved for olive oil, which reached a value of 792% and 82%, respectively. The best chewiness was found for an oleogel containing olive oil, whereas oleogels with the other two tested oils showed better springiness. Hence, the results presented in this work demonstrated that alginate-based cryogels can be effectively used as templates for oleogels and potentially find applications in the food industry.

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.