Solidification pathways and hot tearing susceptibility of MgZn_xY_4Zr_(0.5) alloys

Hot tearing is known as one of the most serious solidification defects commonly encountered during solidification. It is very important to study the solidification path of alloys. In the work, thermal analysis with cooling curve was used for the investigation of microstructure evolution with different Zn contents during solidification process of MgZn_xY_4Zr_(0.5) alloys. Thermal analysis results of MgY_4Zr_(0.5) alloys revealed one distinct phase precipitation: α-Mg. Three different phase peaks were detected in the Zn-containing alloys: α-Mg, Z-phase(Mg_(12)YZn) and W-phase(Mg_3 Y_2Zn_3). In addition, for the present MgZn_xY_4Zr_(0.5) alloys, the freezing ranges of these alloys from large to small were: MgZn_(1.5)Y_4Zr_(0.5)>MgZn)(3.0) Y)4Zr_(0.5)>MgZn0.5 Y4 Zr0.5>MgY_4Zr_(0.5). The effect of different contents of Zn(0, 0.5, 1.5, 3.0 wt.%) on hot tearing behavior of MgY_4Zr_(0.5) alloy was investigated using a constrained rod casting(CRC) apparatus equipped with a load cell and data acquisition system. The experimental results show that the addition of Zn element significantly increases hot tearing susceptibility(HTS) of the MgY_4Zr_(0.5) alloy due to its extended freezing range. Some free dendrite-like bumps and ruptured liquid films on the fracture surfaces were observed in all the fracture surfaces. These phenomena proved the fact that the hot tearing formation was caused by interdendritic separation due to lack of feeding at the end of solidification.

Hot tearing susceptibility of Mg-xZn-2Y alloys

Effects of Zn content (0, 0.5%, 1.5% and 4.5%) on the hot tearing characteristics of Mg?2%Y alloy were studied in aconstrained rod casting (CRC) apparatus attached with a load cell and data acquisition system. The experimental results indicate thatthe hot tearing susceptibility (HTS) is affected by the content of Zn. The Zn-free base alloy shows the lowest HTS. The HTS ofMg?xZn?2Y alloys increases with increasing Zn content, reaches the maximum at 1.5% Zn, and then decreases with further Znaddition. The high HTS observed in the alloy with 1.5% Zn is attributed to its high force release rate and large force drop duringsolidification. The hot cracks of casting are initiated and propagate along the dendritic or grain boundaries. The predictions of HTS ofMg?xZn?2Y alloys using ProCAST software are in good agreement with the results obtained by experimental measurements.

Hot tearing behaviors and in-situ thermal analysis of Mg-7Zn-xCu-0.6Zr alloys

Thermal analysis was used to investigate the microstructural evolution of Mg-7 Zn-x Cu-0.6 Zr alloys during solidification. The effect of Cu content（0, 1, 2 and 3, mass fraction, %） on the hot tearing behavior of the Mg-7 Zn-x Cu-0.6 Zr alloys was investigated with a constrained rod casting（CRC） apparatus, equipped with a load sensor and a data acquisition system. The thermal analysis results of Mg-7 Zn-x Cu-0.6 Zr alloy revealed that the alloy consisted of two distinct phases： α-Mg and Mg Zn2. Three distinct peaks were observed in the alloys with Cu addition, which were identified as α-Mg, Mg Zn Cu and Mg Zn2. In addition, the reaction temperature of α-Mg decreased and the reaction temperatures of Mg Zn2 and Mg Zn Cu increased as the Cu content increased. The experimental results of hot tearing demonstrated that the addition of Cu significantly reduced the hot tearing susceptibility（HTS） of Mg-7 Zn-x Cu-0.6 Zr alloys due to the higher eutectic temperature and the shorter solidification temperature region.

Prediction of Multicomponent Reaction Yields Using Machine Learning

Prediction of reaction yields using machine learning(ML)can help chemists select high-yielding reactions and provide prior experience before wet-lab experimenting to improve efficiency.However,the exploration of a multicomponent organic reaction features many complex variables and limited number of experimental data,which are challenging for the application of ML.Herein,we perform yield prediction for the synthesis of 2-oxazolidones via Cu-catalyzed radical-type oxy-alkylation of allylamines and herteroaryl-methylamines with CO_(2),which is a three-component reaction.Using physicochemical descriptors as features to launch ML modelling,we find that XGBoost shows significantly improved performance over linear models and these features are effective for the yield prediction.Moreover,out-of-sample prediction indicates the application potential of the model.This study demonstrates great potential of regression-modelling-based ML in organic synthesis even with complex factors and a general small size of reaction data,which are generated from the classical research pattern of method for the inquiry of multicomponent reactions.