Effect of Zr modification on solidification behavior and mechanical properties of Mg–Y–RE (WE54) alloy

AbstractMagnesium alloys containing rare earth elements (RE) have received considerable attention in recent years due to their high mechanical strength and good heat-resisting performance. Among them, Mg–5%Y–4%RE (WE54) magnesium alloy is a high strength sand casting magnesium alloy for use at temperatures up to 300 °C, which is of great interest to engineers in the aerospace industry. In the present work, the solidification behavior of Zr-containing WE54 alloy and Zr-free alloy was investigated by computer-aided cooling curve analysis (CA-CCA) technique. And the solidification microstructure and mechanical properties of them were also investigated comparatively. It is found from the cooling curves and as-cast microstructure of WE54 alloy that the nucleation temperature of α-Mg in WE54 alloy increases after Zr addition, and the as-cast microstructure of the alloy is significantly refined by Zr. While the phase constitution of WE54 alloy is not changed after Zr addition. These phenomena indicate that Zr acts as heterogeneous nuclei during the solidification of WE54 alloy. Due to refined microstructure, the mechanical properties of Zr-containing WE54 alloy is much higher than Zr-free WE54 alloy

KG2Vec: A node2vec-based vectorization model for knowledge graph.

Since the word2vec model was proposed, many researchers have vectorized the data in the research field based on it. In the field of social network, the Node2Vec model improved on the basis of word2vec can vectorize nodes and edges in social networks, so as to carry out relevant research on social networks, such as link prediction, and community division. However, social network is a network with homogeneous structure. When dealing with heterogeneous networks such as knowledge graph, Node2Vec will lead to inaccurate prediction and unreasonable vector quantization data. Specifically, in the Node2Vec model, the walk strategy for homogeneous networks is not suitable for heterogeneous networks, because the latter has distinguishing features for nodes and edges. In this paper, a Heterogeneous Network vector representation method is proposed based on random walks and Node2Vec, called KG2vec (Heterogeneous Network to Vector) that solves problems related to the inadequate consideration of the full-text semantics and the contextual relations that are encountered by the traditional vector representation of the knowledge graph. First, the knowledge graph is reconstructed and a new random walk strategy is applied. Then, two training models and optimizing strategies are proposed, so that the contextual environment between entities and relations is obtained, semantically providing a full vector representation of the Heterogeneous Network. The experimental results show that the KG2VEC model solves the problem of insufficient context consideration and unsatisfactory results of one-to-many relationship in the vectorization process of the traditional knowledge graph. Our experiments show that KG2vec achieves better performance with higher accuracy than traditional methods

Atomistic insights into the droplet size evolution during self-microemulsification

Microemulsions have been attracting great attention for their importance in various fields including nanomaterials fabrication, food industry, drug delivery, and enhanced oil recovery. Atomistic insights into the self-microemulsifying process and the underlying mechanisms are crucial for the design and tuning of the size of microemulsion droplets towards applications. In this work, coarse-grained models were used to investigate the role of droplet sizes played in the preliminary self-microemulsifying process. Time evolution of liquid mixtures consisting of several hundreds of water/surfactant/oil droplets was resolved in large-scale simulations. By monitoring the size variation of the microemulsion droplets in the self-microemulsifying process, the dynamics of diameter distribution of water/surfactant/oil droplets were studied. The underlying mass transport mechanisms responsible for droplet size evolution and stability were elucidated. Specifically, temperature effects on the droplet size were clarified. This work supplies the knowledge of the self-microemulsifying of the water-in-oil microemulsions at the nanoscale. The results are expected to serve as guidelines for practical strategies for preparing a microemulsion system with desirable droplet sizes and properties