Structural pathway for nucleation and growth of topologically close-packed phase from parent hexagonal crystal

The solid diffusive phase transformation involving the nucleation and growth of one nucleus is universal and frequently employed but has not yet been fully understood at the atomic level. Here, our first-principles calculations reveal a structural formation pathway of a series of topologically close-packed (TCP) phases within the hexagonally close-packed (hcp) matrix. The results show that the nucleation follows a nonclassical nucleation process, and the whole structural transformation is completely accomplished by the shuffle-based displacements, with a specific 3-layer hcp-ordering as the basic structural transformation unit. The thickening of plate-like TCP phases relies on forming these hcp-orderings at their coherent TCP/matrix interface to nucleate ledge, but the ledge lacks the dislocation characteristics considered in the conventional view. Furthermore, the atomic structure of the critical nucleus for the Mg2Ca and MgZn2 Laves phases was predicted in terms of Classical Nucleation Theory (CNT), and the formation of polytypes and off-stoichiometry in TCP precipitates is found to be related to the nonclassical nucleation behavior. Based on the insights gained, we also employed high-throughput screening to explore several common hcp-metallic (including hcp-Mg, Ti, Zr, and Zn) systems that may undergo hcp-to-TCP phase transformations. These insights can deepen our understanding of solid diffusive transformations at the atomic level, and constitute a foundation for exploring other technologically important solid diffusive transformations

An intelligent decision support approach for quantified assessment of innovation ability via an improved BP neural network

In today's competitive and changing social environment, innovation and entrepreneurial ability have become important factors for the successful development of college students. However, relying solely on traditional evaluation methods and indicators cannot comprehensively and accurately evaluate the innovation and entrepreneurial potential and ability of college students. Therefore, developing a comprehensive evaluation model is urgently needed. To address this issue, this article introduces machine learning methods to explore the learning ability of subjective evaluation processes and proposes an intelligent decision support method for quantitatively evaluating innovation capabilities using an improved BP (Back Propagation) neural network. This article first introduces the current research status of evaluating the innovation and entrepreneurship ability of college students, and based on previous research, it has been found that inconsistent evaluation standards are one of the important issues at present. Then, based on different BP models and combined with the actual situation of college student innovation and entrepreneurship evaluation, we selected an appropriate input layer setting for the BP neural network and improved the setting of the middle layer (hidden layer). The identification of output nodes was also optimized by combining the current situation. Subsequently, the conversion function, initial value and threshold were determined. Finally, evaluation indicators were determined and an improved BP model was established which was validated using examples. The research results indicate that the improved BP neural network model has a low error rate, strong generalization ability and ideal prediction effect which can be effectively used to analyze problems related to intelligent evaluation of innovation ability

Fabrication of Nickel Nanostructure Arrays Via a Modified Nanosphere Lithography

In this paper, we present a modified nanosphere lithographic scheme that is based on the self-assembly and electroforming techniques. The scheme was demonstrated to fabricate a nickel template of ordered nanobowl arrays together with a nickel nanostructure array-patterned glass substrate. The hemispherical nanobowls exhibit uniform sizes and smooth interior surfaces, and the shallow nanobowls with a flat bottom on the glass substrate are interconnected as a net structure with uniform thickness. A multiphysics model based on the level set method (LSM) was built up to understand this fabricating process by tracking the interface between the growing nickel and the electrolyte. The fabricated nickel nanobowl template can be used as a mold of long lifetime in soft lithography due to the high strength of nickel. The nanostructure–patterned glass substrate can be used in optical and magnetic devices due to their shape effects. This fabrication scheme can also be extended to a wide range of metals and alloys

Thick photoresists for electroforming metallic microcomponents

microcomponent