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Large columnar grains with epitaxial growth are commonly obtained in the additive manufacturing of metals, resulting in anisotropic mechanical properties and even hot-tearing cracks. Here, we demonstrate a new approach via remelting-introduced columnar-to-equiaxed transformation to promote the formation of fully-equiaxed microstructures in the additive manufacturing of Ni32Co30Cr10Fe10Al18 eutectic high-entropy alloy. Our present work displays promising isotropic mechanical properties with a superior combination of strength and ductility, compared to conventional as-cast and other as-printed eutectic high-entropy alloys. This novel finding could be applicable to the additive manufacturing of other eutectic systems for equiaxed microstructure control and performance optimization.

Background: Myosin heavy chain 9 (MYH9) plays essential role in human diseases including multiple cancers, however, little is known about the mechanism of MYH9 in gliomas. Methods: The expression of mRNA and protein was detected by clinical samples. The role of HMGA1 and MYH9 on gliomas was identified by cell functional and chemo sensitivity assay. Transcriptional regulation was confirmed by dual luciferase reporter and chromatin immunoprecipitation. Coimmunoprecipitation assay was used to verify the protein interaction and the ubiquitination. Findings: HMGA1 and MYH9 were upregulated in gliomas and correlated with WHO grade, and HMGA1 could promote malignant phenotypes and chemo resistance of glioma cells by regulating the expression of MYH9 through c-Jun-mediated transcription. Moreover, MYH9 interacted with GSK-3β and inhibited the expression of GSK-3β protein by promoting its ubiquitination, and downregulated GSK-3β could subsequently promote the nuclear translocation of β-catenin which could enhance the growth, invasion and migration, as well as temozolomide-resistance in glioma cells. Further survival analysis suggested that HMGA1 and MYH9 expression significantly correlated with patient’s survival and could be considered as independent prognostic factors in gliomas. Interpretation: Our findings provide new insights into the role of HMGA1 and MYH9 in the development of gliomas and implicate the potential application of HMGA1 and MYH9 in cancer therapy in the future. Funding: This study was supported by the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province and the President Fund of Nanfang Hospital. Declaration of Interest: None to declare. Ethical Approval: This study obtained approval from the Ethics Committees of Nanfang Hospital for Nationalities were obtained.

Precipitation-hardened high entropy alloys (HEAs) with carefully tuned compositions have shown excellent mechanical properties, demonstrating great potential for engineering applications. However, due to the lack of precise multiple phase diagrams, the composition design of multi-principal-component HEAs still inevitably relies on the extremely time-consuming trial-and-error approach. The present study, on the basis of powerful composition quantification ability of atom probe tomography (APT) technology, proposed a framework to guide the quantitative deign of precipitation-hardened HEAs. In this framework, the elemental partitioning was used as a crucial route to avoid the thermodynamic challenge of designing precipitation-hardened HEAs. As a case study, the role of Ti/Al ratio in the design of γ-γ′ HEAs was predicted through the proposed framework and then validated by experimental studies. The framework predicted that when the total content of Ti and Al is fixed a higher Ti/Al ratio makes a γ-γ′ HEA stronger. APT and mechanical results agreed well with these predictions and validated the feasibility of the framework. These findings provided a new route to design the precipitation-hardened alloys and a deeper insight into the design of γ-γ′ HEA.

The multicomponent L12 phase in high entropy alloys (HEAs) has shown excellent thermal stability, but understandings on its intrinsic origins are limited to the Lifshitz-Slyozov-Wagner (LSW) theories. In the present study, we investigated the influence of lattice misfit, which is not considered in LSW theory, on the coarsening kinetics of the multicomponent L12 phase. Different from those in most traditional alloys, the coarsening constants of multicomponent L12 particles decrease with the increasing lattice misfit in HEAs. This finding provides a new view for understanding the sluggish coarsening kinetics of L12 phase in HEAs.

Microstructure quantification is one of the core procedures in building process-structure-property linkages to accelerate the design and development of new materials. The approach of the two-point statistics technique combined with principal component analysis (TST-PCA) has emerged as an efficient and unbiased method for quantitatively capturing low-dimensional features of microstructures. However, when encountering a dataset with non-uniform microstructures, the traditional metric for evaluation can't precisely quantify the representativeness of low-dimensional features in terms of replacing the original microstructures, which makes it difficult in determining the proper number of reserved low-dimensional features. The information loss after data dimension reduction may be so great for partial members in the dataset that the low-dimensional features could not represent the original microstructures. To address this challenge, a new quantitative metric linked to the amount of structural information of microstructures captured by low-dimensional features was first designed. Next, a clustering optimization framework for the quantification of non-uniform microstructures based on TST-PCA was proposed. Based on the invariant moment method, a comparison between original and reconstructed microstructures was completed, which proves that the new metric is reasonable to measure the reliability of the improved microstructure quantification framework. Finally, The representative low-dimensional features of experimental microstructures were successfully obtained when the new framework was applied to an unbalanced dataset. This computationally convenient framework was envisioned to be applicable to most microstructure datasets.

Eutectics in high entropy alloys (HEAs) have shown excellent properties and promising applications. With empirical rules, various of eutectic high entropy alloys (EHEAs) have been proposed. The current design strategies shed light on the formation of eutectics in HEAs, but they are incapable of confirming multiple variables quantitatively in the selection of a specific system. In the present study, the eutectic formation in the multi-principal element systems is uncovered via data mining with machine learning (ML), where the critical elements and strongly associated elements were discovered. Taking the Al-Co-Cr-Fe-Ni system as an example, Al is confirmed to be the critical element for the eutectic formation and Cr is the strongly associated element with Al. Ni, Co, Fe and minor additions with comparably large solid solubility can be considered overall. With these understandings, a three-step approach can be summarized for designing EHEAs in a given system. Within the designed EHEAs, properties can be tested for optimization of application orientated design. The findings can not only accelerate the exploitation of EHEAs with better performance but also provide new ideas for designing compositionally complex alloys.