As a typical metal-organic framework (MOF), Mg-MOF74 can release biocompatible Mg2+ when the framework is degraded, and it has the potential to be used as filler in the field of bone tissue engineering (BTE). However, Mg-MOF74 has poor stability in aqueous environment and limited antibacterial ability, which limit its further development and applications. In this work，MgCu-MOF74 particles with different Cu content were synthesized through a facile one-step hydrothermal method. The physicochemical properties and water stability of the synthesized powders were characterized. The osteogenic potential of the MgCu-MOF74 particles on human osteogenic sarcoma cells (SaOS-2) was evaluated. The hybrid MgCu-MOF74 exhibited favorable water stability. These results indicated that MgCu-MOF74 enhanced cellular viability, alkaline phosphatase (ALP) levels, collagen (COL) synthesis and osteogenesis-related gene expression. Moreover, the samples doped with Cu2+ were more sensitive to the acidic microenvironment produced by bacteria, and exhibited stronger antibacterial ability than Mg-MOF74. In conclusion, MgCu-MOF-74 with good water stability, osteogenic ability and antibacterial ability, which could be attributed to the doping of Cu2+. Hence, MgCu-MOF74 shows great potential as a novel medical bio-functional fillers for the treatment of bone defects.

Ever-increasing demands for superior alloys with improved hightemperature service properties require accurate design of their composition. However, conventional approaches to screen the properties of alloys such as creep resistance and microstructural stability cost a lot of time and resources. This work therefore proposes a novel high throughput-based design strategy for high-temperature alloys to accelerate their composition selections, by taking Ni-based superalloys as an example. A numerical inverse method is used to massively calculate the multielement diffusion coefficients based on an accurate atomic mobility database. These coefficients are subsequently employed to refine the physical models for tuning the creep rates and structural stability of alloys, followed by unsupervised machine learning to categorize their composition and determine the range of the composition with optimal performance. By using a strict screening criterion, two sets of composition with comprehensively optimal properties are selected, which is then validated by experiments. Compared with recent data-driven methods for materials design, this strategy exhibits high accuracy and efficiency attributed to the high-throughput multicomponent diffusion couples, self-developed atomic mobility database, and refined physical models. Since this strategy is independent of the alloy composition, it can efficiently accelerate the development of multicomponent high-performance alloys and tackle challenges in discovering novel materials.

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