A new strategy based on a microfluidic platform is proposed for the rapid and high-quality control of explosives' crystal morphology.

This study presents a strategy for the preparation of multiscale α phase by high/low-temperature two-step aging in metastable β titanium alloy, Ti-15Mo-3Al-2.7Nb-0.2Si. The purpose of high-temperature aging is to obtain a number of micro-scale secondary α phases (α_S), besides precipitation-free zones (PFZs). After that, in the second stage of low-temperature aging, the nano-scale tertiary α phase (α_T) precipitates in the PFZs. The second-step aging temperature and the time at low temperature will have an important effect on α_T. On the one hand, with the extension of aging time, the morphology of α_T changes from dot-like to needle-like. Secondly, the increase of the second-step aging temperature will promote precipitation of α_T in PFZs. The strength increases gradually with the accelerated precipitation of α_T, and good plasticity is still maintained. The effect of multiscale α on fracture behavior was investigated by in situ tensile testing. Results show that cracks form a transgranular fracture along the slip line after solution treatment. After first-step aging, the crack easily passes through α_S and forms an intergranular fracture. After second-step aging, the formation of multiscale α phase has a stronger effect in hindering the crack growth, which leads to crack propagation mainly by transgranular fracture. Overall, this study demonstrates that the alloy can be heat-treated to achieve an excellent match between strength and ductility, which is expected to meet the application requirements of structural materials of the new generation of aircraft.

The production of alloys with high strength and toughness concurrently is still a difficult challenge. Here, we designed a simple solution-ageing heat treatment system to control the morphology and density of α in Ti-15Mo-3Al-2.7Nb-0.2Si via different heat treatment temperatures. The experimental results show that Ti-15Mo-3Al-2.7Nb-0.2Si exhibits a synergistic combination of tensile strength (1364 MPa), plasticity (7.8% elongation), and fracture toughness (101 MPa·m^1/2) through solutions in the α/β biphasic region and duplex ageing. Notably, the strength of the alloy after the second step of the ageing process is increased by 15% compared with that after the first step of the ageing process. However, there is less than a 5% reduction in the fracture toughness. TEM observations show that the matrix continues to precipitate denser secondary α during duplex ageing, which causes the strength to increase significantly and causes the fracture toughness to weaken. Our work may provide a novel method to optimize the mechanical properties of alloys by controlling the precipitates.

In this paper, we explore the zero-shot capability of the Segment Anything Model (SAM) for food image segmentation. To address the lack of class-specific information in SAM-generated masks, we propose a novel framework, called FoodSAM. This innovative approach integrates the coarse semantic mask with SAM-generated masks to enhance semantic segmentation quality. Besides, we recognize that the ingredients in food can be supposed as independent individuals, which motivated us to perform instance segmentation on food images. Furthermore, FoodSAM extends its zero-shot capability to encompass panoptic segmentation by incorporating an object detector, which renders FoodSAM to effectively capture non-food object information. Drawing inspiration from the recent success of promptable segmentation, we also extend FoodSAM to promptable segmentation, supporting various prompt variants. Consequently, FoodSAM emerges as an all-encompassing solution capable of segmenting food items at multiple levels of granularity. Remarkably, this pioneering framework stands as the first-ever work to achieve instance, panoptic, and promptable segmentation on food images. Extensive experiments demonstrate the feasibility and impressing performance of FoodSAM, validating SAM's potential as a prominent and influential tool within the domain of food image segmentation. We release our code at https://github.com/jamesjg/FoodSAM.