Makoto Kobashi

Aluminum (Al) powder with low sinterability is difficult to use in binder jetting (BJT) additive manufacturing, which involves sintering a metal powder after forming a green body. A liquid-phase sintering process for Al powder using Al–Cu eutectic alloy powder as a sintering aid has recently been developed. In this study, to clarify the applicability of liquid-phase sintering to BJT additive manufacturing, the effect of the initial relative density of green bodies (ρ_rel,0 = 50–90%) on the final relative density was investigated. The final relative density was not significantly affected by ρ_rel,0 and achieved 96–97% after sintering at 630 °C for 1800 s. However, pores are likely to remain in the sintered body with a high ρ_rel,0 of 90%. In situ observations using synchrotron radiation X-ray computed tomography revealed that large pores were formed at the early sintering stage of the green body with ρ_rel,0 of 90% and partially retained after sintering. By contrast, the green body with ρ_rel,0 of 50% exhibited a significant rearrangement at the early sintering stage, promoting the densification. This study provides a deep understanding of liquid-phase sintering of Al powder, which is considered a suitable post-processing method for BJT additive manufacturing.

Sinter-based additive manufacturing (AM) requires sintering for the densification of green bodies. Al powder is difficult to sinter due to the dense oxide film on the surface, and it is difficult to apply to sinter-based AM. Liquid phase sintering using Al-based eutectic alloy powder is promising for sintering Al powder without external pressure. In this study, Al powders with various oxide film thicknesses were sintered using Al-X eutectic alloy powders (X=Cu, Ca, and Mg) to clarify suitable alloy elements in the sintering aids for the liquid phase sintering. When an as-supplied Al powder with an oxide film thickness of approximately 2 nm (presumably amorphous Al₂O₃ film) was used, Al-Cu and Al-Ca aids promoted the densification, whereas numerous pores were observed in the sample sintered using Al-Mg aid. The pores would be formed during the cooling after sintering, along with the homogenization of Mg distribution. When Al powder with an oxide film thickness of around 4 nm was used, a high relative density of over 95% was maintained using Al-Cu aid, whereas the relative density of the sample sintered using Al-Ca aid significantly degraded, presumably due to the formation of Ca-based oxide. These results indicate that the Al-Cu eutectic alloy powder is a promising sintering aid for the liquid phase sintering of Al powder.

Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.