Sprayable copper and copper-zinc nanowires inks for antiviral surface coating.

Copper alloys are known for their high antimicrobial efficacy. Retrofitting high-touch surfaces in public space with solid copper components is expensive and often impractical. Directly coating copper onto these high-touch surfaces can be achieved with hot or cold spray, but the procedure is complicated and requires special equipment. This article reports on the development of sprayable copper and copper-zinc nanowire inks for antiviral surface coating applications. Our results show that copper nanowires inactivate the SARS-CoV-2 virus faster than bulk copper. And a trace amount of zinc addition has a significant effect in enhancing the virucidal effect. More importantly, these nanowire inks are sprayable. They can be easily applied on high-touch surfaces with a spray can. When combined with common chemical disinfectants, the copper-based nanowire ink spray may prolong the disinfecting effect well after application.

Machine learning assisted prediction of the Young's modulus of compositionally complex alloys.

We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young's modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young's modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.

Recycling of additively printed rare-earth bonded magnets.

In this work, we describe an efficient and environmentally benign method of recycling of additive printed Nd-Fe-B polymer bonded magnets. Rapid pulverization of bonded magnets into composite powder containing Nd-Fe-B particles and polymer binder was achieved by milling at cryogenic temperatures. The recycled bonded magnets fabricated by warm compaction of ground cryomilled coarse composite powders and nylon particles showed improved magnetic properties and density. Remanent magnetization and saturation magnetization increased by 4% and 6.5% respectively, due to enhanced density while coercivity and energy product were retained from the original additive printed bonded magnets. This study presents a facile method that enables the direct reuse of end-of-life bonded magnets for remaking new bonded magnets. In addition to magnetic properties, mechanical properties comparable to commercial products have been achieved. This research advances efforts to ensure sustainability in critical materials by forming close loop supply chain.