This study investigates the interfacial reaction between powder-metallurgy high-entropy alloys (HEAs) and cast aluminum. HEA pellets are produced by the spark plasma sintering of Al_0.5CoCrCu_0.5FeNi HEA powder. These sintered pellets are then placed in molten Al, and the phases formed at the interface between the HEA pellets and cast Al are analyzed. First, Kirkendall voids are observed due to the difference in the diffusion rates between the liquid Al and solid HEA phases. In addition, although Co, Fe, and Ni atoms, which have low mixing enthalpies with Al, diffuse toward Al, Cu atoms, which have a high mixing enthalpy with Al, tend to form Al–Cu intermetallic compounds. These results provide guidelines for designing Al matrix composites containing high-entropy phases.

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• Simultaneous enhancement of strength and ductility of Al matrix composites enabled by submicron-sized high-entropy alloy phases
  Chahee Jung, Seungin Nam, Hansol Son, Juyeon Han, Jaewon Jeong, Hyokyung Sung, Hyoung Seop Kim, Seok Su Sohn, Hyunjoo Choi
  Journal of Materials Research and Technology.2024; 33: 1470.     CrossRef

Multi-walled carbon nanotube (MWCNT)–copper (Cu) composites are successfully fabricated by a combination of a binder-free wet mixing and spark plasma sintering (SPS) process. The SPS is performed under various conditions to investigate optimized processing conditions for minimizing the structural defects of CNTs and densifying the MWCNT–Cu composites. The electrical conductivities of MWCNT–Cu composites are slightly increased for compositions containing up to 1 vol.% CNT and remain above the value for sintered Cu up to 2 vol.% CNT. Uniformly dispersed CNTs in the Cu matrix with clean interfaces between the treated MWCNT and Cu leading to effective electrical transfer from the treated MWCNT to the Cu is believed to be the origin of the improved electrical conductivity of the treated MWCNT–Cu composites. The results indicate the possibility of exploiting CNTs as a contributing reinforcement phase for improving the electrical conductivity and mechanical properties in the Cu matrix composites.

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• Proposing Machine Learning Models Suitable for Predicting Open Data Utilization
  Junyoung Jeong, Keuntae Cho
  Sustainability.2024; 16(14): 5880.     CrossRef