Chemical catalysts are quietly working behind the scenes to speed up essential reactions that drive innovation and production across a wide range of industries. Of these catalysts, multicomponent intermetallics are particularly valued for their capacity to power green technologies like clean-energy systems.
Multicomponent intermetallics are high-entropy alloys (HEAs)—mixtures of metallic elements specifically combined to form distinct crystalline structures—often used as catalysts in fuel cells and electrolysers that generate electricity from clean sources such as water.
However, scientists have not yet deciphered the optimal arrangement of the metallic elements to maximise their catalytic activity for industrial use. “As a result, the durability of some HEAs, such as those involving platinum, has been a long-standing challenge, affecting their large-scale application,” said Na Gong, a Scientist at A*STAR’s Institute of Materials Research and Engineering (IMRE).
Gong’s team, in partnership with Yong Wang from Nanyang Technological University, Singapore, proposed that having more organised and specific arrangements of the metals within multicomponent intermetallics can ultimately bolster their stability.
The researchers tested their theory using multicomponent intermetallic nanoparticles containing platinum, iron, cobalt, copper and nickel with varying degrees of ordering. “The ordering process is driven by atomic diffusion, a phenomenon where atoms move and rearrange themselves,” Gong explained.
“By carefully controlling the temperature and the duration of heating, we were able to fine-tune the degree of orderliness in the alloy.”
They analysed their nanoparticle structures and electrocatalytic efficiencies to discover that their approach paid off—the team identified a highly ordered HEA structure that significantly outperformed commercial catalysts in enhancing the oxygen reduction and hydrogen evolution reactions.
Critically, the study revealed a clear link between the structure (highly ordered stacking of the individual metal layers) and the resulting electrocatalytic efficiency, providing fresh insights for future catalyst design.
Gong and colleagues hope that these findings will pave new inroads into the development of sustainable energy systems through cost-effective and durable catalysts for fuel cells and electrolysers.
“To further our research, we are now investigating the application of machine learning in expediting the exploration of alternative high-entropy intermetallics,” concluded Gong.
The A*STAR-affiliated researchers contributing to this research are from the Institute of Materials Research and Engineering (IMRE).
