Unveiling the potential of palladium-based materials in electrocatalysis

Palladium-based materials are widely recognized for their adaptability in electrocatalytic applications, offering finely tunable electronic properties, surface structures, and strong resistance to intermediate poisoning. Their unique ability to stabilize key reaction intermediates enables outstanding catalytic activity and selectivity across a range of electrochemical processes central to sustainable energy technologies and resource utilization. This review comprehensively explores the advancements in Pd-based catalysts, emphasizing strategies to optimize their performance through alloying, nanostructuring, phase engineering, and surface modifications. A wide range of synthesis techniques, including wet chemical methods, electrodeposition, and templating approaches, has enabled precise control over Pd morphology, composition, and electronic properties, leading to breakthroughs in catalytic efficiency, durability, and cost-effectiveness. Pd-based catalysts have demonstrated outstanding performance across a range of electrocatalytic reactions, including hydrogen evolution (HER), oxygen evolution (OER), oxygen reduction (ORR), hydrogen oxidation (HOR), and formic acid oxidation (FAO) in water-splitting and fuel cell systems, as well as CO2reduction (CO2RR), nitrogen reduction (NRR), and nitrate reduction (NO3RR) for sustainable fuel and chemical production. The interplay of structural and electronic tuning has allowed Pd-based materials to drive key electrochemical reactions with enhanced stability, selectivity, and mass activity. Despite these advancements, long-term stability, cost, and scalability challenges remain, necessitating further research into alternative Pd-based hybrid materials and novel design strategies. This review provides an in-depth analysis of the progress in Pd-based catalysts, highlighting their potential to drive future innovations in clean energy technologies. © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.