Non-precious metal catalysts for electrocatalytic reduction of nitrate to ammonia

Abstract

The electrocatalytic reduction of nitrate to ammonia (NRA) presents a sustainable approach for ammonia production, yet the design of high-performance catalysts remains a formidable challenge. This review explores how the integration of density functional theory (DFT), machine learning (ML), and in situ characterization can accelerate the development of non-precious catalyst for NRA. By synergizing real-time experimental data, first-principles calculations, and data-driven models, these techniques establish a robust framework for unraveling complex structure–activity relationships and enhancing catalyst performance. ML facilitates rapid screening and prediction, while DFT offers detailed atomic-level insights into reaction mechanisms. in situ characterization, on the other hand, provides dynamic understanding of catalyst behavior under actual reaction conditions. The combination of these methods not only enables more rational catalyst design but also deepens our comprehension of the underlying core processes. This review summarizes recent advancements, discusses ongoing challenges, and outlines future directions for leveraging these integrated approaches to achieve efficient and scalable electrocatalytic nitrate-to-ammonia reduction using non-precious catalysts.