Doping or loading: unraveling the optimal anchoring strategy of single metal atom on Co3O4 for electrochemical nitrate reduction reaction

Abstract

Developing efficient electrocatalysts for the nitrate reduction reaction (NIRR) to ammonia is vital for environmental remediation and sustainable ammonia synthesis. Metal-oxide-based single-atom catalysts (SACs) offer atomic-scale efficiency, yet unclear anchoring strategies for single metal sites hinder their rational design. This study systematically explored the effects of surface-loading and lattice-doping strategies on anchoring transition, rare-earth, and main-group metal atoms onto Co₃O₄ via the synergy of machine learning and density functional theory calculations. Through a comprehensive assessment of stability, catalytic activity, and electronic structures, it is discovered that lattice-doping enhances SACs stability by firmly anchoring metal atoms on Co sites, while surface-loading significantly boosts catalytic activity for the NIRR. Calculations predicted that Al, Ir, Rh, and Mo sites anchored through the surface-loading strategy exhibited exceptional NIRR activity (the limiting potential for Al site can reaches −0.25 V versus the reversible hydrogen electrode), far surpassing many other configurations. To further decipher the underlying mechanisms, the machine learning algorithms, especially the tree-based pipeline optimization tool model, revealed that SACs activity is highly correlated with the local environment of the active center, particularly its electronic and structural characteristics. This work establishes a new design paradigm for SACs, providing both theoretical guidelines for anchoring strategy selection and a predictive framework for efficient NIRR electrocatalysts.