Using ternary steric hindrance synergy of a defective MoS2 monolayer to manipulate the electrocatalytic mechanism toward nitric oxide reduction: a first-principles and machine learning study

Abstract

Designing the performance of multi-atom embellished catalysts is an indispensable prerequisite for accelerating the electrochemical nitric oxide reduction reaction (NORR). Common electronic restructuring strategies (doping, alloying, and steric hindrance) are known to have significant effects on the tuning of catalytic activity, but the effect of these strategies on manipulating by which mechanism a reaction proceeds remains unexplored. Herein, by combining density functional theory and machine learning methods, we have successfully identified a promising catalyst candidate, Fe@MoS₂, which not only has fine thermodynamic stability, but also strong resistance to electrochemical corrosion under local alloying conditions. More importantly, competing complex electroreduction reaction pathways are guided in the expected direction by taking advantage of the innate steric hindrance of the defective MoS₂. To the best of our knowledge, this work may be the first to propose an action that does not require chemical contact to improve the Faraday efficiency of the product by means of controlling the fundamental mechanism, rather than just focusing on the selectivity of competitive responses.