Atomistic modelling of N2 reduction reaction on bulk/nano based Fe-electrocatalysts

Abstract

The electrocatalysis plays a pivotal part in addressing on growing energy demand of the 21st century. Electrocatalysts enable efficient and environmentally friendly conversion of chemical energy into electrical energy. The nitrogen reduction reaction (NRR) is an importnat electrochemical reaction for the various industrial chemical productions. However, the efficiency of an ideal electrocatalyst has not been achieved, and one of the reasons is among the lethargic kinetics of the NRR on the cathode. This reaction typically requires high energy input and harsh conditions, limiting its practicality. Enhancing the NRR activity requires the development of efficient electrocatalysts that can facilitate the conversion of nitrogen gas to valuable nitrogen-containing compounds with high selectivity. Atomistic modeling techniques can be employed to investigate the reaction mechanism and energetics of the catalysis process. This thesis deals with computational study of iron-based electrocatalysts for NRR activity. Computational studies can indeed provide valuable insights and guidelines for the experimental synthesis of efficient catalysts. By leveraging quantum chemistry/solid-state computational methods, comprehensive screenings of surface-alloy, composition, facet, size, and shape-dependent NRR activity of iron nanocluster catalysts can be obtained and the key factors can be identified that can influence the NRR activity.