Crystal Facet Effect of Transition Metals for NH 3 Decomposition

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Authors: Kaipeng Deng, Bingbing Gong, He Lin, L H Zhang, Hao Jiang
Cited by: 1
References: 54

Abstract

Transition-metal nanoparticles have been extensively investigated as catalysts for ammonia decomposition, and the crystal facet effect of these metals has not yet been comprehensively elucidated. Here, the catalytic performance of ammonia decomposition on low-index crystal facets of Fe, Co, Ni, Cu, Ru, Rh, Ir, and Pt was systematically investigated by employing density functional theory (DFT) and machine learning (ML). All investigated crystal facets effectively activate NH 3 molecules, with further regulation of nitrogen atom desorption achievable by altering the exposed metal crystal facet. Among the 24 studied crystal facets, Ru(110), Co(001), and Fe(100) exhibit outstanding NH 3 dissociation activity, whereas Fe(100) demonstrates limited catalytic efficiency due to an excessively high N 2 binding energy barrier. Comparative analysis indicates that Ru(110) and Co(001) possess optimal catalytic performance, with reaction energy barriers of 1.12 and 1.18 eV, respectively. The projected density of states (PDOS) and integrated crystal orbital Hamilton population (ICOHP) calculations of the N–H bond in the rate-determining step for Ru(110) and Co(001) reveal that stronger N-M interactions correlate with enhanced NH 3 adsorption on Ru(110). This further facilitates the activation of the N–H bond in the rate-determining step, thereby granting Ru(110) superior catalytic activity compared with Co(001). The charge density difference and Bader charge analysis reveal that the metal–nitrogen bond acts as an electron transfer bridge, facilitating electron migration from the NH 3 molecule to the substrate and inducing charge redistribution. This effect enhances the chemical adsorption of NH 3 and improves the activation efficiency. Furthermore, an expression relating geometric structure, electronic structure, and ammonia activation was established by sure independence screening and sparsifying operator (SISSO). The interdisciplinary approach of DFT-ML provides insights into the origin of NH 3 adsorption and offers a novel pathway for designing high-performance catalysts.

Fields of Study & Subjects

Ammonia Synthesis and Nitrogen Reduction Advanced Chemical Physics Studies Hydrogen Storage and Materials

Publication details

Journal / source
The Journal of Physical Chemistry C
Publisher
American Chemical Society (ACS)
Volume
130
Issue
25
Pages
8716–8726
Publication date
12 June 2026
Language
EN