How poisoning is avoided in a step of relevance to the Haber-Bosch catalysis

Abstract

For a catalyst to be efficient and durable, it is crucial that the reaction products do not poison the catalyst. In the case of the Haber-Bosch iron based catalyst, the rate-limiting step is believed to be the decomposition of nitrogen molecules on the Fe(111) surface. This step leads to the production of atomic nitrogen on the surface N* that unless are hydrogenated and eventually released as NH3 molecules, remain on the surface. Thus, it is important to ascertain how a high N* coverage affects nitrogen dissociative chemisorption. To answer this question, we study the properties of the Fe(111) surface at different N* coverage both at room and operando temperature. Using state-of-the-art simulations, we have already found that, at high temperatures, the surface atoms are highly mobile and that the catalytic centers normally associated with the surface activity acquire a finite lifetime [1]. Here, we discover that the N* surface atoms are highly mobile and that coverage reduces but does not eliminate iron mobility. The N* atoms promote the formation of metastable iron triangular surface structures whose chemical composition can be described as F e_3^*N_i^* i = 1, 4. These structures are the result of the frustrated drive of the system towards a more stable Fe4N phase. As a consequence of the formation of these structures, nitrogen atoms tend to cluster, reducing their poisoning effect. At the same time, the reduction in the number of catalytic centers is counteracted by an increase in their lifetime. The combined effect is that the barrier for dissociation is not significantly altered at least up to the maximum coverage studied here of 50%.

How Does Structural Disorder Impact Heterogeneous Catalysts? The Case of Ammonia Decomposition on Non-stoichiometric Lithium Imide

“…Such fluctuations turn out to be essential for promoting catalytic reactions. This falls in line with the recent developments in heterogeneous catalysis that point to a highly relevant role of the surface disorder and dynamics. ,,− Describing the surface dynamics in a statistical manner is thus fundamental for understanding the working principles of these catalysts and for identifying novel design strategies.…”

Structure and polymerization of liquid sulfur across the λ-transition

“…18,29 In recent years, the combination of machine-learning interatomic potentials (MLPs) of ab initio-quality and enhanced sampling (ES) in an active learning framework has proven effective in overcoming similar difficulties in processes as complex as the liquid-liquid transition in phosphorus, 11 the nucleation and phase diagram of gallium, 31 the decomposition of urea 32 or the dynamical nature of heterogenous catalytic processes. [33][34][35] In our case, such an approach has allowed the limitation of standard molecular dynamics (MD) simulations to be overcome and to simulate systems of thousands of atoms for timescales of the order of nanoseconds.…”