The
electrocatalytic dinitrogen reduction reaction (NRR) is promising
to realize the decentralized production of ammonia by using renewable
energies, which contrasts with the energy-intensive Haber–Bosch
process. The key to achieve it is to find stable, efficient and selective
catalysts. Recently, the heterogeneous single-cluster catalysts (SCCs)
have emerged as a promising class of catalysts for electrochemical
reactions due to their atomically precise active site, abundant active
atoms and atomic level controllability. Herein, the NRR catalyzed
by the two-atom SCCs consisting of homonuclear 3d transition metal
(TM) dimers over the N-doped graphene, denoted as M2-N6G, is systematically investigated by using density functional
theory (DFT). Our results indicate that the ability of metal dimer
to capture N2 is related to the reducibility of the catalyst
and the orbital interaction between the N-2p states and the TM-3d
states. Subsequently, comparing with those metals which overbind N2 through side-on configurations, the M2-N6G SCCs with end-on adsorption of N2 work better. Furthermore,
we obtain a linear relationship between the adsorption free energies
of *N2H (ΔadsG*NH2
) and that of *NH2 (ΔadsG*NH2
). Based on this scaling relationship, we propose a
compromised strategy for screening efficient two-atom SCCs for NRR.
Finally, by comparing the stability, activity and selectivity of various
M2-N6G SCCs, the Cr2-N6G and Mn2-N6G are predicted to be most active
for NRR with low limiting potential and high suppression to hydrogen
evolution reaction (HER). The present work not only provides experimentally
synthesizable electrocatalyst candidates for NRR, but also gives insight
into the development of the two-atom SCCs.
The electrocatalytic nitrogen reduction reaction (NRR)
is one of
the most promising ways to achieve NH3 production at room
temperature and pressure. However, there exists significant disagreement
between the theoretically predicted potentials required for the NRR
by the conventional quantum–theoretical calculations and those
observed experimentally. Here, an explicit computational model incorporating
the solvation effect and electrode potential has been proposed for
NRR on single iron atoms supported on nitrogen-doped graphene. We
find that the aqueous environment plays an essential role in NRR by
promoting N2 adsorption, whereas the electrode potential
impacts considerably on the electrode–electrolyte interface
where NRR occurs. The constrained molecular dynamics (cMD) simulations
and a thermodynamic integration method are used to explore the free
energy profiles of N2 adsorption and the proton transfer
process. The results are consistent with experimental observations,
i.e., the NRR can take place at a relatively low electrode potential,
thus revealing the critical role of the explicit inclusion of the
solvation effect and electrode potential in computationally studying
electrochemical reactions. With this approach, we have provided atomic-level
mechanistic insights into the electrode–electrolyte interface
for NRR through electrochemical catalysis.
As an innovative development of single-atom catalysts (SACs), single-cluster catalysts (SCCs) such as dualatom catalysts have attracted considerable interest due to their excellent performance in catalysis. As one of the most powerful and visualizable tools, scanning transmission electron microscopy (STEM) has been widely applied in the characterization of SCCs. Herein, the nitrogen-doped carbonsupported FeFe and CoFe, two representative examples of homonuclear and heteronuclear SCCs, are characterized by STEM. Furthermore, an image processing program is developed to analyze the STEM images and to obtain the locations of atoms, as well as the projected distances between atoms in possible dual-atom pairs. The dimer distances of both CoFe and FeFe catalysts exhibit a trimodal distribution, which can correspond to the energy-favorable atomic structures of the theoretical simulations. Our work offers an avenue for directly revealing the possible atomic configurations of dual-atom sites in SCCs via big data statistics of STEM images and strong theoretical simulations.
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