Unveiling Curvature
Effect on Fe Atom Embedded N‑Doped
Carbon Nanotubes for Electrocatalytic Oxygen Reduction Reactions Using
Hybrid Quantum-Mechanics/Machine-Learning Potential
posted on 2024-02-19, 22:45authored byYikun Kang, Ye-Fei Li, Zhi-Pan Liu
The curvature of the catalyst’s surface is a novel
dimension
of variables that can significantly affect the catalytic activity.
Theoretical simulations of the curvature effect on catalytic activity
are, however, highly challenging because the catalyst model, being
at the mesoscopic scale (nm to μm), is far beyond the current
computational power in treating chemical reactions based on first-principles
calculations. Here we develop a hybrid QM/ML calculation scheme that
combines quantum mechanics (QM) and machine learning (ML) potentials
to explore the curvature effect on catalytic activity. With this approach,
we are able to establish quantitative curvature–activity relationships
in the representative electrocatalytic reactions, namely, oxygen reduction
reaction (ORR) on both FeN4 and Fe2N6 moieties embedded in dissimilar carbon substrates (either graphene
or carbon nanotubes) with different curvatures (κ) ranging from
0 nm–1 to 2 nm–1. The free energy
changes of the potential-determining step (ΔGPDS) decrease linearly with the increase of curvature,
and on the Fe2N6 it exhibits a steeper slope
with dΔGPDS/dκ = −0.09
eV nm. By analyzing the electronic structures, we find a linear downshift
of the energy level of Fe d-orbital as curvature increases, which
leads to the change of binding strength of key reaction intermediates,
i.e., the enhancement in Fe–OH2 binding. Our results
provide new insights into the design of electrocatalysts by tuning
the catalyst’s local curvature.