Epitaxial Core‐Shell Oxide Nanoparticles: First‐Principles Evidence for Increased Activity and Stability of Rutile Catalysts for Acidic Oxygen Evolution

Abstract

Due to their high activity and favorable stability in acidic electrolytes, Ir and Ru oxides are primary catalysts for the oxygen evolution reaction (OER) in proton-exchange membrane (PEM) electrolyzers. For a future large-scale application, coreshell nanoparticles are an appealing route to minimize the demand for these precious oxides. Here, we employ firstprinciples density-functional theory (DFT) and ab initio thermodynamics to assess the feasibility of encapsulating a cheap rutile-structured TiO 2 core with coherent, monolayer-thin IrO 2 or RuO 2 films. Resulting from a strong directional dependence of adhesion and strain, a wetting tendency is only obtained for some low-index facets under typical gas-phase synthesis conditions. Thermodynamic stability in particular of latticematched RuO 2 films is instead indicated for more oxidizing conditions. Intriguingly, the calculations also predict an enhanced activity and stability of such epitaxial RuO 2 /TiO 2 coreshell particles under OER operation.

Improving the Oxygen Evolution Activity and Stability of Nb-Doped TiO2-Supported RuO2 by a SnO2 Interlayer: A Model Catalyst Study on Single-Crystal Oxide Heterostructures

“…Lee et al studied the OER overpotential of a RuO 2 /TiO 2 (110) model core–shell structure by DFT calculations. They deduced that both the OER activity and electrochemical structural stability of the surface RuO 2 layer can be improved due to the anisotropic strain derived from the large lattice mismatch between RuO 2 and TiO 2 . Therefore, the moderate anisotropic compressive strain in the [001] direction of the surface RuO 2 (110) layers, which is realized by the SnO 2 interlayer at the RuO 2 /TiO 2 interface, should be correlated with the enhanced OER activity of RuO 2 /SnO 2 /TiO 2 .…”

“…The acidic environment of PEMWE causes the dissolution of the catalysts and a steep increase in the OER overpotential. Hence, research to improve Ru oxide catalysts, especially the electrochemical structural stability, has been eagerly pursued. − …”

Ab Initio Thermodynamic Stability of Carbide Catalysts under Electrochemical Conditions

“…Lower positive values reflect a higher stability compared to other competing surface configurations, while negative values indicate an overall thermodynamic instability. With regards to the carbides’ propensity to oxidize in aqueous electrochemical environments, specifically the potential- and pH-dependent surface free energies γ = γ­( U , pH) of oxygen- or hydroxyl-covered surface terminations appear as natural descriptor candidates. , The potential or pH values at which one of these surface free energies becomes negative would then approximately delineate the electrochemical stability and the onset of oxide formation. Analogously, under strongly reducing conditions, the potential or pH values at which the surface free energy of bare C-terminations becomes negative, would signal the onset of cathodic de-carburization.…”

“…Combining the surface free energies of the most stable surface configurations within this window of operation into a Wulff construction allows us to deduce the potential-dependent equilibrium nanoparticle shape, , which in turn allows us to deduce the facet types exhibited. Figure compiles the corresponding shapes for the considered group VB and VIB carbides.…”

Staged Training of Machine-Learning Potentials from Small to Large Surface Unit Cells: Efficient Global Structure Determination of the RuO2(100)-c(2 × 2) Reconstruction and (410) Vicinal

“…Its training was bootstrapped with a DFT training set comprising atomic information, gas phase O 2 dimer data with varying O−O bond lengths, and rutile RuO 2 bulk unit cells at optimized and constrained lattice constants with both optimized and displaced internal coordinates. The set also contained all inequivalent (1 × 1) surface structures that result from simple bulk truncation at various crystal planes, 59 where each time both the bulk truncated and the locally DFT optimized geometry was added to the pool. The iterative refinement proceeded in nine cycles and was terminated because no further unknown structures were found.…”