Role of Gold Atoms in Oxidation and Reduction of Cationic Rhodium–Gold Oxide Clusters, Rh_nAu_mO_k⁺, Studied by Thermal Desorption Spectrometry and DFT Calculations

Abstract

The effect of Au atom addition to Rh n + on redox reactions was investigated by measuring the oxidation rate constants and threshold energies of the thermal reduction of gas-phase rhodium−gold oxide clusters, Rh n Au m O k + (n = 4−8; m = 0, 1, 2) and by density functional theory (DFT) calculation. The thermally stable clusters at ∼800 K were found to be Rh n Au m O n + regardless of m, suggesting that Au atoms are less or negatively involved in stabilization of the oxides. The DFT calculations on the stable Rh 6 Au m O 6 + clusters showed that Au atoms bind to Rh atoms, keeping O atoms away from the Au atoms. Electronically, the Au atoms reduced the natural charges of Rh and O atoms, forming superoxide, O 2 − . The Au atoms are considered to have an oxidation state of −1 in Rh 6 Au 2 O 6 + .

Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm

“…Efficient NO removal technology under lean-burn conditions has been intensely studied to satisfy increasingly strict emission requirements for gasoline and diesel engines, and catalytic converters based on the direct decomposition of NO without any reducing agent, namely, 2NO → N 2 + O 2 , would be ideal for small passenger vehicles. , Several pure metal catalysts are known to activate the NO dissociation, which is the first step of the NO decomposition, but those catalyst surfaces are known to be poisoned by oxygen atoms generated by the dissociation because of their too strong binding energies with the oxygens . Past studies indicate that alloying the catalysts with Au can enhance the oxygen desorption, − and the results encourage development of the bifunctional Rh (1– x ) Au x alloy catalyst.…”

Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors

“…It has been shown that alloying the catalysts with Au can improve the desorption rate of oxygen. [ 119 ] Designing the corresponding alloy NPs can further improve the utilization of noble metal atoms and the catalytic activities of surface sites, because of their high specific surface areas and volume ratios. Relying on the fact that catalytically active sites are determined by their local atomic configurations, Jinnouchi employed a ML scheme and extrapolated the experience to alloy NPs by learning geometrical information of the well‐defined single crystal (SC) surfaces (see Figure 8 ).…”