Interface engineering for a rational design of poison-free bimetallic CO oxidation catalysts

Shin, Kihyun; Zhang, Liang; An, Hyesung; Ha, Hyunwoo; Yoo, M.H.; Lee, Hyuck Mo; Henkelman, Graeme; Kim, Hyun You

doi:10.1039/c7nr01382e

Cited by 31 publications

(23 citation statements)

References 78 publications

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“…Specifically, for CO oxidation, it is found that many transition metals suffer from CO poisoning, while alloying those elements with Cu can promote oxygen adsorption and CO oxidation. [41][42][43][44] Additionally, it should be mentioned that compared to the Au-based alloys shown in Fig. 8, some Ag-and Cu-based alloys have relatively large distributions of H-and O-binding energies (Fig.…”

Section: Random Alloy Clustersmentioning

confidence: 99%

Effects of ensembles, ligand, and strain on adsorbate binding to alloy surfaces

Shin

Henkelman

2018

The Journal of Chemical Physics

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Alloying elements with strong and weak adsorption properties can produce a catalyst with optimally tuned adsorbate binding. A full understanding of this alloying effect, however, is not well-established. Here, we use density functional theory to study the ensemble, ligand, and strain effects of closepacked surfaces alloyed by transition metals with a combination of strong and weak adsorption of H and O. Specifically, we consider PdAu, RhAu, and PtAu bimetallics as ordered and randomly alloyed (111) surfaces, as well as randomly alloyed 140-atom clusters. In these alloys, Au is the weak-binding component and Pd, Rh, and Pt are characteristic strong-binding metals. In order to separate the different effects of alloying on binding, we calculate the tunability of Hand O-binding energies as a function of lattice constant (strain effect), number of alloy-substituted sublayers (ligand effect), and randomly alloyed geometries (ensemble effect). We find that on these alloyed surfaces, the ensemble effect more significantly tunes the adsorbate binding as compared to the ligand and strain effects, with the binding energies predominantly determined by the local adsorption environment provided by the specific triatomic ensemble on the (111) surface. However, we also find that tuning of adsorbate binding from the ligand and strain effects cannot be neglected in a quantitative description. Extending our studies to other bimetallics (PdAg, RhAg, PtAg, PdCu, RhCu, and PtCu), we find similar conclusions that the tunability of adsorbate binding on random alloys is predominately described by the ensemble effect.

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Section: Random Alloy Clustersmentioning

confidence: 99%

Effects of ensembles, ligand, and strain on adsorbate binding to alloy surfaces

Shin

Henkelman

2018

The Journal of Chemical Physics

Self Cite

221

134

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“…Nanoparticles (NPs) are of great scientific interest because they often show unexpected physical and chemical properties resulting from their quantum confinement effect 1,2 or high surface area 3,4 . This leads to various applications of NPs, such as quantum dots, 5-7 magnetic 8,9 or bio- [10][11][12][13] materials, and catalysis 3,[14][15][16][17][18][19][20][21] . As a key feature to determine the properties of NPs, an electronic structure such as electronic density of states (DOS) has been usually considered, where the electronic structure significantly depends on the sizes and shapes of the NPs although the elements constituting the NPs are identical.…”

Section: Introductionmentioning

confidence: 99%

Accelerated mapping of electronic density of states patterns of metallic nanoparticles via machine-learning

Bang

Yeo

Kim

et al. 2021

Sci Rep

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Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). With the PCA, a mathematically high-dimensional DOS image can be converted to a low-dimensional vector. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features that are easily extracted from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method are considered. The PCA-CGCNN model predicts the R2 value to be 0.85 or higher for Au pure NPs and 0.77 or higher for Au@Pt core@shell bimetallic NPs, respectively, in which the values are for the test sets. Although the PCA-CGCNN method showed a small loss of accuracy when compared with DFT calculations, the prediction time takes just ~ 160 s irrespective of the NP size in contrast to DFT method, for example, 13,000 times faster than the DFT method for Pt147. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy).

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“…2, but also in reforming reactions, segregates forming species (like carbon filaments) [34] avoiding a fast deactivation of the catalyst surface [35]. Oxygen also tends to remain on the catalyst surface blocking the reactivity [36]. Addition of H-donors (methane, as in dry reforming) remove both these species (surfacebound O and C) and maintains catalyst activity.…”

Section: Introductionmentioning

confidence: 99%