Predicting metal–metal interactions. I. The influence of strain on nanoparticle and metal adlayer stabilities

Abstract

Strain-engineering of bimetallic nanomaterials is an important design strategy for developing new catalysts. Herein, we introduce an approach for including strain effects into a recently introduced, density functional theory (DFT)-based alloy stability model. The model predicts adsorption site stabilities in nanoparticles and connects these site stabilities with catalytic reactivity and selectivity. Strain-based dependencies will increase the model’s accuracy for nanoparticles affected by finite-size effects. In addition to the stability of small nanoparticles, strain also influences the heat of adsorption of epitaxially grown metal-on-metal adlayers. In this respect, we successfully benchmark the strain-including alloy stability model with previous experimentally determined trends in the heats of adsorption of Au and Cu adlayers on Pt (111). For these systems, our model predicts stronger bimetallic interactions in the first monolayer than monometallic interactions in the second monolayer. We explicitly quantify the interplay between destabilizing strain effects and the energy gained by forming new metal–metal bonds. While tensile strain in the first Cu monolayer significantly destabilizes the adsorption strength, compressive strain in the first Au monolayer has a minimal impact on the heat of adsorption. Hence, this study introduces and, by comparison with previous experiments, validates an efficient DFT-based approach for strain-engineering the stability, and, in turn, the catalytic performance, of active sites in bimetallic alloys with atomic level resolution.

Assessing Catalytic Rates of Bimetallic Nanoparticles with Active Site Specificity - A Case Study using NO Decomposition

“…79,[81][82][83][84] For the third assumption regarding tensile/compressive strain on the active site, earlier work has found that for monometallic systems the αN,Z parameters can be straight-forwardly corrected to account for strain. 11 It has also been shown that strain has a substantial effect on 𝐵𝐸 ̅̅̅̅ 𝑀 . However, as a corresponding stress model is yet to be developed for bimetallic systems, the α-scheme will not give accurate results for significantly strained alloy structures and, hence, the full model proposed herein should be applied with caution for highly strained systems.…”

“…The evaluation of 𝐵𝐸 ̅̅̅̅ 𝑀 can be further tuned by accounting for tensile and compressive strain in the analysis. 11 In addition, we recently showed that improved description of adsorption energies on alloyed surfaces are obtained if the projected d-band center of a site (that scales with the local 𝐵𝐸 ̅̅̅̅ 𝑀 )…”

“…RPBE is known to provide a reasonable compromise between accuracy and computational cost for estimating chemisorption on metals, 92 and the computational set-up permits direct comparison to previous work on direct NO decomposition, 57 as well as the α-scheme. [4][5][6][7]11,12 The Monkhorst-Pack method 112 was used to generate k-point grids on which the Kohn-Sham equations were solved self-consistently in reciprocal space. Kinetic energy cutoffs for plane waves and charge densities were chosen as 500 eV and 5000 eV, respectively.…”

“…To bridge this gap, various structure-activity relationships have emerged for bimetallic surface facets, and more recently for bimetallic nanoparticles. [4][5][6][7] Using features of active sites as inputs, both physics-based [4][5][6][7][8][9][10][11][12][13][14][15][16][17]…”

Assessing Catalytic Rates of Bimetallic Nanoparticles with Active Site Specificity - A Case Study using NO Decomposition

“…To bridge this gap, various structure-activity relationships have emerged for bimetallic surface facets, and more recently for bimetallic nanoparticles. [4][5][6][7] Using features of active sites as inputs, both physics-based [4][5][6][7][8][9][10][11][12][13] and data-driven [14][15][16][17][18][19] relationships can evaluate catalytic descriptors with accuracy of 0.1 to 0.2 eV compared to DFT. Applying these active site-resolved descriptors for nanoparticles holds promise for efficient predictions of nanoparticle size, shape, and composition yielding optimal reaction rates.…”