TY - JOUR
T1 - Probing the nanoscale driving forces for adsorbate-induced Rh50Pd50 nanoparticle reconstruction via mean-field models of multi-faceted nanoparticles
AU - Wang, Shuqiao
AU - Hensley, Alyssa J.R.
N1 - Publisher Copyright:
© 2024 The Royal Society of Chemistry.
PY - 2023/12/11
Y1 - 2023/12/11
N2 - Bimetallic catalysts frequently exhibit synergistic performance as compared to their monometallic constituents but will undergo adsorbate-induced surface segregation and reconstruction upon exposure to reaction conditions. Although such phenomena have been extensively studied by both experimental and computational approaches, there is still a lack of insight into the impact of adsorbate-surface and adsorbate-adsorbate interactions on such reconstruction. Furthermore, current computational approaches for capturing such reconstruction require expensive and time-consuming multi-scale simulations. Here, we address these challenges to modeling adsorbate-induced bimetallic nanoparticle reconstruction by developing a fast and accurate mean-field approach to modeling multi-faceted nanoparticles through a combination of density functional theory (DFT), kubic harmonics interpolation, and microkinetic modeling taken at the equilibrium limit. The power of our approach is demonstrated via a case study of Rh50Pd50 nanoparticle reconstruction under cyclical oxidizing and reducing conditions. Using DFT, we mapped the coverage, facet, and surface composition dependent O* adsorption and surface formation energies to develop mean-field models. Multi-faceted Rh50Pd50 nanoparticles under a range of temperature and pressure conditions were then modeled. The resulting O* coverage and surface layer compositions over the nanoparticles showed that highly attractive O*-Rh interactions are predominantly responsible for reconstruction but are modified by repulsive O*-O* interactions such that the absence of such O*-O* interactions fail to reproduce known experimental trends. Overall, our multi-faceted bimetallic nanoparticle modeling approach supplies faster, but still accurate predictions of surface reconstruction behaviors with a concise mean-field model. This allows for the identification of the reaction conditions and nanoscale interactions that lead to bimetallic nanoparticle reconstruction, as well as enabling the potential for tunable bimetallic nanoparticle engineering induced by reaction conditions.
AB - Bimetallic catalysts frequently exhibit synergistic performance as compared to their monometallic constituents but will undergo adsorbate-induced surface segregation and reconstruction upon exposure to reaction conditions. Although such phenomena have been extensively studied by both experimental and computational approaches, there is still a lack of insight into the impact of adsorbate-surface and adsorbate-adsorbate interactions on such reconstruction. Furthermore, current computational approaches for capturing such reconstruction require expensive and time-consuming multi-scale simulations. Here, we address these challenges to modeling adsorbate-induced bimetallic nanoparticle reconstruction by developing a fast and accurate mean-field approach to modeling multi-faceted nanoparticles through a combination of density functional theory (DFT), kubic harmonics interpolation, and microkinetic modeling taken at the equilibrium limit. The power of our approach is demonstrated via a case study of Rh50Pd50 nanoparticle reconstruction under cyclical oxidizing and reducing conditions. Using DFT, we mapped the coverage, facet, and surface composition dependent O* adsorption and surface formation energies to develop mean-field models. Multi-faceted Rh50Pd50 nanoparticles under a range of temperature and pressure conditions were then modeled. The resulting O* coverage and surface layer compositions over the nanoparticles showed that highly attractive O*-Rh interactions are predominantly responsible for reconstruction but are modified by repulsive O*-O* interactions such that the absence of such O*-O* interactions fail to reproduce known experimental trends. Overall, our multi-faceted bimetallic nanoparticle modeling approach supplies faster, but still accurate predictions of surface reconstruction behaviors with a concise mean-field model. This allows for the identification of the reaction conditions and nanoscale interactions that lead to bimetallic nanoparticle reconstruction, as well as enabling the potential for tunable bimetallic nanoparticle engineering induced by reaction conditions.
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U2 - 10.1039/d3cy01197f
DO - 10.1039/d3cy01197f
M3 - Article
AN - SCOPUS:85180067655
SN - 2044-4753
VL - 14
SP - 1122
EP - 1137
JO - Catalysis Science and Technology
JF - Catalysis Science and Technology
IS - 5
ER -