{"title":"通过多面纳米粒子的均方场模型探测吸附剂诱导的 Rh50Pd50 纳米粒子重构的纳米级驱动力","authors":"Shuqiao Wang , Alyssa J. R. Hensley","doi":"10.1039/d3cy01197f","DOIUrl":null,"url":null,"abstract":"<div><p>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 <em>via</em> a case study of Rh<sub>50</sub>Pd<sub>50</sub> 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 Rh<sub>50</sub>Pd<sub>50</sub> 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.</p></div>","PeriodicalId":66,"journal":{"name":"Catalysis Science & Technology","volume":"14 5","pages":"Pages 1122-1137"},"PeriodicalIF":4.2000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Probing the nanoscale driving forces for adsorbate-induced Rh50Pd50 nanoparticle reconstruction via mean-field models of multi-faceted nanoparticles†\",\"authors\":\"Shuqiao Wang , Alyssa J. R. Hensley\",\"doi\":\"10.1039/d3cy01197f\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 <em>via</em> a case study of Rh<sub>50</sub>Pd<sub>50</sub> 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 Rh<sub>50</sub>Pd<sub>50</sub> 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.</p></div>\",\"PeriodicalId\":66,\"journal\":{\"name\":\"Catalysis Science & Technology\",\"volume\":\"14 5\",\"pages\":\"Pages 1122-1137\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-03-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Catalysis Science & Technology\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/org/science/article/pii/S2044475324001023\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Catalysis Science & Technology","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S2044475324001023","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Probing the nanoscale driving forces for adsorbate-induced Rh50Pd50 nanoparticle reconstruction via mean-field models of multi-faceted nanoparticles†
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.
期刊介绍:
A multidisciplinary journal focusing on cutting edge research across all fundamental science and technological aspects of catalysis.
Editor-in-chief: Bert Weckhuysen
Impact factor: 5.0
Time to first decision (peer reviewed only): 31 days