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Computational strategies to address the catalytic activity of nanoclusters

Focus Article

Akhil S. Nair, Biswarup Pathak

Published Online: Dec 10 2020

DOI: 10.1002/wcms.1508

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Abstract Metal nanoclusters have been emerged as imperative elements of heterogenous catalysis. Remarkably different properties from the bulk at the atomic level asserts construction of unique principles for understanding nanocluster catalysis. Computational studies abided by delicate theoretical framework are versatile tools for unveiling the structural, energetic, and mechanistic aspects of nanocluster‐based catalysis. Unlike other systems, nanoclusters feature properties derived from the unique surface structure, size dependence, and dynamics insisting fine‐tuning of computational approaches for accurate prediction of catalytic properties. Finding a balance between realistic simulation and computational affordability is of pressing priority. We highlight the urgency of computational models and practices to be updated by learning from the recent developments in this field. Focus is given to less explored factors governing the catalytic potential of nanoclusters. This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Density Functional Theory Structure and Mechanism > Reaction Mechanisms and Catalysis

(a) Breaking of scaling relationships between O* versus OH* for Pt_n (n = 1–6) clusters at both PBE and PBE0 levels of theory. Poor scaling is indicated low coefficient determination (R²) values. Reprinted with permission from Reference 76. Copyright 2019 American Chemical Society. (b) Structural transformation of Au₁₃ cluster from planar to 3‐dimensional geometry with increase in CO coverage revealed from AIMD simulation. Reprinted with permission from Reference 77. Copyright 2016 American Chemical Society. (c) Structural variations in the global minimum and energetically near‐lying isomers for Pt₇ and Pt₈ clusters supported on Al₂O₃. ∆Eads, P_700k and ∆Q(e) represent adsorption energy of clusters, Boltzmann population of geometries at 700 K temperature and charge transferred from Al₂O₃ to clusters. Reprinted with permission from Reference 78. Copyright 2016 American Chemical Society. (d) Scaling relation between adsorption energies of NH* and NH₂* versus N* with the intermediates occupied at the interfacial region of doped Au/MgO(100) surface. Dopants involve both inorganic elements and transition metals. Reprinted with permission from Reference 79. Copyright 2017 Wiley Publishing Group

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(a) Size versus adsorbate induced effects depicted via charge density delocalization for O* adsorption for platinum clusters of different size. Reference Pt (111) periodic surface is also included. Top and horizontal views are represented in up and down panels, respectively. Reprinted with permission from Reference 51. Copyright 2016 American Chemical Society. (b) Comparison of shape dependent ORR activity between a cuboctahedral Pt₇₉ and octahedral Pt₈₅ clusters. In the mechanism diagram, free energies (outside parenthesis) and activation energies (inside the parenthesis) of cuboctahedral, octahedral and a reference Pt(111) surface are indicated by superscripts C, O and 58(reference number), respectively. Reprinted with permission from Reference 53. Copyright 2016 Royal Society of Chemistry. (c) Screening of Pt₇₉ core–shell cluster with 3d series metals (M₁₉@Pt₆₀, M = ScCu) as core species. The cluster model is shown in the inset with core atoms violet and Pt atoms blue, respectively. The stability versus activity relationships is based on dissolution potential and ORR overpotential, respectively. Red, yellow and green regions correspond to low, medium and high activity. Reprinted with permission from Reference 54. Copyright 2019 American Chemical Society. (d) Activity differences between a trimetallic Au₁₀Co₁₉@Pt₆₀ nanocluster and a corresponding periodic AuCoPt(111) surface for H₂O₂ formation via 2 electron ORR. Reprinted with permission from Reference 55. Copyright 2017 Royal Society of Chemistry

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(a) Catalytic activity measured by limiting potentials as a function of generalized coordination number (GCN) for elementary steps of CO₂ reduction over various Copper surfaces. AD, SE, k and CAV represents adatom, step‐edge, kink and cavity sites of respective surfaces. The overpotential values are given in the Gray region where the top of the volcano corresponds to an optimum GCN value of 3.1. Reprinted with permission from Reference 42. Copyright 2016 American Chemical Society. (b) Linear correlation between O* adsorption energy and s‐state based orbital‐wise coordination number (CN^s) for gold clusters of varying sizes. Reprinted with permission from Reference 43. Copyright 2017 American Physical Society

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Free energy profiles of CO₂ reduction reaction on (a) fully protected [Au₂₅(SCH₃)₁₈]⁻ and (b) de‐thiolated [Au₂₅(SCH₃)₁₇]⁻ clusters. Reprinted with permission from Reference 100. Copyright 2016 American Institute of Physics. (c) Volcano plot of ORR activity of Au₂₂(1,8‐bis[diphenylphosphino])₆ cluster (Au₂₂ (L⁸)₆) and periodic gold surfaces constructed between limiting potential and OH* adsorption free energy. Left and right legs of volcano correspond to strong and weak OH* binding, respectively. Reprinted with permission from Reference 101. Copyright 2019 American Chemical Society. (d) Correlation of onset potentials for CO₂ reduction (UCO₂) and hydrogen evolution (UH₂) with interspace distance for confined Au(111) model. The confined region for CO adsorption is shown in inset. Reprinted with permission from Reference 102. Copyright 2020 American Chemical Society

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(a) Top (up panel) and horizonal (down panel) views of periodic slab model of platinum (111) surface. (b) Finite cluster model of cuboctahedral Pt₁₄₇ nanocluster with atoms labeled based on their coordination environment. (c) Differences in the kinetics of direct NO reduction pathway for Ni₈₅ cluster and Ni(111) surface models. Reprinted with permission from Reference 32. Copyright 2016 Nature Publishing Group

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Factors entailing adaptation of distinct theoretical frameworks for accurate computational description of nanocluster versus bulk or higher sized nanoparticle catalysts

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Different approaches of solvent modeling for nanoclusters. (a) A schematic representation of implicit solvation model with a nanocluster surrounded by dielectric solvent medium. (b) Solvation shell with water surrounding Pt₃₈ cluster during AIMD simulation. Reprinted with permission from Reference 109. Copyright 2016. Royal Society of Chemistry. (c) Consistency of micro solvation approach with three water molecules in predicting the free energy of OH* adsorption () with reasonable accuracy of extended solvation layer (). Reprinted with permission from Reference 110. Copyright 2018 American Chemical Society

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