Graphene nanosheets serve as excellent support materials in the synthesis of advanced metal nanoparticle− graphene electrocatalysts. In this study, we employ a combination of density functional theory and bond-order potential calculations to perform a systematic investigation of the adsorption energetics, structural features, and electronic structure of platinum nanoclusters supported on both defective and defect-free graphene. We establish a hierarchy of point defects and their reconstructions that can act as strong trapping sites for platinum nanoclusters and inhibit catalyst sintering. We demonstrate that the preferred low-energy structure of supported platinum nanoclusters are neither high-symmetry structures (e.g., icosahedral, cuboctahedral) nor readily derived from moderate structural distortions of high-symmetry structures, as is often assumed in computational models. Rather, supported nanoparticles assume open, low-symmetry shapes much like those observed in earlier computational work on annealing of unsupported clusters in a vacuum. The formation of metal−carbon bonds at support defects influences the average bond length and thus the strain in the metal cluster, stronger binding correlating with larger average bond lengths. Additionally, stronger binding of the cluster to the support leads to increased charge transfer from the cluster to the substrate accompanied by a substantial downshift of the cluster d-band center; in several instances, the d-band center is shifted below that of a Pt(111) surface. Our study suggests possible avenues for enhancing the stability and CO tolerance of platinum nanoparticles on graphene through defect engineering.
Platinum (Pt) nanoclusters on graphene have been shown to possess superior catalytic activity and increased selectivity in a variety of electrochemical reactions compared with bulk Pt electrodes. In this work, we use density functional theory calculations to investigate the adsorption of CO on low-energy Pt 13 clusters bound at various point defects in graphene. The presence of dangling bonds at defects in the graphene support leads to strong Pt−carbon bonding and a commensurate downshift of the cluster d-band center. This downshift of the d-band in turn decreases the binding energy of CO molecules to the cluster. Systematic random sampling of CO adsorption on clusters bound at various defects in graphene reveals that supported clusters, on average, bind CO more weakly than unsupported clusters. Moreover, the adsorption energies of CO on defective-graphene-supported clusters are found to be comparable with reported adsorption energies at undercoordinated sites, such as step-edges, on low-index Pt surfaces. Our results suggest that tailoring cluster−support interactions through defect engineering could provide a route for improving the tolerance of subnanometer Pt clusters to CO poisoning.
Two-dimensional (2D) heterostructures are interesting candidates for efficient energy storage devices due to their high carrier capacity by reversible intercalation. We employ here density functional theory calculations to investigate the structural and electronic properties of lithium-intercalated graphene/molybdenum disulfide (Gr/MoS2) heterostructures. We explore the extent to which Li intercalates at the interface formed between graphene (Gr) and molybdenum disulfide (MoS2) layers by considering the adsorption and diffusion of Li atoms, the energetic stability, and the changes in the structural morphology of MoS2. We investigate the corresponding electronic structure and charge distribution within the heterostructure at varying concentrations of Li. Our results indicate that the maximum energetically allowed ratio of Li to Mo (Li to C) is 1:1 (1:3) for both the 2H and 1T′ phases of MoS2. This is double the Li concentration allowed in graphene bilayers. We find that there is 60% more charge transfer to MoS2 than to Gr in the bilayer heterostructure, which results in a maximum doping of Gr and MoS2 of n C = 3.6 × 1014 cm–2 and n MoS2 = 6.0 × 1014 cm–2, respectively.
Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, we present over 5000 DFT calculations of H adsorption energies on dilute Ag alloys and describe a general machine learning approach to rapidly predict H adsorption energies for new Ag alloy structures. We find that random forests provide accurate predictions and that the best features are combinations of traditional chemical and structural descriptors. Further analysis of our model errors and the underlying forest kernel reveals unexpected finite-size electronic structure effects: embedded dopant atoms can display counterintuitive behavior such as nonmonotonic trends as a function of composition and high sensitivity to dopants far from the adsorbing H atom. We explain these behaviors with simple tight-binding Hamiltonians and d-orbital densities of states. We also use variations among forest leaves to predict the uncertainty of predictions, which allows us to mitigate the effects of larger errors.
We study the elastic response of graphene nanomeshes based on molecular-statics and molecular-dynamics simulations of uniaxial tensile deformation tests. Elastic properties are determined as a function of the nanomesh architecture, namely, the lattice arrangement of the pores, pore morphology, material density (ρ), and pore edge passivation, and scaling laws for the density dependence of the elastic modulus M, M(ρ), are established. We find that, for circular unpassivated pores, M scales with the square of ρ. Deviations from quadratic scaling are most strongly influenced by pore morphology and, to a lesser extent, by pore edge passivation and temperature.
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