The effect of SnO2(110) supports on the geometrical and electronic properties of platinum nanoparticles

Theoretical search for characteristic atoms in supported gold nanoparticles: a large-scale DFT study

The size and site dependences of atomic and electronic structures in isolated and supported gold nanoparticles have been investigated using large-scale density functional theory (DFT) calculations using multi-site support functions. The effects of the substrate on nanoparticles with diameters of 2 nm and several different shapes have been examined. First, isolated gold nanoparticles with diameters of 0.6 nm (13 atoms) to 4.5 nm (2057 atoms), which have comparable sizes to nanoparticles used in experiments, were considered. To analyse huge amounts of data obtained from large-scale DFT calculations, we performed principal component analysis (PCA), which helps systematically and efficiently clarify the electronic structures of large nanoparticles. The PCA results reveal the site dependence of the electronic structures. Notably, the atoms in the surface and subsurface have different electronic structures to those located in the inner layers, especially at the vertexes of the particles. The convergence of local electronic structures with respect to the particle size has also been demonstrated. For supported nanoparticles, PCA helps indicate which atoms are affected, and how much, by the substrate. The correlation between the PCA results and site dependence of reaction activity is also discussed herein.

Adsorption Energy in Oxygen Electrocatalysis

Adsorption energy (AE) of reactive intermediate is currently the most important descriptor for electrochemical reactions (e.g., water electrolysis, hydrogen fuel cell, electrochemical nitrogen fixation, electrochemical carbon dioxide reduction, etc.), which can bridge the gap between catalyst's structure and activity. Tracing the history and evolution of AE can help to understand electrocatalysis and design optimal electrocatalysts. Focusing on oxygen electrocatalysis, this review aims to provide a comprehensive introduction on how AE is selected as the activity descriptor, the intrinsic and empirical relationships related to AE, how AE links the structure and electrocatalytic performance, the approaches to obtain AE, the strategies to improve catalytic activity by modulating AE, the extrinsic influences on AE from the environment, and the methods in circumventing linear scaling relations of AE. An outlook is provided at the end with emphasis on possible future investigation related to the obstacles existing between adsorption energy and electrocatalytic performance.

Adsorption States of N2/H2 Activated on Ru Nanoparticles Uncovered by Modulation-Excitation Infrared Spectroscopy and Density Functional Theory Calculations

The adsorption states of N₂ and H₂ on MgO-supported Ru nanoparticles under conditions close to those of ammonia synthesis (AS; 1 atm, 250 °C) were uncovered by modulation-excitation infrared spectroscopy and density functional theory calculations using a nanoscale Ru particle model. The two most intense N₂ adsorption peaks corresponded to the vertical chemisorption of N₂ on the nanoparticle's top and bridge sites, while the remaining peaks were assigned to horizontally adsorbed N₂ in view of the site heterogeneity of Ru nanoparticles. Long-term observations showed that vertically adsorbed N₂ molecules gradually migrated from the top sites to the bridge sites. Compared to those adsorbed vertically, N₂ molecules adsorbed horizontally exhibited a lower dipole moment, an increased N─N bond distance, and a decreased N─N bond order (i.e., were activated), which was ascribed to enhanced Ru-to-N charge transfer. H₂ molecules were preferentially adsorbed horizontally on top sites and then rapidly dissociated to afford strongly surface-bound H atoms and thus block the active sites of Ru nanoparticles. Our results clarify the controversial adsorption/desorption behavior of N₂ and H₂ on AS catalysts and facilitate their further development.

Density Functional Theory and Machine Learning Description and Prediction of Oxygen Atom Chemisorption on Platinum Surfaces and Nanoparticles

Elucidating chemical interactions between catalyst surfaces and adsorbates is crucial for understanding surface chemical reactivity. Herein, interactions between O atoms and Pt surfaces and nanoparticles are described as a linear combination of the properties of pristine surfaces and isolated nanoparticles. The energetics of O chemisorption onto Pt surfaces were described using only two descriptors related to surface geometrical features. The relatively high coefficient of determination and low mean absolute error between the density functional theory-calculated and predicted O binding energies indicate good accuracy of the model. For Pt nanoparticles, O binding is described by the geometrical features and electronic properties of isolated nanoparticles. Using a linear combination of five descriptors and accounting for nanoparticle size effects and adsorption site types, the O binding energy was estimated with a higher accuracy than with conventional single-descriptor models. Finally, these five descriptors were used in a general model that decomposes O binding energetics on Pt surfaces and nanoparticles. Good correlation was achieved between the calculated and predicted O binding energies, and model validation confirmed its accuracy. This is the first model that considers the nanoparticle size effect and all possible adsorption sites on Pt nanoparticles and surfaces.

An Element-Based Generalized Coordination Number for Predicting the Oxygen Binding Energy on Pt3M (M = Co, Ni, or Cu) Alloy Nanoparticles

We studied the binding energies of O species on face-centered-cubic Pt3M nanoparticles (NPs) with a Pt-skin layer using density functional theory calculations, where M is Co, Ni, or Cu. It is desirable to express the property by structural parameters rather than by calculated electronic structures such as the d-band center. A generalized coordination number (GCN) is an effective descriptor to predict atomic or molecular adsorption energy on Pt-NPs. The GCN was extended to the prediction of highly active sites for oxygen reduction reaction. However, it failed to explain the O binding energies on Pt-skin Pt150M51-NPs. In this study, we introduced an element-based GCN, denoted as GCNA-B, and considered it as a descriptor for supervised learning. The obtained regression coefficients of GCNPt-Pt were smaller than those of the other GCNA-B. With increasing M atoms in the subsurface layer, GCNPt-M, GCNM-Pt, and GCNM-M increased. These factors could reproduce the calculated result that the O binding energies of the Pt-skin Pt150M51-NPs were less negative than those of the Pt201-NPs. Thus, GCNA-B explains the ligand effect of the O binding energy on the Pt-skin Pt150M51-NPs.