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.

While Pt-nanoparticles supported on SnO 2 exhibit improved durability, a substantial detriment is observed on the Ptnanoparticles' activity toward the oxygen reduction reaction. A density functional theory method is used to calculate isolated, SnO 2 -and graphene-supported Pt-nanoparticles. Work function difference between the Pt-nanoparticles and SnO 2 leads to electron donation from the nanoparticles to the support, making the outer-shell atoms of the supported nanoparticles more positively charged compared to unsupported nanoparticles. From an electrostatic point of view, nucleophilic species tend to interact more stably with less negatively charged Pt atoms blocking the active sites for the reaction to occur, which can explain the low activity of Pt-nanoparticles supported on SnO 2 . Introducing oxygen vacancies and Nb dopants on SnO 2 decreases the support work function, which not only reduces the charge transferred from the Pt-nanoparticles to the support but also reverses the direction of the electrons flow making the surface Pt atoms more negatively charged. A similar effect is observed when using graphene, which has a lower work function than Pt. Thus, the blocking of the active sites by nucleophilic species decreases, hence increasing the activity. These results provide a clue to improve the activity by modifying the support work function and by selecting a support material with an appropriate work function to control the charge of the nanoparticle's surface atoms. Graphic abstractKeywords Density functional theory · Platinum nanoparticles · SnO 2 · Support effect · Polymer electrolyte fuel cells Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s4245 2-019-1478-0) contains supplementary material, which is available to authorized users.* Michihisa Koyama, koyama.michihisa@nims.go.jp |

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.