Identifying the Structure of Supported Metal Catalysts Using Vibrational Fingerprints from Ab Initio Nanoscale Models.

Identifying active sites of supported noble metal nanocatalysts remains challenging, since their size and shape undergo changes depending on the support, temperature, and gas mixture composition. Herein, the anharmonic infrared spectrum of adsorbed CO is simulated using density functional theory (DFT) to gain insight into the nature of Pd nanoparticles (NPs) supported on ceria. The authors systematically determine how the simulated infrared spectra are affected by CO coverage, NP size (0.5-1.5 nm), NP morphology (octahedral, icosahedral), and metal-support contact angle, by exploring a diversity of realistic models inspired by ab initio molecular dynamics. The simulated spectra are then used as a spectroscopic fingerprint to characterize nanoparticles in a real catalyst, by comparison with in-situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) experiments. Truncated octahedral NPs with an acute Pd-ceria angle reproduce most of the measurements. In particular, the authors isolate features characteristic of CO adsorbed at the metal-support interface appearing at low frequencies, both seen in simulation and experiment. This work illustrates the strong need for realistic models to provide a robust description of the active sites, especially at the interface of supported metal nanocatalysts, which can be highly dynamic and evolve considerably during reaction.