Determining surface-specific Hubbard- U corrections and identifying key adsorbates on nickel and cobalt oxide catalyst surfaces.
Shang JiangSamir H MushrifPublished in: Physical chemistry chemical physics : PCCP (2023)
NiO is a popular transition metal oxide (TMO) with high thermal and chemical stability and Co 3 O 4 is a relatively more reducible TMO due to weaker metal-oxygen bonds. Both are often used as catalysts in a variety of chemical transformations. Density functional theory (DFT) and X-ray photoelectron spectroscopy (XPS) are used to investigate catalysis on TMO surfaces, yet both techniques have their own limitations. The accuracy of DFT highly depends on the choice of Hubbard U correction. The bulk-property optimized U value of 5.3 eV for NiO and different U values for Co 3 O 4 , without any consensus, are often used in the literature to simulate surface catalysis. However, U values optimized using bulk properties often fail to reproduce surface-adsorbate interactions on TMOs. Similarly, there exists arbitrariness in assigning observed XPS shifts to different surface species on these metal oxides. Hence, a synergistic application of XPS and DFT+ U is implemented to determine the surface specific U values for NiO and Co 3 O 4 , and to identify adsorbed surface moieties corresponding to experimentally observed XPS shifts. For the NiO (100) surface, the U value of ∼2 eV is able to reproduce the experimentally observed XPS O1s core level binding energy shifts correctly, instead of the bulk property optimized and commonly used U value of 5.3 eV. Using this surface specific U value of 2 eV, the experimentally observed XPS shifts are assigned. Similarly, for Co 3 O 4 (100) surface, ∼3 eV of U value could successfully predict the experimentally observed XPS shifts and corresponding adsorbates. The surface adsorbates and configurations suggested in this work will help analyze experimental XPS data and the surface specific U values will ensure accurate predictions of adsorption and reaction energetics on these catalysts.
Keyphrases
- density functional theory
- molecular docking
- transition metal
- systematic review
- machine learning
- molecular dynamics
- escherichia coli
- gold nanoparticles
- pseudomonas aeruginosa
- magnetic resonance
- transcription factor
- deep learning
- computed tomography
- reduced graphene oxide
- single molecule
- decision making
- room temperature
- mass spectrometry