Deconvoluting XPS Spectra of La-Containing Perovskites from First-Principles.
Ariel WhittenDezhou GuoElif TezelReinhard DeneckeEranda NikollaJean-Sabin McEwenPublished in: JACS Au (2024)
Perovskite-based oxides are used in electrochemical CO 2 and H 2 O reduction in electrochemical cells due to their compositional versatility, redox properties, and stability. However, limited knowledge exists on the mechanisms driving these processes. Toward this understanding, herein we probe the core level binding energy shifts of water-derived adspecies (H, O, OH, H 2 O) as well as the adsorption of CO 2 on LaCoO 3 and LaNiO 3 and correlate the simulated peaks with experimental temperature-programmed X-ray photoelectron spectroscopy (TPXPS) results. We find that the strong adsorption of such chemical species can affect the antiferromagnetic ordering of LaNiO 3 . The adsorption of such adspecies is further quantified through Bader and differential charge analyses. We find that the higher O 1 s core level binding energy peak for both LaCoO 3 and LaNiO 3 corresponds to adsorption of water-related species and CO 2 , while the lower energy peak is due to lattice oxygen. We further correlate these density functional theory-based core level O 1 s binding energies with the TPXPS measurements to quantify the decrease of the O 1 s contribution due to desorption of adsorbates and the apparent increase of the lattice oxygen (both bulk and surface) with temperature. Finally, we quantify the influence of adsorbates on the La 4 d , Co 2 p , and the Ni 3 p core level binding energy shifts. This work demonstrates how theoretically generated XPS data can be utilized to predict species-specific binding energy shifts to assist in deconvolution of the experimental results.
Keyphrases
- density functional theory
- dna binding
- aqueous solution
- gold nanoparticles
- binding protein
- high resolution
- healthcare
- induced apoptosis
- ionic liquid
- magnetic resonance imaging
- computed tomography
- cell cycle arrest
- machine learning
- electronic health record
- quantum dots
- solar cells
- cell death
- genetic diversity
- deep learning
- contrast enhanced