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Characterizing Graphitic Carbon with X-ray Photoelectron Spectroscopy: A Step-by-Step Approach

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Rosenthal,  Dirk
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

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Li,  Henan
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

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Knop-Gericke,  Axel
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

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Schlögl,  Robert
Inorganic Chemistry, Fritz Haber Institute, Max Planck Society;

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Citation

Blume, R., Rosenthal, D., Tessonnier, J.-P., Li, H., Knop-Gericke, A., & Schlögl, R. (2015). Characterizing Graphitic Carbon with X-ray Photoelectron Spectroscopy: A Step-by-Step Approach. ChemCatChem, 7(18), 2871-2881. doi:10.1002/cctc.201500344.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0027-D2F1-6
Abstract
X-ray photoelectron spectroscopy (XPS) is a widely used technique for characterizing the chemical and electronic properties of highly ordered carbon nanostructures, such as carbon nanotubes and graphene. However, the analysis of XPS data—in particular the C 1s region—can be complex, impeding a straightforward evaluation of the data. In this work, an overview of extrinsic and intrinsic effects that influence the C 1s XPS spectra—for example, photon broadening or carbon–catalyst interaction—of various graphitic samples is presented. Controlled manipulation of such samples is performed by annealing, sputtering, and oxygen functionalization to identify different C[BOND]C bonding states and assess the impact of the manipulations on spectral line shapes and their binding energy positions. With high-resolution XPS and XPS depth profiling, the spectral components arising from disordered carbon and surface-defect states can be distinguished from aromatic sp-2 carbon. These findings illustrate that both spectral line shapes and binding energy components must be considered in the analysis of potentially defective surfaces of carbon materials. The sp-2 peak, characteristic of aromatic carbon, features a strong asymmetry that changes with the curvature of the sample surface and, thus, cannot be neglected in spectral analysis. The applied deconvolution strategy may provide a simple guideline to obtaining high-quality fits to experimental data on the basis of a careful evaluation of experimental conditions, sample properties, and the limits of the fit procedure.