Characterizing Graphitic Carbon with X-ray Photoelectron Spectroscopy: A Step-by-Step Approach
Characterizing Graphitic Carbon with X-ray Photoelectron Spectroscopy: A Step-by-Step Approach
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DOI：
10.1002/cctc.201500344
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发表时间：
2015-09-14
期刊：
影响因子：
4.5
通讯作者：
Schloegl, Robert
中科院分区：
文献类型：
--
作者：
Blume, Raoul;Rosenthal, Dirk;Schloegl, Robert
X射线光电子能谱（XPS）是用于表征高度有序的碳纳米结构（例如碳纳米管和石墨烯）的化学和电子性质的广泛使用的技术。然而，XPS数据的分析，特别是C1s区域可能是复杂的，阻碍了数据的直接评价。在这项工作中，外部和内在的影响C1s XPS spectraexperimental，例如，光子展宽或碳催化剂interactionof各种石墨样品的概述。通过退火、溅射和氧官能化来进行此类样品的受控操纵，以识别不同的CC键合状态并评估操纵对谱线形状及其结合能位置的影响。通过高分辨率XPS和XPS深度分析，可以将来自无序碳和表面缺陷态的光谱成分与芳香sp（2）碳区分开来。这些发现说明，在分析碳材料的潜在缺陷表面时，必须同时考虑谱线形状和结合能分量。sp（2）峰是芳香碳的特征峰，具有很强的不对称性，随样品表面的曲率而变化，因此在光谱分析中不能忽略。所应用的去卷积策略可以提供一个简单的指导方针，以获得高质量的拟合实验数据的基础上仔细评估的实验条件，样品的属性，和拟合过程的限制。
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 datain particular the C1s regioncan be complex, impeding a straightforward evaluation of the data. In this work, an overview of extrinsic and intrinsic effects that influence the C1s XPS spectrafor example, photon broadening or carbon-catalyst interactionof various graphitic samples is presented. Controlled manipulation of such samples is performed by annealing, sputtering, and oxygen functionalization to identify different CC 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.