X射线光电子能谱
残余物
马克西玛
标准差
曲线拟合
功能(生物学)
分析化学(期刊)
样品(材料)
缩小
计算物理学
化学
材料科学
算法
数学
物理
统计
热力学
数学优化
核磁共振
表演艺术
艺术史
色谱法
艺术
进化生物学
生物
作者
George H. Major,Vincent Fernandez,Neal Fairley,Emily F. Smith,Matthew R. Linford
出处
期刊:Journal of vacuum science & technology
[American Institute of Physics]
日期:2022-09-22
卷期号:40 (6)
被引量:37
摘要
Peak fitting of x-ray photoelectron spectroscopy (XPS) data is the primary method for identifying and quantifying the chemical states of the atoms near the surface of a sample. Peak fitting is typically based on the minimization of a figure-of-merit, such as the residual standard deviation (RSD). Here, we show that optimal XPS peak fitting is obtained when the peak shape (the synthetic mathematical function that represents the chemical states of the material) best matches the physics and chemistry of the underlying data. However, because this ideal peak shape is often unknown, constraints on the components of a fit are usually necessary to obtain good fits to data. These constraints may include fixing the relative full width at half maxima (peak widths), area ratios, and/or the relative positions of fit components. As shown in multiple examples, while unconstrained, less-than-optimal peak shapes may produce lower RSDs, they often lead to incorrect results. Thus, the “suboptimal” results (somewhat higher RSDs) that are obtained when constraints are applied to less-than-perfect peak shapes are often preferable because they prevent a fit from yielding unphysical or unchemical results. XPS peak fitting is best performed when all the information available about a sample is used, including its expected chemical and physical composition, information from other XPS narrow and survey scans from the same material, and information from other analytical techniques.
科研通智能强力驱动
Strongly Powered by AbleSci AI