几何对黑硅光电子能谱化学分析的影响。

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Jens U Neurohr, Anton Wittig, Hendrik Hähl, Friederike Nolle, Thomas Faidt, Samuel Grandthyll, Karin Jacobs, Michael A Klatt, Frank Müller
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引用次数: 0

摘要

对于光滑表面,化学成分可以很容易地使用各种光谱技术进行分析,一个突出的例子是x射线光电子能谱(XPS),其中元素的相对比例主要由元素特定光电子的强度比决定。然而,对于像黑硅(b-Si)这样的纳米级表面,由于几何形状的陡峭斜坡,模拟发射角的局部变化,这种分析变得更加复杂。在本研究中,通过使用Minkowski张量的积分几何分析,将XPS化学数据与原子力显微镜(AFM)的地形信息相关联,明确量化了这种效应。这种方法可以对纳米级表面的层厚进行可靠的估计。对于b-Si,发现其氧化层比标准硅片上的天然氧化层厚约50%-80%。这项研究强调了纳米几何形状对化学性质分析的重要影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of Geometry on Chemical Analysis Exemplified for Photoelectron Spectroscopy of Black Silicon.

For smooth surfaces, chemical composition can be readily analyzed using various spectroscopic techniques, a prominent example is X-ray photoelectron spectroscopy (XPS), where the relative proportions of the elements are mainly determined by the intensity ratio of the element-specific photoelectrons. However, this analysis becomes more complex for nanorough surfaces like black silicon (b-Si) due to the geometry's steep slopes, which mimic local variations in emission angles. In this study, this effect is explicitly quantified through an integral geometric analysis using Minkowski tensors, correlating XPS chemical data with topographical information from Atomic Force Microscopy (AFM). This approach yields reliable estimates of layer thicknesses for nanorough surfaces. For b-Si, it is found that the oxide layer is ≈50%-80% thicker than the native oxide layer on a standard Si wafer. This study underscores the significant impact of nanoscale geometries on chemical property analysis.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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