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首页> 外文期刊>Applied Surface Science >A perspective on two chemometrics tools: PCA and MCR, and introduction of a new one: Pattern recognition entropy (PRE), as applied to XPS and ToF-SIMS depth profiles of organic and inorganic materials
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A perspective on two chemometrics tools: PCA and MCR, and introduction of a new one: Pattern recognition entropy (PRE), as applied to XPS and ToF-SIMS depth profiles of organic and inorganic materials

机译：透视两种化学计量学工具：PCA和MCR，并引入一种新的方法：模式识别熵（PRE），应用于有机和无机材料的XPS和ToF-SIMS深度剖面

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X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) are much used analytical techniques that provide information about the outermost atomic and molecular layers of materials. In this work, we discuss the application of multivariate spectral techniques, including principal component analysis (PCA) and multivariate curve resolution (MCR), to the analysis of XPS and ToF-SIMS depth profiles. Multivariate analyses often provide insight into data sets that is not easily obtained in a univariate fashion. Pattern recognition entropy (PRE), which has its roots in Shannon's information theory, is also introduced. This approach is not the same as the mutual information/entropy approaches sometimes used in data processing. A discussion of the theory of each technique is presented. PCA, MCR, and PRE are applied to four different data sets obtained from: a ToF-SIMS depth profile through ca. 100 nm of plasma polymerized C3F6 on Si, a ToF-SIMS depth profile through ca. 100 nm of plasma polymerized PNIPAM (poly (N-isopropylacrylamide)) on Si, an XPS depth profile through a film of SiO2 on Si, and an XPS depth profile through a film of Ta2O5 on Ta. PCA, MCR, and PRE reveal the presence of interfaces in the films, and often indicate that the first few scans in the depth profiles are different from those that follow. PRE and backward difference PRE provide this information in a straightforward fashion. Rises in the PRE signals at interfaces suggest greater complexity to the corresponding spectra. Results from PCA, especially for the higher principal components, were sometimes difficult to understand. MCR analyses were generally more interpretable. (C) 2017 Elsevier B.V. All rights reserved.
机译：X射线光电子能谱（XPS）和飞行时间二次离子质谱（ToF-SIMS）是使用最广泛的分析技术，可提供有关材料最外层原子和分子层的信息。在这项工作中，我们讨论了包括主成分分析（PCA）和多元曲线分辨率（MCR）在内的多元光谱技术在XPS和ToF-SIMS深度剖面分析中的应用。多变量分析通常可以洞察不容易以单变量方式获得的数据集。还介绍了模式识别熵（PRE），它起源于Shannon的信息理论。该方法与有时在数据处理中使用的互信息/熵方法不同。讨论了每种技术的原理。 PCA，MCR和PRE应用于四个不同的数据集，这些数据集来自：ToF-SIMS深度剖面（通过ca）。 Si上的100 nm等离子体聚合的C3F6，通过ca的ToF-SIMS深度分布。 Si上100 nm的等离子聚合PNIPAM（聚（N-异丙基丙烯酰胺）），Si上SiO2膜的XPS深度分布和Ta上Ta2O5膜的XPS深度分布。 PCA，MCR和PRE揭示了薄膜中存在界面，并经常表明深度剖面中的前几次扫描与随后的扫描不同。 PRE和后向差异PRE以简单的方式提供此信息。接口处PRE信号的升高表明相应光谱的复杂性更高。 PCA的结果，尤其是对于较高主成分的结果，有时很难理解。 MCR分析通常更具解释性。 （C）2017 Elsevier B.V.保留所有权利。

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