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Applied surface science v.433, 2018년, pp.994 - 1017   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

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

Chatterjee, Shiladitya (Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA ) ; Singh, Bhupinder (Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA ) ; Diwan, Anubhav (Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA ) ; Lee, Zheng Rong (Department of Physics, Illinois Institute of Technology, Chicago, IL 60616, USA ) ; Engelhard, Mark H. (Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA ) ; Terry, Jeff (Department of Physics, Illinois Institute of Technology, Chicago, IL 60616, USA ) ; Tolley, H. Dennis (Department of Statistics, Brigham Young University, Provo, UT 84602, USA ) ; Gallagher, Neal B. (Eigenvector Research, Inc., PO Box B, Manson, WA 98831, USA ) ; Linford, Matthew R. (Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84602, USA ) ;
  • 초록  

    Abstract 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. 100nm of plasma polymerized C 3 F 6 on Si, a ToF-SIMS depth profile through ca. 100nm of plasma polymerized PNIPAM (poly ( N -isopropylacrylamide)) on Si, an XPS depth profile through a film of SiO 2 on Si, and an XPS depth profile through a film of Ta 2 O 5 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. Highlights Comparison is presented of PCA and MCR of XPS and ToF-SIMS depth profiles. Results from two polymeric films and two inorganic films are shown. MCR results are more intuitive/straightforward than those from PCA. The pattern recognition entropy (PRE) is introduced as a summary statistic. PRE and finite difference PRE identify transitions in depth profiles. Graphical abstract [DISPLAY OMISSION]


  • 주제어

    Chemometrics .   Depth profile .   MCR .   PCA .   XPS .   ToF-SIMS.  

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