.png){kind=icon}
.png){kind=icon}
Return
Multivariate analysis for scanning tunneling spectroscopy data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.apsusc.2017.09.124.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We applied principal component analysis (PCA) to two-dimensional tunneling spectroscopy (2DTS) data obtained on a Si(111)-(7 x 7) surface to explore the effectiveness of multivariate analysis for interpreting 2DTS data. We demonstrated that several components that originated mainly from specific atoms at the Si(111)-(7 x 7) surface can be extracted by PCA. Furthermore, we showed that hidden components in the tunneling spectra can be decomposed (peak separation), which is difficult to achieve with normal 2DTS analysis without the support of theoretical calculations. Our analysis showed that multivariate analysis can be an additional powerful way to analyze 2DTS data and extract hidden information from a large amount of spectroscopic data. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Scanning tunneling microscopy
Multivariate analysis
Principal component analysisa
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.9
Papers:
6.1W
Citations:
19.4W
Organization
View more organizations