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| Hauptseite > Publikationsdatenbank > A spectrum deconvolution method based on grey relational analysis > print |
| Hauptseite > Publikationsdatenbank > A spectrum deconvolution method based on grey relational analysis > print |
| 001 | 902984 | ||
| 005 | 20240712084531.0 | ||
| 024 | 7 | _ | |a 10.1016/j.rinp.2021.104031 |2 doi |
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| 037 | _ | _ | |a FZJ-2021-04730 |
| 082 | _ | _ | |a 530 |
| 100 | 1 | _ | |a Mu, Rui |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a A spectrum deconvolution method based on grey relational analysis |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2021 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a The extensive usage of X-ray spectroscopies in studying complex material systems is not only intended toreveal underlying mechanisms that govern physical phenomena, but also used in applied studies focused onan insight-driven performance improvement of a wide range of devices. However, the traditional analysismethods for X-ray spectroscopic data are rather time-consuming and sensitive to errors in data preprocessing(e.g., normalization or background subtraction). In this study, a method based on grey relationalanalysis, a multi-variable statistical method, is proposed to analyze and extract information from X-rayspectroscopic data. As a showcase, the valence bands of microcrystalline silicon suboxides probed by hardX-ray photoelectron spectroscopy (HAXPES) were investigated. The results obtained by the proposedmethod agree well with conventionally derived composition information (e.g., curve fit of Si 2p core levelof the silicon suboxides). Furthermore, the uncertainty of chemical compositions derived by the proposedmethod is smaller than that of traditional analysis methods (e.g., the least square fit), when artificial linearfunctions are introduced to simulate the errors in data pre-processing. This suggests that the proposedmethod is capable of providing more reliable and accurate results, especially for data containing significantnoise contributions or that is subject to inconsistent data pre-processing. Since the proposed method is lessexperience-driven and error-prone, it offers a novel approach for automate data analysis, which is of greatinterest for various applications, such as studying combinatorial material “libraries”. |
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| 700 | 1 | _ | |a Zheng, Yujie |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Lambertz, Andreas |0 P:(DE-Juel1)130263 |b 2 |
| 700 | 1 | _ | |a Wilks, Regan G. |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Bär, Marcus |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Zhang, Yufeng |0 P:(DE-HGF)0 |b 5 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.rinp.2021.104031 |g Vol. 23, p. 104031 - |0 PERI:(DE-600)2631798-9 |p 104031 |t Results in Physics |v 23 |y 2021 |x 2211-3797 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/902984/files/1-s2.0-S2211379721001984-main.pdf |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/902984/files/A%20spectrum%20deconvolution%20method%20based%20on%20grey%20relational%20analysis.pdf |
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