Novel principal component analysis tool based on python for analysis of complex spectra of time-of-flight secondary ion mass spectrometry

Zhou, Yadong; Jiang, Peishi; Chen, Ping; Jia, Endong; Welch, Cole S.; Zhao, Qian; Dhas, Jeffrey A.; Graham, Emily B.; Chen, Xingyuan; Zhang, Xin; Zhu, Zihua

doi:10.1116/6.0003355

Novel principal component analysis tool based on python for analysis of complex spectra of time-of-flight secondary ion mass spectrometry

Journal Article · Tue Feb 06 00:00:00 EST 2024 · Journal of Vacuum Science and Technology A

DOI:https://doi.org/10.1116/6.0003355· OSTI ID:2561411

; Jiang, Peishi; Chen, Ping; Jia, Endong; Welch, Cole S.; ; ; ; ; ;

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface analysis tool, which can simultaneously provide elemental, isotopic, and molecular information with part per million (ppm) sensitivity. However, each spectrum may be composed of hundreds of ion signals, which makes the spectra data complex. Principal component analysis (PCA) is a multivariate analysis technique that has been widely used to figure out the variances among samples in ToF-SIMS spectra data analysis and is showing great success in the explanation of complex ToF-SIMS spectra. So far, several software tools have been developed for PCA of ToF-SIMS spectra; however, none of them are freely available. Such a situation leads to some difficulties in extending applications of PCA to various research fields. More importantly, it has long been challenging for common researchers to understand PCA plots and extract chemical differences among samples. In this work, we developed a new and flexible software tool (named “advanced spectra pca toolbox”) based on python for PCA of complex ToF-SIMS spectra along with an easy-to-read manual. It can generate data analysis reports automatically to explain chemical differences among samples, allowing less experienced researchers to easily understand tricky PCA results. Moreover, it is expandable and compatible with artificial intelligence/machine learning functions. Pure goethite and different lignin adsorbed goethite samples were used as a model system to demonstrate our new software tool, proving that our software tool can be readily used in complex spectra data processing. Our new software tool is open-source, convenient, flexible, and expandable. We expect this open-source tool will benefit the ToF-SIMS community.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2561411

Alternate ID(s):: OSTI ID: 2299513

Report Number(s):: PNNL-SA--189615; 023204

Journal Information:: Journal of Vacuum Science and Technology A, Journal Name: Journal of Vacuum Science and Technology A Journal Issue: 2 Vol. 42; ISSN 0734-2101

Publisher:: American Vacuum SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Novel principal component analysis tool based on python for analysis of complex spectra of time-of-flight secondary ion mass spectrometry

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