skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Multivariate statistical analysis of time-of-flight secondary ion mass spectrometry images using AXSIA.

Abstract

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) by its parallel nature, generates complex and very large datasets quickly and easily. An example of such a large dataset is a spectral image where a complete spectrum is collected for each pixel. Unfortunately, the large size of the data matrix involved makes it difficult to extract the chemical information from the data using traditional techniques. Because time constraints prevent an analysis of every peak, prior knowledge is used to select the most probable and significant peaks for evaluation. However, this approach may lead to a misinterpretation of the system under analysis. Ideally, the complete spectral image would be used to provide a comprehensive, unbiased materials characterization based on full spectral signatures. Automated eXpert spectral image analysis (AXSIA) software developed at Sandia National Laboratories implements a multivariate curve resolution technique that was originally developed for energy dispersive X-ray spectroscopy (EDS) [Microsci. Microanal. 9 (2003) 1]. This paper will demonstrate the application of the method to TOF-SIMS. AXSIA distills complex and very large spectral image datasets into a limited number of physically realizable and easily interpretable chemical components, including both spectra and concentrations. The number of components derived during the analysis represents the minimum numbermore » of components needed to completely describe the chemical information in the original dataset. Since full spectral signatures are used to determine each component, an enhanced signal-to-noise is realized. The efficient statistical aggregation of chemical information enables small and unexpected features to be automatically found without user intervention.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
1004385
Report Number(s):
SAND2003-3042C
TRN: US201103%%429
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the Fourteenth International Meeting on Secondary Ion Mass Spectrometry (SIMS XIV) held September 14-19, 2003 in San Diego, CA.
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; A CODES; ION MICROPROBE ANALYSIS; MASS SPECTROSCOPY; TIME-OF-FLIGHT MASS SPECTROMETERS; MULTIVARIATE ANALYSIS; IMAGE PROCESSING; MASS SPECTRA

Citation Formats

Peebles, Diane Elaine, Kotula, Paul Gabriel, Ohlhausen, James Anthony, and Keenan, Michael Robert. Multivariate statistical analysis of time-of-flight secondary ion mass spectrometry images using AXSIA.. United States: N. p., 2003. Web.
Peebles, Diane Elaine, Kotula, Paul Gabriel, Ohlhausen, James Anthony, & Keenan, Michael Robert. Multivariate statistical analysis of time-of-flight secondary ion mass spectrometry images using AXSIA.. United States.
Peebles, Diane Elaine, Kotula, Paul Gabriel, Ohlhausen, James Anthony, and Keenan, Michael Robert. Fri . "Multivariate statistical analysis of time-of-flight secondary ion mass spectrometry images using AXSIA.". United States.
@article{osti_1004385,
title = {Multivariate statistical analysis of time-of-flight secondary ion mass spectrometry images using AXSIA.},
author = {Peebles, Diane Elaine and Kotula, Paul Gabriel and Ohlhausen, James Anthony and Keenan, Michael Robert},
abstractNote = {Time-of-flight secondary ion mass spectrometry (TOF-SIMS) by its parallel nature, generates complex and very large datasets quickly and easily. An example of such a large dataset is a spectral image where a complete spectrum is collected for each pixel. Unfortunately, the large size of the data matrix involved makes it difficult to extract the chemical information from the data using traditional techniques. Because time constraints prevent an analysis of every peak, prior knowledge is used to select the most probable and significant peaks for evaluation. However, this approach may lead to a misinterpretation of the system under analysis. Ideally, the complete spectral image would be used to provide a comprehensive, unbiased materials characterization based on full spectral signatures. Automated eXpert spectral image analysis (AXSIA) software developed at Sandia National Laboratories implements a multivariate curve resolution technique that was originally developed for energy dispersive X-ray spectroscopy (EDS) [Microsci. Microanal. 9 (2003) 1]. This paper will demonstrate the application of the method to TOF-SIMS. AXSIA distills complex and very large spectral image datasets into a limited number of physically realizable and easily interpretable chemical components, including both spectra and concentrations. The number of components derived during the analysis represents the minimum number of components needed to completely describe the chemical information in the original dataset. Since full spectral signatures are used to determine each component, an enhanced signal-to-noise is realized. The efficient statistical aggregation of chemical information enables small and unexpected features to be automatically found without user intervention.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2003},
month = {8}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: