Atomic-Scale Phase Composition through Multivariate Statistical Analysis of Atom Probe Tomography Data

2011 ◽  
Vol 17 (3) ◽  
pp. 418-430 ◽  
Author(s):  
Michael R. Keenan ◽  
Vincent S. Smentkowski ◽  
Robert M. Ulfig ◽  
Edward Oltman ◽  
David J. Larson ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Atom Probe Tomography ◽  
Atom Probe ◽  
Atomic Scale ◽  
Single Atom ◽  
Chemical Information ◽  
Laptop Computer ◽  
Multivariate Statistical ◽  
Tomography Data

AbstractWe demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

scholarly journals Phase Composition at the Atomic-Size Scale through Multivariate Statistical Analysis of Atom Probe Tomography Data

2011 ◽  
Vol 17 (S2) ◽  
pp. 720-721
Author(s):  
M Keenan ◽  
V Smentkowski ◽  
R Ulfig ◽  
E Oltman ◽  
D Larson ◽  
...  
Keyword(s):  
Phase Composition ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Atom Probe Tomography ◽  
Atom Probe ◽  
Multivariate Statistical ◽  
Atomic Size ◽  
Size Scale ◽  
Tomography Data

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

scholarly journals Multivariate Statistical Analysis of Atom Probe Tomography Data

2010 ◽  
Vol 16 (S2) ◽  
pp. 270-271 ◽  
Author(s):  
MR Keenan ◽  
VS Smentkowski ◽  
RM Ulfig ◽  
E Oltman ◽  
DJ Larson ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Atom Probe Tomography ◽  
Atom Probe ◽  
Multivariate Statistical ◽  
Tomography Data

Extended abstract of a paper presented at Microscopy and Microanalysis 2010 in Portland, Oregon, USA, August 1 – August 5, 2010.

scholarly journals Applying Multivariate Statistical Analysis to Atom Probe Tomography

2010 ◽  
Vol 16 (S2) ◽  
pp. 1858-1859 ◽  
Author(s):  
C Parish ◽  
C Capdevila ◽  
MK Miller
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Atom Probe Tomography ◽  
Atom Probe ◽  
Multivariate Statistical

Extended abstract of a paper presented at Microscopy and Microanalysis 2010 in Portland, Oregon, USA, August 1 – August 5, 2010.

Wood discrimination analyses of Pterocarpus tinctorius and endangered Pterocarpus santalinus using DART-FTICR-MS coupled with multivariate statistics

IAWA Journal ◽  
2019 ◽  
Vol 40 (1) ◽  
pp. 58-74 ◽  
Author(s):  
Maomao Zhang ◽  
Guang Jie Zhao ◽  
Bo Liu ◽  
Tuo He ◽  
Juan Guo ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Information ◽  
Ion Cyclotron Resonance ◽  
Timber Species ◽  
Multivariate Statistical ◽  
Fticr Ms ◽  
Pterocarpus Santalinus

ABSTRACT Pterocarpus santalinus, listed in CITES Appendix II, is an endangered timber species as a result of illegal harvesting due to its high value and commercial demand. The growing demand for P. santalinus and timbers with the morphologically similar Pterocarpus tinctorius has resulted in confusion as well as identification problems. Therefore, it is of vital importance to explore reliable ways to accurately discriminate between P. santalinus and P. tinctorius. In this study, the method of direct analysis in real time and fourier transform ion cyclotron resonance mass spectrometry (DART-FTICR-MS), combined with multivariate statistical analysis, was used to extract chemical information from xylarium wood specimens and to explore the feasibility of distinguishing these two species. Significant differences were observed in their DART-FTICR-MS spectra. Orthogonal partial least square-discriminant analysis (OPLS-DA) showed the highest prediction, with an accuracy of 100%. These findings demonstrate the feasibility of authenticating wood types using DART-FTICR-MS coupled with multivariate statistical analysis.

Improving the Quantification of Deuterium in Zirconium Alloy Atom Probe Tomography Data Using Existing Analysis Methods

2021 ◽  
pp. 1-10
Author(s):  
Megan E. Jones ◽  
Andrew J. London ◽  
Andrew J. Breen ◽  
Paul D. Styman ◽  
Shyam Sikotra ◽  
...  
Keyword(s):  
Hydrogen Embrittlement ◽  
Atom Probe Tomography ◽  
Zirconium Alloys ◽  
Nuclear Fission ◽  
Atom Probe ◽  
Atomic Scale ◽  
Ratio Spectrum ◽  
Alloy Atom ◽  
Fission Reactors ◽  
Tomography Data

Zirconium alloys are common fuel claddings in nuclear fission reactors and are susceptible to the effects of hydrogen embrittlement. There is a need to be able to detect and image hydrogen at the atomic scale to gain the experimental evidence necessary to fully understand hydrogen embrittlement. Through the use of deuterium tracers, atom probe tomography (APT) is able to detect and spatially locate hydrogen at the atomic scale. Previous works have highlighted issues with quantifying deuterium concentrations using APT due to complex peak overlaps in the mass-to-charge-state ratio spectrum between molecular hydrogen and deuterium (H2 and D). In this work, we use new methods to analyze historic and simulated atom probe data, by applying currently available data analysis tools, to optimize solving peak overlaps to improve the quantification of deuterium. This method has been applied to literature data to quantify the deuterium concentrations in a concentration line profile across an α-Zr/deuteride interface.

scholarly journals An atomic-scale view at the composition of amyloid-beta fibrils by atom probe tomography

2018 ◽  
Author(s):  
Kristiane A.K. Rusitzka ◽  
Leigh T. Stephenson ◽  
Agnieszka Szczepaniak ◽  
Lothar Gremer ◽  
Dierk Raabe ◽  
...  
Keyword(s):  
Amyloid Beta ◽  
Atom Probe Tomography ◽  
Senile Plaques ◽  
Three Dimensional ◽  
Atom Probe ◽  
Pulse Frequency ◽  
Atomic Scale ◽  
Chemical Information ◽  
Protein Fibrils ◽  
Spectrometry Technique

ABSTRACTAmyloid-beta (Aβ) proteins play an important role in a number of neurodegenerative diseases. Aβ is found in senile plaques in brains of Alzeimer’s disease patients. The 42 residues of the monomer form dimers which stack to fibrils gaining several micrometers in length. Using Aβ fibrils with 13C and 15N marker substitution, we developed an innovative approach to obtain insights to structural and chemical information of the protein. We deposited the modified protein fibrils to pre-sharped aluminium needles with >100-nm apex diameters and, using the position-sensitive mass-to-charge spectrometry technique of atom probe tomography, we acquired the chemically-resolved three dimensional information for every detected ion evaporated in small fragments from the protein. We also discuss the influence of experimental parameters such as pulse energy and pulse frequency of the used Laser beam which lead to differences in the size of the gained fragments, developing the capability of localising metal atom within Aβ plaques.

Extracting Chemical Information From Energy-Dispersive X-Ray Spectra by Multivariate Statistical Analysis (MSA)

1998 ◽  
Vol 4 (S2) ◽  
pp. 204-205
Author(s):  
M. Saunders ◽  
E.S.K. Menon ◽  
D.J. Chisholm ◽  
A.G. Fox
Keyword(s):  
Statistical Analysis ◽  
Elemental Composition ◽  
Multivariate Statistical Analysis ◽  
Single Phase ◽  
Principal Component ◽  
Chemical Information ◽  
Direct Identification ◽  
Multivariate Statistical ◽  
Eds Analysis ◽  
The Individual

The introduction of Multivariate Statistical Analysis techniques such as Principal Component Analysis (PCA) to the study of EDS and EELS spectra has opened up new possibilities for processing spectral data. In conventional EDS analysis each spectrum provides information about the elemental composition of the sample at a specific probe position. Where the signal arises from a single phase this elemental composition corresponds to the chemical composition of the phase. However, where the probe is incident on a multiphase region of the sample it is impossible to make a direct identification of the different chemical phases contributing to the overall spectrum.With PCA it is no longer necessary to consider the spectra as isolated pieces of information. It is now possible to analyze a series of spectra as a single entity, looking for correlations between the variations in the elemental signals present in the individual spectra.

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