Multivariate statistical analysis of a series of alchemi spectra

1996 ◽  
Vol 54 ◽  
pp. 550-551 ◽  
Author(s):  
Ian M. Anderson ◽  
J. Bentley
Keyword(s):  
Statistical Analysis ◽  
Grain Boundary ◽  
Multivariate Statistical Analysis ◽  
Spectral Component ◽  
Boundary Segregation ◽  
Grain Boundary Segregation ◽  
Multivariate Statistical ◽  
X Ray ◽  
The Matrix ◽  
Electron Energy Loss

Multivariate statistical analysis (MSA) of a series of spectra or images offers an objective and quantitative way to characterize the features of the spectra that vary in a correlated fashion and to determine the number of independently varying components in the series. For example, in a series of spectra showing grain boundary segregation, there may be only one independently varying spectral component, which signifies an increase in the concentrations of the segregants and a corresponding decrease in the concentrations of some of the matrix constituents. The basis of the MSA method has been outlined by Trebbia and Bonnet, with application to the analysis of electron energy-loss spectrum images. Titchmarsh et al., have applied this analysis to a series of energy dispersive X-ray (EDX) spectra for the study of grain boundary segregation. The present paper illustrates the application of MSA methods to a series of EDX spectra acquired for ALCHEMI analysis. The basic method has been modified slightly for the analysis of ALCHEMI data.

scholarly journals Chemical Species, Micromorphology, and XRD Fingerprint Analysis of Tibetan MedicineZuotaiContaining Mercury

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Cen Li ◽  
Hongxia Yang ◽  
Yuzhi Du ◽  
Yuancan Xiao ◽  
Zhandui ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Chemical Species ◽  
Physicochemical Characteristics ◽  
Chemical Substance ◽  
Tibetan Medicine ◽  
Fingerprint Analysis ◽  
Multivariate Statistical ◽  
Positive Role ◽  
X Ray

Zuotai(gTso thal) is one of the famous drugs containing mercury in Tibetan medicine. However, little is known about the chemical substance basis of its pharmacodynamics and the intrinsic link of different samples sources so far. Given this, energy dispersive spectrometry of X-ray (EDX), scanning electron microscopy (SEM), atomic force microscopy (AFM), and powder X-ray diffraction (XRD) were used to assay the elements, micromorphology, and phase composition of nineZuotaisamples from different regions, respectively; the XRD fingerprint features ofZuotaiwere analyzed by multivariate statistical analysis. EDX result shows thatZuotaicontains Hg, S, O, Fe, Al, Cu, and other elements. SEM and AFM observations suggest thatZuotaiis a kind of ancient nanodrug. Its particles are mainly in the range of 100–800 nm, which commonly further aggregate into 1–30 μm loosely amorphous particles. XRD test shows thatβ-HgS, S8, andα-HgS are its main phase compositions. XRD fingerprint analysis indicates that the similarity degrees of nine samples are very high, and the results of multivariate statistical analysis are broadly consistent with sample sources. The present research has revealed the physicochemical characteristics ofZuotai, and it would play a positive role in interpreting this mysterious Tibetan drug.

Quantification Of Segregation Levels Using Xeds In The Stem

1999 ◽  
Vol 5 (S2) ◽  
pp. 146-147
Author(s):  
V. J. Keast ◽  
D. B. Williams
Keyword(s):  
Grain Boundary ◽  
Boundary Segregation ◽  
Scanning Transmission Electron Microscope ◽  
Factor V ◽  
X Ray ◽  
Interaction Volume ◽  
Probe Size ◽  
Scanning Transmission ◽  
The Matrix ◽  
Image Position

The quantification of grain boundary segregation levels, as measured with X-ray energy dispersive spectroscopy (XEDS) in a scanning transmission electron microscope (STEM), is dependent on the size and shape of the interaction volume. The segregation level T (in atoms/nm2) is related to the intensities of the characteristic peaks in the X-ray spectrum, Is and Im, bywhere ρ is the density of the matrix in atoms/nm3, Am and As are the atomic masses of the matrix and segregant respectively and ksm is the usual k-factor. The geometric factor, V/A, is the ratio of the volume of interaction to the area of the grain boundary inside in the interaction volume. Different models have been used to describe the interaction volume and these are illustrated in Fig. 1 and the appropriate expression for V/A is given in each case. In the simplest case, beam broadening is neglected and the interaction volume can be described as a cylinder with diameter equal to the probe size, d.

scholarly journals Multivariate Statistical Analysis of Spectrum Lines and Images

1997 ◽  
Vol 3 (S2) ◽  
pp. 931-932 ◽  
Author(s):  
Ian M. Anderson ◽  
Jim Bentley
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quantitative Imaging ◽  
Boundary Segregation ◽  
Large Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Recent Developments ◽  
Data Files

Recent developments in instrumentation and computing power have greatly improved the potential for quantitative imaging and analysis. For example, products are now commercially available that allow the practical acquisition of spectrum images, where an EELS or EDS spectrum can be acquired from a sequence of positions on the specimen. However, such data files typically contain megabytes of information and may be difficult to manipulate and analyze conveniently or systematically. A number of techniques are being explored for the purpose of analyzing these large data sets. Multivariate statistical analysis (MSA) provides a method for analyzing the raw data set as a whole. The basis of the MSA method has been outlined by Trebbia and Bonnet.MSA has a number of strengths relative to other methods of analysis. First, it is broadly applicable to any series of spectra or images. Applications include characterization of grain boundary segregation (position-), of channeling-enhanced microanalysis (orientation-), or of beam damage (time-variation of spectra).

scholarly journals Multivariate Statistical Analysis of Particle X-ray Spectra

1998 ◽  
Vol 4 (S2) ◽  
pp. 202-203
Author(s):  
Ian M. Anderson ◽  
John A. Small
Keyword(s):  
Statistical Analysis ◽  
Trace Element ◽  
Multivariate Statistical Analysis ◽  
A Priori ◽  
Detectable Level ◽  
Multivariate Statistical ◽  
Raw Data ◽  
X Ray ◽  
Carbon Substrates ◽  
Particle Spectra

Multivariate statistical analysis (MSA) is a powerful tool for the analysis of series of spectra. This paper explores an application of MSA to a series of energy dispersive X-ray (EDX) spectra acquired in the scanning electron microscope (SEM) from a series of particles. The raw data were series of spectra previously acquired to test analytical procedures for trace element detection. This paper explores the possibility of performing the trace element detection with MSA components that have been extracted from the raw data without any a priori assumptions about the information content of the particle spectra. Particles were prepared from two analytical glasses, dispersed onto carbon substrates and coated with carbon. The compositions of the two glasses are substantially similar, except that one glass (K-3106) contains 0.7 wt.% Fe, whereas the other glass (K-3069) does not contain Fe at a detectable level.

Measuring grain boundary segregation using Wavelength Dispersive X-ray Spectroscopy: Further developments

2011 ◽  
Vol 605 (7-8) ◽  
pp. 848-858 ◽  
Author(s):  
P. Nowakowski ◽  
F. Christien ◽  
M. Allart ◽  
Y. Borjon-Piron ◽  
R. Le Gall
Keyword(s):  
Grain Boundary ◽  
Boundary Segregation ◽  
Grain Boundary Segregation ◽  
X Ray

Application of Multivariate Statistical Analysis to STEM X-ray Spectral Images: Interfacial Analysis in Microelectronics

2006 ◽  
Vol 12 (6) ◽  
pp. 538-544 ◽  
Author(s):  
Paul G. Kotula ◽  
Michael R. Keenan
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Multivariate Curve Resolution ◽  
Foil Thickness ◽  
Beam Specimen ◽  
Spectral Image ◽  
Multivariate Statistical ◽  
Data Set ◽  
Thickness Change ◽  
X Ray

Multivariate statistical analysis methods have been applied to scanning transmission electron microscopy (STEM) energy-dispersive X-ray spectral images. The particular application of the multivariate curve resolution (MCR) technique provides a high spectral contrast view of the raw spectral image. The power of this approach is demonstrated with a microelectronics failure analysis. Specifically, an unexpected component describing a chemical contaminant was found, as well as a component consistent with a foil thickness change associated with the focused ion beam specimen preparation process. The MCR solution is compared with a conventional analysis of the same spectral image data set.

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