A simple parameterization scheme for inner-shell cross sections

1988 ◽  
Vol 46 ◽  
pp. 532-533
Author(s):  
R.F. Egerton
Keyword(s):  
Solid State ◽  
Energy Loss ◽  
Cross Section ◽  
Cross Sections ◽  
Elemental Analysis ◽  
Incident Energy ◽  
Core Loss ◽  
Dipole Oscillator ◽  
Electron Energy Loss ◽  
Hand Calculator

Quantitative elemental analysis by electron energy-loss spectroscopy requires values of core-loss cross section σ(β,Δ) integrated up to a scattering angle β and over an energy range Δ above the ionization threshold. Such cross sections can be calculated using atomic models [1-3], neglecting solid-state effects. They can also be determined experimentally [4,5], but only for particular values of β,Δ and incident energy E0. By representing σ(β,Δ) in terms of an integrated dipole oscillator strength f(Δ) which is independent of β and E0, we realize two advantages: (1) measurements on solids can be directly compared with one another and with theory, and (2) values of σ(β,Δ) for K, L and M edges can be derived from tabulated values of f(Δ) by use of a hand calculator or a very short computer program.

Phase Identification and Mapping Based on Valence Loss EELS and ELNES

2009 ◽  
Vol 17 (3) ◽  
pp. 16-19
Author(s):  
R.D. Twesten
Keyword(s):  
Electronic Structure ◽  
Energy Loss ◽  
Elemental Composition ◽  
Elemental Analysis ◽  
Core Loss ◽  
Core Shell ◽  
Phase Identification ◽  
X Ray ◽  
Response Information ◽  
Electron Energy Loss

Much of analytical TEM is based on elemental analysis of core-shell ionizations and their role in electron energy-loss spectroscopy (EELS) and energy-dispersive X-ray spectroscopy (EDS). In these techniques, integrals of the primary or secondary ionization signals (typically over many tens of eV in energy) are used to measure and map the elemental composition of probed sample areas.In contrast, present-day STEM EELS systems are able to reveal spectral details with resolution in the range 0.1-1.0 eV. This means that EELS provides access to electronic structure and response information that goes beyond the simple elemental composition information of the integrated core-loss signals.

Energy Loss Imaging in Biology

1982 ◽  
Vol 40 ◽  
pp. 416-419
Author(s):  
H. Shuman ◽  
A.V. Somlyo ◽  
A.P. Somlyo ◽  
T. Frey ◽  
D. Safer
Keyword(s):  
Electron Microscope ◽  
Energy Loss ◽  
Cross Section ◽  
Cross Sections ◽  
Count Rate ◽  
Electron Flux ◽  
Characteristic Energy ◽  
Spectrometer Slit ◽  
Electron Energy Loss ◽  
Sensitive Method

It has been recognized for sometime that electron energy loss spectroscopy (EELS) is potentially the most sensitive method of measuring elemental composition in the electron microscope. Magnetic sector spectrometers currently in use collect most of the inelastically scattered electrons, while the cross sections for ionization of the L2 3 levels of the biologically important elements are large. The energies of the theoretically predicted L2 3 absorption edge maxima and their corresponding differential cross section for lOmrad collection and 80keV incident electrons are shown in Table I. The characteristic energy loss electron count rate expected from one atom with lOeV spectrometer slit width and lOOA/cm2 (the maximum available from a tungsten hairpin) electron flux at the specimen, indicates that the minimum detectable mass sensitivity of EELS will be high. An experimentally determined count rate and cross section for the Fe M2, 3 edge was determined from the ferritin images shown in Fig. 1.

Determination of ionization cross section by electron spectroscopic imaging

1988 ◽  
Vol 46 ◽  
pp. 536-537
Author(s):  
G.T. Simon ◽  
Y.M. Heng ◽  
F.P. Ottensmeyer
Keyword(s):  
Energy Loss ◽  
Cross Section ◽  
Ionization Cross Section ◽  
Unit Area ◽  
Core Loss ◽  
Ionization Cross ◽  
Image Position ◽  
Electron Energy Loss ◽  
Ionization Edge

Electron energy loss spectroscopy (EELS) has become a significant technique for high resolution elemental microanalysis and mapping. Theoretically, quantitative analysis requires only one simple equation:Eqn.(1)where N is the number of atoms per unit area analysed; I(net)(a,δ) is the core loss intensity integrated over an energy range δ beyond the ionization edge and with a collection angle a; I(total) is the total intensity integrated beneath the whole spectrum; σ(a,δ) is the corresponding ionization cross section.To obtain I(net) according to current convention and some theoretical justification, the simplest way of removing the background beneath an ionization edge is simply fitting at least two pre-edge measurements to the equation of I=AE-r, where I is the intensity of electrons that have energy loss E; A and r are constants to be determined from the fitted pre-edge region. Several other methods have also been derived for better accuracy in specific applications.

A data base for energy loss cross sections and mean free paths

1992 ◽  
Vol 50 (2) ◽  
pp. 1264-1265 ◽  
Author(s):  
R.F. Egerton
Keyword(s):  
Oscillator Strength ◽  
Cross Section ◽  
Cross Sections ◽  
Incident Energy ◽  
Theoretical Models ◽  
Probable Error ◽  
Data Compilation ◽  
Order Of Magnitude ◽  
Dipole Oscillator ◽  
Opposite Extreme

For quantitative elemental analysis by standard EELS techniques, we require inner-shell cross sections integrated over an energy range Δ beyond the ionization edge. These cross sections are conveniently parameterized in terms of a dipole oscillator strength f(Δ) which is independent of incident energy Eo and collection semi-angle β.Values of f(Δ) are obtainable from EELS measurements, from theoretical models and from photoabsorption data. Compilation of this data allows one to obtain a best estimate of a given cross section, together with an estimate of the probable error. Consistency is good for K-shells, allowing f(Δ) to be predicted to within 5% accuracy. At the opposite extreme, discrepancies of an order of magnitude exist for the N- and O-shells (Fig.1). In general, there is more consistency with regard to edge shape, as expressed by the ratio ϕ(Δ)=f(Δ)/f(100); see Fig.2.If microanalysis is carried out by matching to stored edge profiles, the convenient quantity is the differential oscillator strength df/dE. Such data is available directly from atomic calculations and, for some edges, from experimental measurements; see Fig.3.

Signal/Noise Ratio and Elemental Detectivity by Eels

1982 ◽  
Vol 40 ◽  
pp. 488-489
Author(s):  
R. F. Egerton
Keyword(s):  
Energy Loss ◽  
Electron Energy ◽  
Elemental Analysis ◽  
Electron Energy Loss Spectroscopy ◽  
Emission Spectroscopy ◽  
X Ray ◽  
Signal Noise ◽  
Characteristic Signal ◽  
Noise Ratio ◽  
Electron Energy Loss

An important parameter governing the sensitivity and accuracy of elemental analysis by electron energy-loss spectroscopy (EELS) or by X-ray emission spectroscopy is the signal/noise ratio of the characteristic signal.

Oxygen K near edge structures studied with multiple scattering calculations

1988 ◽  
Vol 46 ◽  
pp. 504-505
Author(s):  
Xudong Weng ◽  
Peter Rez
Keyword(s):  
Cross Section ◽  
Cross Sections ◽  
Partial Cross Section ◽  
Energy Window ◽  
Edge Structure ◽  
Fine Structures ◽  
Hydrogenic Model ◽  
Electron Energy Loss ◽  
Quantitative Chemical

In electron energy loss spectroscopy, quantitative chemical microanalysis is performed by comparison of the intensity under a specific inner shell edge with the corresponding partial cross section. There are two commonly used models for calculations of atomic partial cross sections, the hydrogenic model and the Hartree-Slater model. Partial cross sections could also be measured from standards of known compositions. These partial cross sections are complicated by variations in the edge shapes, such as the near edge structure (ELNES) and extended fine structures (ELEXFS). The role of these solid state effects in the partial cross sections, and the transferability of the partial cross sections from material to material, has yet to be fully explored. In this work, we consider the oxygen K edge in several oxides as oxygen is present in many materials. Since the energy window of interest is in the range of 20-100 eV, we limit ourselves to the near edge structures.

Quantitative Electron Energy Loss Mapping

1985 ◽  
Vol 43 ◽  
pp. 404-405
Author(s):  
R.D. Leapman ◽  
C.R. Swyt
Keyword(s):  
Energy Loss ◽  
Electron Energy ◽  
Electron Energy Loss Spectroscopy ◽  
Elemental Concentration ◽  
Core Loss ◽  
Elemental Distribution ◽  
Electron Energy Loss

The intensity of a characteristic electron energy loss spectroscopy (EELS) image does not, in general, directly reflect the elemental concentration. In fact, the raw core loss image can give a misleading impression of the elemental distribution. This is because the measured core edge signal depends on the amount of plural scattering which can vary significantly from region to region in a sample. Here, we show how the method for quantifying spectra due to Egerton et al. can be extended to maps.

Electron energy-loss near-edge structure in silicate minerals

1992 ◽  
Vol 50 (2) ◽  
pp. 1258-1259
Author(s):  
D W McComb ◽  
R S Payne ◽  
P L Hansen ◽  
R Brydson
Keyword(s):  
Solid State ◽  
Energy Loss ◽  
Electron Energy ◽  
State Energy ◽  
Local Environment ◽  
Edge Structure ◽  
Silicate Minerals ◽  
Two Samples ◽  
Electron Energy Loss ◽  
Careful Processing

Electron energy-loss near-edge structure (ELNES) is an effective probe of the local geometrical and electronic environment around particular atomic species in the solid state. Energy-loss spectra from several silicate minerals were mostly acquired using a VG HB501 STEM fitted with a parallel detector. Typically a collection angle of ≈8mrad was used, and an energy resolution of ≈0.5eV was achieved.Other authors have indicated that the ELNES of the Si L2,3-edge in α-quartz is dominated by the local environment of the silicon atom i.e. the SiO4 tetrahedron. On this basis, and from results on other minerals, the concept of a coordination fingerprint for certain atoms in minerals has been proposed. The concept is useful in some cases, illustrated here using results from a study of the Al2SiO5 polymorphs (Fig.l). The Al L2,3-edge of kyanite, which contains only 6-coordinate Al, is easily distinguished from andalusite (5- & 6-coordinate Al) and sillimanite (4- & 6-coordinate Al). At the Al K-edge even the latter two samples exhibit differences; with careful processing, the fingerprint for 4-, 5- and 6-coordinate aluminium may be obtained.

A Poor Man’s Approach to Semi-Quantitative Analysis with Electron Energy Loss Spectroscopy

1980 ◽  
Vol 38 ◽  
pp. 110-111
Author(s):  
M. Isaacson
Keyword(s):  
Quantitative Analysis ◽  
Energy Loss ◽  
Electron Energy ◽  
Cross Section ◽  
Electron Energy Loss Spectroscopy ◽  
Excitation Cross Section ◽  
Incident Electron ◽  
Integral Cross Section ◽  
Image Position ◽  
Electron Energy Loss

In an earlier paper1 it was found that to a good approximation, the efficiency of collection of electrons that had lost energy due to an inner shell excitation could be written as where σE was the total excitation cross-section and σE(θ, Δ) was the integral cross-section for scattering within an angle θ and with an energy loss up to an energy Δ from the excitation edge, EE. We then obtained: where , with P being the momentum of the incident electron of velocity v. The parameter r was due to the assumption that d2σ/dEdΩ∞E−r for energy loss E. In reference 1 it was assumed that r was a constant.

In Situ Analysis of Surface Contaminant Desorption during Low-Temperature Silicon Substrate Cleaning using Reflection Electron Energy Loss Spectrometry

1992 ◽  
Vol 259 ◽  
Author(s):  
Selmer S. Wong ◽  
Shouleh Nikzad ◽  
Channing C. Ahn ◽  
Aimee L. Smith ◽  
Harry A. Atwater
Keyword(s):  
Low Temperature ◽  
Energy Loss ◽  
Electron Energy ◽  
Core Loss ◽  
Temperature Dependent ◽  
Electron Energy Loss Spectrometry ◽  
Analysis Technique ◽  
Chemical Analysis Technique ◽  
Electron Energy Loss

ABSTRACTWe have employed reflection electron energy loss spectrometry (REELS), a surface chemical analysis technique, in order to analyze contaminant coverages at the submonolayer level during low-temperature in situ cleaning of hydrogen-terminated Si(100). The chemical composition of the surface was analyzed by measurements of the C K, O K and Si L2,3 core loss intensities at various stages of the cleaning. These results were quantified using SiC(100) and SiO2 as reference standards for C and O coverage. Room temperature REELS core loss intensity analysis after sample insertion reveals carbon at fractional monolayer coverage. We have established the REELS detection limit for carbon coverage to be 5±2% of a monolayer. A study of temperature-dependent hydrocarbon desorption from hydrogen-terminated Si(100) reveals the absence of carbon on the surface at temperatures greater than 200°C. This indicates the feasibility of epitaxial growth following an in situ low-temperature cleaning and also indicates the power of REELS as an in situ technique for assessment of surface cleanliness.

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