Application-driven computerisation of the energy filtering Electron Microscope for imaging and analysis

1989 ◽  
Vol 47 ◽  
pp. 58-59
Author(s):  
R. Bauer ◽  
W. Probst ◽  
G. Lamprecht
Keyword(s):  
Electron Microscope ◽  
Chemical Composition ◽  
Energy Loss ◽  
Statistical Information ◽  
Chemical Information ◽  
Elemental Distribution ◽  
Energy Filtering ◽  
Physical And Chemical ◽  
Electron Energy Loss ◽  
Electron Energy Loss Spectra

An Energy Filtering Electron Microscope (EFEM) with an integrated imaging energy loss spectrometer offers many different methodes for imaging and analysis. Thus there is a wealth of different information from a specimen. They are all related to the structure as well as to the physical and chemical composition of a specimen. There are e.g. geometrical and statistical information from brightfield and darkfield images, information about crystalline structures from diffraction images, chemical information from electron energy loss spectra and highly resolved elemental distribution images. Most of this information is detail information not final results. To get final results, detail information have to be related and processed. To do this reliably and economically it is important to have a powerful and flexible computer system permanently available on the microscope to record, enhance, process, evalute, analyse, and store images, spectra and data. An important point is the use of the computer capabilities to perform automatic control of the microscope.

Electron spectroscopic imaging and analysis of electron energy loss spectra with an energy filtering Electron Microscope

1989 ◽  
Vol 47 ◽  
pp. 404-405
Author(s):  
John J. Godleski ◽  
Rebecca C. Stearns ◽  
Emil J. Millet
Keyword(s):  
Electron Microscope ◽  
Energy Loss ◽  
Electron Energy ◽  
Scintillation Detector ◽  
Oxide Particle ◽  
Leading Edge ◽  
Hard Disk ◽  
Energy Filtering ◽  
Electron Energy Loss ◽  
Electron Energy Loss Spectra

The Zeiss CEM902, energy filtering electron microscope, can be used to image the structure of unstained 30 nm sections of biologic materials, to image the distribution of selected elements in such sections, and to determine electron energy loss spectra (EELS) of elements in areas as small as 10 nm. Although the integrated computer in the latest version of the CEM902 can collect and display signals from the scintillation detector for recording EELS, our instrument did not have this capability. Therefore, we have added a Leading Edge Model D personal computer with a 20 Mbyte hard disk, Hercules compatible graphics display adapter, and a programmable gain analog to digital converter board (Metrabyte DAS16-G1) to collect and analyze voltage signals corresponding to changes in accelerating voltage and changes in the signal from the photomultiplier tube (PMT) of the scintillation detector. With this board, the gain on the PMT channel is dynamically adjusted for optimal resolution. Software is designed to monitor and display voltages, store data on the hard disk, display spectra with adjustable axes, as well as subtract spectra and determine areas beneath regions of interest.Canine alveolar macrophages with ingested cobalt oxide particles were fixed with 2.5% glutaraldehyde in 0.164M phosphate buffer, post-fixed in 1% OsO4 in 0.lM Na cacodylate buffer, dehydrated through alcohols, embedded in araldite, and sectioned at 30nm. Sections were assessed with our CEM902 as described above. The spectral range of 500 to 900 electron volts while focused on acobalt oxide particle at 20,000x is illustrated in Figure 1 .

Analysis of Diffraction Contrast as A Function of Energy Loss in Energy Filtering Transmission Electron Microscope (EFTEM) Imaging and Possible Implications on High-Resolution Compositional Mapping

1999 ◽  
Vol 5 (S2) ◽  
pp. 620-621
Author(s):  
K.T. Moore ◽  
J.M. Howe
Keyword(s):  
Electron Microscope ◽  
Energy Loss ◽  
Electron Energy ◽  
Transmission Electron Microscope ◽  
Diffraction Contrast ◽  
Energy Filtering ◽  
Background Removal ◽  
Transmission Electron ◽  
Important Relationship ◽  
Electron Energy Loss

The dependence of diffraction contrast on electron energy loss is an important relationship that needs to be understood because of its potential effect on energy-filtering transmission electron microscope (EFTEM) images. Often when either a two-window jump-ratio image or a three-window elemental map is produced diffraction contrast is not totally eliminated and contributes to the intensity of the final EFTEM image. Background removal procedures often are unable to completely account for intensity changes due to dynamical effects (i.e., elastic scattering) that occur between images acquired at different energy losses, leaving artifacts in the final EFTEM image.In this study, the relationship between diffraction contrast and electron energy loss was investigated by obtaining EFTEM images of a bend contour in aluminum in 100 eV increments from 0 to 1000 eV (Fig. 1). EFTEM images were acquired a JOEL 2010F FEG TEM with a Gatan imaging filter (GIF) at a microscope magnification of 8 kX using a 1 eV/pixel dispersion, 2X binning (512 x 512) and exposure times ranging from 0.25 s for 0 eV energy loss up to 132 sec for 1000 eV energy loss.

Inner Shell Ionization by 60 keV Electrons, Application to Microanalysis

1975 ◽  
Vol 33 ◽  
pp. 126-127
Author(s):  
Y. Kihn ◽  
J. Sevely ◽  
B. Jouffrey
Keyword(s):  
Electron Microscope ◽  
Energy Loss ◽  
Electron Energy ◽  
Selection Rules ◽  
First Approximation ◽  
Inner Shell Ionization ◽  
Electron Energy Loss ◽  
Electron Energy Loss Spectra ◽  
Shell Ionization

By using a Castaing-Henry filtering device adapted on a Siemens Elmiskop I electron microscope, we have directly observed plasmon and inner shell excitations by 60 keV electrons on electron energy loss spectra. Inner shell excitation edges have been detected up to 1900 eV.By comparing L and K inner shell excitation profiles in the case of Magnesium, Aluminium and Silicon, it is concluded that the optical selection rules, △1 = ±1, explain the shape of the spectrum after the edge in a first approximation.

Eels of Gases in the Electron Microscope

1982 ◽  
Vol 40 ◽  
pp. 650-651
Author(s):  
R.P. Burgner ◽  
O.L. Krivanek ◽  
P.R. Swann
Keyword(s):  
Electron Microscope ◽  
Chemical Composition ◽  
Energy Loss ◽  
Transmission Electron Microscope ◽  
Electron Energy Loss Spectroscopy ◽  
Gas Phases ◽  
Transmission Electron ◽  
Gas Tight ◽  
Electron Energy Loss

Interest in the role of microstructure in chemical reactions (particularly in catalysis) has stimulated the development of environmental cells (E-cells) for the transmission electron microscope. A knowledge of the local changes in chemical composition that occur during these reactions is useful but difficult to obtain by conventional energy-dispersive Xray techniques because of the physical requirements for a gas-tight cell. By contrast, electron energy loss spectroscopy (EELS) presents no additional instrumentation difficulties and may be used to monitor low and high-Z chemical changes in both solid and gas phases throughout the chemical reaction.

Data Acquisition and Handling Unit for a Quantitative Use of Electron Energy Loss Spectroscopy

1977 ◽  
Vol 35 ◽  
pp. 232-233 ◽  
Author(s):  
P. Trebbia ◽  
P. Ballongue ◽  
C. Colliex
Keyword(s):  
Electron Microscope ◽  
Energy Loss ◽  
Electron Energy ◽  
Electron Energy Loss Spectroscopy ◽  
Single Channel ◽  
Detection System ◽  
Surface Barrier ◽  
Solid State Detector ◽  
Electrostatic Mirror ◽  
Electron Energy Loss

An effective use of electron energy loss spectroscopy for chemical characterization of selected areas in the electron microscope can only be achieved with the development of quantitative measurements capabilities.The experimental assembly, which is sketched in Fig.l, has therefore been carried out. It comprises four main elements.The analytical transmission electron microscope is a conventional microscope fitted with a Castaing and Henry dispersive unit (magnetic prism and electrostatic mirror). Recent modifications include the improvement of the vacuum in the specimen chamber (below 10-6 torr) and the adaptation of a new electrostatic mirror.The detection system, similar to the one described by Hermann et al (1), is located in a separate chamber below the fluorescent screen which visualizes the energy loss spectrum. Variable apertures select the electrons, which have lost an energy AE within an energy window smaller than 1 eV, in front of a surface barrier solid state detector RTC BPY 52 100 S.Q. The saw tooth signal delivered by a charge sensitive preamplifier (decay time of 5.10-5 S) is amplified, shaped into a gaussian profile through an active filter and counted by a single channel analyser.

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.

Background Fitting in the Low-Loss Region of Electron Energy Loss Spectra

1990 ◽  
Vol 48 (2) ◽  
pp. 56-57
Author(s):  
C P Scott ◽  
A J Craven ◽  
C J Gilmore ◽  
A W Bowen
Keyword(s):  
Energy Loss ◽  
Multiple Scattering ◽  
Simple Form ◽  
Single Scattering ◽  
Low Loss ◽  
Characteristic Energy ◽  
Normal Method ◽  
Fitting In ◽  
Electron Energy Loss ◽  
Electron Energy Loss Spectra

The normal method of background subtraction in quantitative EELS analysis involves fitting an expression of the form I=AE-r to an energy window preceding the edge of interest; E is energy loss, A and r are fitting parameters. The calculated fit is then extrapolated under the edge, allowing the required signal to be extracted. In the case where the characteristic energy loss is small (E < 100eV), the background does not approximate to this simple form. One cause of this is multiple scattering. Even if the effects of multiple scattering are removed by deconvolution, it is not clear that the background from the recovered single scattering distribution follows this simple form, and, in any case, deconvolution can introduce artefacts.The above difficulties are particularly severe in the case of Al-Li alloys, where the Li K edge at ~52eV overlaps the Al L2,3 edge at ~72eV, and sharp plasmon peaks occur at intervals of ~15eV in the low loss region. An alternative background fitting technique, based on the work of Zanchi et al, has been tested on spectra taken from pure Al films, with a view to extending the analysis to Al-Li alloys.

ELNES of Iron Compounds

1990 ◽  
Vol 48 (2) ◽  
pp. 28-29
Author(s):  
Hiroki Kurata ◽  
Kazuhiro Nagai ◽  
Seiji Isoda ◽  
Takashi Kobayashi
Keyword(s):  
Energy Loss ◽  
Metal Ions ◽  
Energy Resolution ◽  
Transition Metal Oxides ◽  
Local Symmetry ◽  
Iron Compounds ◽  
Fine Structures ◽  
Electron Energy Loss ◽  
Fe Ions ◽  
Electron Energy Loss Spectra

Electron energy loss spectra of transition metal oxides, which show various fine structures in inner shell edges, have been extensively studied. These structures and their positions are related to the oxidation state of metal ions. In this sence an influence of anions coordinated with the metal ions is very interesting. In the present work, we have investigated the energy loss near-edge structures (ELNES) of some iron compounds, i.e. oxides, chlorides, fluorides and potassium cyanides. In these compounds, Fe ions (Fe2+ or Fe3+) are octahedrally surrounded by six ligand anions and this means that the local symmetry around each iron is almost isotropic.EELS spectra were obtained using a JEM-2000FX with a Gatan Model-666 PEELS. The energy resolution was about leV which was mainly due to the energy spread of LaB6 -filament. The threshole energies of each edges were measured using a voltage scan module which was calibrated by setting the Ni L3 peak in NiO to an energy value of 853 eV.

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