scholarly journals Evaluation of EELS spectrum imaging data by spectral components and factors from multivariate analysis

Microscopy ◽  
2017 ◽  
Vol 67 (suppl_1) ◽  
pp. i133-i141 ◽  
Author(s):  
Siyuan Zhang ◽  
Christina Scheu
Keyword(s):  
Multivariate Analysis ◽  
Imaging Data ◽  
Sn Doping ◽  
Si Substrates ◽  
Spectral Components ◽  
Quantitative Analyses ◽  
Spectrum Imaging ◽  
Doping Profiles ◽  
Electron Energy Loss ◽  
Background Models

Abstract Multivariate analysis is a powerful tool to process spectrum imaging datasets of electron energy loss spectroscopy. Most spatial variance of the datasets can be explained by a limited numbers of components. We explore such dimension reduction to facilitate quantitative analyses of spectrum imaging data, supervising the spectral components instead of spectra at individual pixels. In this study, we use non-negative matrix factorization to decompose datasets from Fe2O3 thin films with different Sn doping profiles on SnO2 and Si substrates. Case studies are presented to analyse spectral features including background models, signal integrals, peak positions and widths. Matlab codes are written to guide microscopists to perform these data analyses.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Electron Energy Analysis of Germanium and Silica Layers on Silicon Substrates

1975 ◽  
Vol 33 ◽  
pp. 96-97
Author(s):  
R. W. Ditchfield ◽  
A. G. Cullis
Keyword(s):  
Epitaxial Growth ◽  
Electron Energy ◽  
Silicon Substrates ◽  
Secondary Phases ◽  
Si Substrates ◽  
Transmission Electron ◽  
Layer Structures ◽  
Si Films ◽  
Electron Energy Loss ◽  
Growth Centres

An energy analyzing transmission electron microscope of the Möllenstedt type was used to measure the electron energy loss spectra given by various layer structures to a spatial resolution of 100Å. The technique is an important, method of microanalysis and has been used to identify secondary phases in alloys and impurity particles incorporated into epitaxial Si films.Layers Formed by the Epitaxial Growth of Ge on Si Substrates Following studies of the epitaxial growth of Ge on (111) Si substrates by vacuum evaporation, it was important to investigate the possible mixing of these two elements in the grown layers. These layers consisted of separate growth centres which were often triangular and oriented in the same sense, as shown in Fig. 1.

scholarly journals Relative Frequency and Risk Factors of COVID-19 Related Headache in a Sample of Egyptian Population: A Hospital-Based Study

Pain Medicine ◽  
2021 ◽  
Author(s):  
Mona Hussein ◽  
Wael Fathy ◽  
Ragaey A Eid ◽  
Hoda M Abdel-Hamid ◽  
Ahmed Yehia ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Platelet Count ◽  
Relative Frequency ◽  
Clinical Laboratory ◽  
Primary Headache ◽  
Female Gender ◽  
Imaging Data ◽  
Laboratory Findings ◽  
Neutrophil Lymphocyte Ratio ◽  
The Impact

Abstract Objectives Headache is considered one of the most frequent neurological manifestations of coronavirus disease 2019 (COVID-19). This work aimed to identify the relative frequency of COVID-19-related headache and to clarify the impact of clinical, laboratory findings of COVID-19 infection on headache occurrence and its response to analgesics. Design Cross-sectional study. Setting Recovered COVID-19 patients. Subjects In total, 782 patients with a confirmed diagnosis of COVID-19 infection. Methods Clinical, laboratory, and imaging data were obtained from the hospital medical records. Regarding patients who developed COVID-19 related headache, a trained neurologist performed an analysis of headache and its response to analgesics. Results The relative frequency of COVID-19 related headache among our sample was 55.1% with 95% confidence interval (CI) (.516–.586) for the estimated population prevalence. Female gender, malignancy, primary headache, fever, dehydration, lower levels of hemoglobin and platelets and higher levels of neutrophil/lymphocyte ratio (NLR) and CRP were significantly associated with COVID-19 related headache. Multivariate analysis revealed that female gender, fever, dehydration, primary headache, high NLR, and decreased platelet count were independent predictors of headache occurrence. By evaluating headache response to analgesics, old age, diabetes, hypertension, primary headache, severe COVID-19, steroid intake, higher CRP and ferritin and lower hemoglobin levels were associated with poor response to analgesics. Multivariate analysis revealed that primary headache, steroids intake, moderate and severe COVID-19 were independent predictors of non-response to analgesics. Discussion Headache occurs in 55.1% of patients with COVID-19. Female gender, fever, dehydration, primary headache, high NLR, and decreased platelet count are considered independent predictors of COVID-19 related headache.

Mapping the Subcellular Distribution of Calcium in Depolarized Neurons by Electron Energy Loss Spectrum Imaging

2000 ◽  
Vol 6 (S2) ◽  
pp. 162-163
Author(s):  
S.B. Andrews ◽  
J. Hongpaisan ◽  
N.B. Pivovarova ◽  
D.D. Friel ◽  
R.D. Leapman
Keyword(s):  
Electron Microscope ◽  
Energy Loss ◽  
Electron Energy ◽  
Transmission Electron Microscope ◽  
Detection Threshold ◽  
Electron Energy Loss Spectrum ◽  
Acquisition Time ◽  
Transmission Electron ◽  
Spectrum Imaging ◽  
Electron Energy Loss

In the context of biological specimens, it is in principle desirable to quantitatively map, rather than just point analyze, the distribution of physiologically important elements, and to do so at subcellular resolution. Presently, this can be accomplished by electron energy loss spectrum-imaging (EELSI) in both the scanning transmission electron microscope (STEM) and the energy-filtering transmission electron microscope (EFTEM). Until recently, this approach has been of limited value for mapping the particularly important element Ca, mainly because intracellular total Ca concentrations are normally quite low (<5 mmol/kg dry weight) and because the background in the vicinity of the Ca L23 edge is complex and requires precise background modeling to extract the very weak Ca signals. As a result, the Ca signal is usually not high enough to reach detection threshold during a practical EELSI acquisition time.

scholarly journals The Open-Source Cornell Spectrum Imager

2012 ◽  
Vol 21 (1) ◽  
pp. 40-44 ◽  
Author(s):  
Robert Hovden ◽  
Paul Cueva ◽  
Julia A. Mundy ◽  
David A. Muller
Keyword(s):  
Data Analysis ◽  
Energy Loss ◽  
Hyperspectral Imaging ◽  
Spectrum Analysis ◽  
Analysis Tool ◽  
User Training ◽  
Spectrum Imaging ◽  
Data Analysis Tool ◽  
Simple Interface ◽  
Electron Energy Loss

Hyperspectral imaging (also known as spectrum imaging) requires software for extracting the signatures present in every spectrum. However, commercial software available for spectrum analysis remains expensive, complicated, and often not transparent regarding the internal workings and approximations made. For user facilities, educational institutes, and other settings where multiple users on a single tool can be expected, the limited availability of software becomes the bottleneck to data analysis, user training, and throughput. The Cornell Spectrum Imager (CSI) was developed as a universal data analysis tool to be freely distributed, to run on all computers, and to minimize training. This is accomplished by using one simple interface for imaging, cathodoluminescence, Raman, Electron Energy Loss Spectroscopic (EELS), and EDX data analysis. This article demonstrates the CSI plugins for ImageJ by guiding you through the basic workflow for processing EELS maps.

Bonding in Ion-Implanted Carbon Films Characterized by TEM Spectrum Lines and Energy-Filtered Imaging

1999 ◽  
Vol 589 ◽  
Author(s):  
J. Bentley ◽  
K.C. Walter ◽  
N.D. Evans
Keyword(s):  
Energy Shift ◽  
Carbon Films ◽  
Binding Energies ◽  
Diamond Like Carbon ◽  
Nitrogen Implantation ◽  
Electron Energy Loss Spectrometry ◽  
Spectrum Imaging ◽  
Binding Energy Shift ◽  
Electron Energy Loss ◽  
Ion Implanted

AbstractIon-implanted diamond-like carbon (DLC) films have been characterized by techniques based on electron energy-loss spectrometry using an imaging energy filter on a 300kV TEM. Nitrogen implantation results in increased sp2 bonding and a 1.3 eV shift to higher binding energies for carbon-K. Argon implantation results in a smaller increase in sp2bonding with no detectable binding energy shift. The fraction of implanted species retained is much smaller for Ar than for N. Differences in behavior between N- and Ar-implanted DLC are consistent with expected chemical reactions. Preliminary results demonstrate the feasibility of mapping the Φ*/σ* intensity (sp2/sp3) ratio by energy-filtered TEM as an alternative to spectrum imaging in STEM mode

Optimization of Acquisition Parameters for Atomic-Column Electron Energy-Loss Spectrum Imaging in a JEM-2200FS Aberration-Corrected Scanning Transmission Electron Microscope

2008 ◽  
Vol 14 (S2) ◽  
pp. 1400-1401 ◽  
Author(s):  
M Watanabe ◽  
M Kanno ◽  
D Ackland ◽  
CJ Kiely ◽  
DB Williams
Keyword(s):  
Electron Microscope ◽  
New Mexico ◽  
Energy Loss ◽  
Scanning Transmission Electron Microscope ◽  
Atomic Column ◽  
Transmission Electron ◽  
Scanning Transmission ◽  
Spectrum Imaging ◽  
Electron Energy Loss ◽  
Aberration Corrected

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

scholarly journals Physicochemical parameters as indicators of the authenticity of monofloral honey from the territory of the Republic of Serbia

2021 ◽  
Vol 40 (1) ◽  
pp. 49
Author(s):  
Saša Milosavljević ◽  
Milka Jadranin ◽  
Mića Mladenović ◽  
Vele Tešević ◽  
Nebojša Menković ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Phenolic Compounds ◽  
Physicochemical Parameters ◽  
Numerical Data ◽  
Physicochemical Analysis ◽  
Quality Parameters ◽  
Acacia Honey ◽  
Quantitative Analyses ◽  
Basic Quality ◽  
The Republic

The aim of this research was to verify the authenticity of monofloral honeys from the territory of the Republic of Serbia on the basis of physicochemical parameters routinely measured in honey quality control using multivariate analysis. Seventeen samples of monofloral honey (11 samples of acacia honey and 6 samples of sunflower honey) from the territory of the Republic of Serbia were analyzed. Physicochemical analysis of the samples included the examination of basic quality parameters and qualitative and quantitative analyses of phenolic compounds. In the samples tested, a total of 93 phenolic compounds were tentatively identified, and 19 of them were quantified. The obtained physicochemical analysis of the data served as input for the multivariate analysis. The hеаt map, which is useful for visualizing numerical data, was used for this purpose. The obtained results showed that the applied data can serve to clearly separate acacia and sunflower honeys.

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