Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Fourier-ratio deconvolution and its Bayesian equivalent.

R F Egerton1, F Wang, M Malac

  • 1Physics Department, University of Alberta, Edmonton, Canada. egerton@phys.ualberta.ca

Micron (Oxford, England : 1993)
|November 27, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

AI-ECG age predicts carotid atherosclerotic plaque volume and progression.

Atherosclerosis·2025
Same author

Disease evolution in systemic juvenile idiopathic arthritis: an international, observational cohort study through JIRcohort.

Pediatric rheumatology online journal·2023
Same author

Structural characterization of poly-Si Films crystallized by Ni Metal Induced Lateral Crystallization.

Scientific reports·2019
Same author

Fatigue at baseline is associated with geriatric impairments and represents an adverse prognostic factor in older patients with a hematological malignancy.

Annals of hematology·2018
Same author

Protective effect of sugars on storage stability of microwave freeze-dried and freeze-dried Lactobacillus paracasei F19.

Journal of applied microbiology·2018
Same author

Clinical and Molecular Phenotypes of Low-Penetrance Variants of NLRP3: Diagnostic and Therapeutic Challenges.

Arthritis & rheumatology (Hoboken, N.J.)·2017

Bayesian deconvolution enhances electron energy-loss spectroscopy by removing plural scattering and improving resolution. This method uses a low-loss spectrum as a kernel, simplifying data processing for core-loss analysis.

Area of Science:

  • Materials Science
  • Spectroscopy
  • Electron Microscopy

Background:

  • Electron energy-loss spectroscopy (EELS) is crucial for material analysis.
  • Plural scattering and limited energy resolution degrade EELS data quality.
  • Existing deconvolution methods like Fourier-ratio have limitations.

Purpose of the Study:

  • To develop and validate a Bayesian deconvolution technique for EELS.
  • To simultaneously address plural scattering and enhance energy resolution.
  • To simplify core-loss data processing in EELS.

Main Methods:

  • Bayesian deconvolution (maximum-entropy/maximum-likelihood) applied to EELS.
  • Utilizing a low-loss spectrum as the deconvolution kernel.
  • Comparing Bayesian deconvolution with Fourier-ratio deconvolution.

Related Experiment Videos

Main Results:

  • Bayesian deconvolution effectively removes plural scattering and improves energy resolution.
  • The method eliminates the need for pre-edge background subtraction for core-loss spectra.
  • Using the low-loss spectrum as both data and kernel provides an in-situ measure of energy resolution.

Conclusions:

  • Bayesian deconvolution offers a robust and simplified approach to EELS data processing.
  • This technique is advantageous for analyzing core-loss spectra and removing matrix effects.
  • The method enhances the accuracy and reliability of EELS analysis for materials characterization.