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Measurement of Total Calcium in Neurons by Electron Probe X-ray Microanalysis
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Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing.

Ala AlAfeef1, Joanna Bobynko2, W Paul Cockshott3

  • 1SUPA School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UK; School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK.

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Summary
This summary is machine-generated.

Dual Energy Electron Energy Loss Spectroscopy (DualEELS) with deconvolution and compressed sensing (DLET) improves elemental mapping in 3D electron tomography. This method enhances accuracy by reducing artifacts and improving 3D morphology reconstruction.

Keywords:
Compressed sensingDeconvoluted EELS dataDualEELS tomographyElectron tomographyHigh manganese steelVanadium carbide precipitates

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Electron Microscopy

Background:

  • Elemental sensitivity in electron tomography is crucial for materials characterization.
  • Multiple scattering and reconstruction artifacts can degrade the accuracy of 3D elemental maps.
  • Dual Energy Electron Energy Loss Spectroscopy (DualEELS) offers potential for elemental sensitivity.

Purpose of the Study:

  • To investigate the efficacy of DualEELS for elementally sensitive tilt series tomography.
  • To evaluate the impact of deconvolution and advanced reconstruction algorithms on tomographic accuracy.
  • To compare the performance of a compressed sensing algorithm (DLET) with a traditional one (SIRT).

Main Methods:

  • Implementation of a deconvolution procedure to correct for multiple scattering effects in DualEELS data.
  • Normalization of the EELS signal by zero-loss peak intensity for linear density dependence.
  • Comparison of 3D reconstruction using the DLET compressed sensing algorithm versus the SIRT algorithm.

Main Results:

  • The linear signal obtained with deconvolution resulted in uniform precipitate density.
  • Omitting deconvolution led to a cupping artifact, showing reduced density in precipitate cores.
  • The DLET algorithm significantly improved 3D morphology reconstruction, minimizing missing wedge elongation.

Conclusions:

  • Reliable elementally sensitive tilt tomography using EELS necessitates the use of DualEELS.
  • Appropriate deconvolution is essential to mitigate artifacts and ensure signal linearity.
  • Compressed sensing reconstruction algorithms like DLET are vital for optimal 3D morphology reproduction from limited EELS data.